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Guendelman S, Kaltwasser L, Bayer M, Gallese V, Dziobek I. Brain mechanisms underlying the modulation of heart rate variability when accepting and reappraising emotions. Sci Rep 2024; 14:18756. [PMID: 39138266 PMCID: PMC11322180 DOI: 10.1038/s41598-024-68352-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 07/23/2024] [Indexed: 08/15/2024] Open
Abstract
Heart rate variability (HRV) has been linked to resilience and emotion regulation (ER). How HRV and brain processing interact during ER, however, has remained elusive. Sixty-two subjects completed the acquisition of resting HRV and task HRV while performing an ER functional Magnetic Resonance Imaging (fMRI) paradigm, which included the differential strategies of ER reappraisal and acceptance in the context of viewing aversive pictures. We found high correlations of resting and task HRV across all emotion regulation strategies. Furthermore, individuals with high levels of resting, but not task, HRV showed numerically lower distress during ER with acceptance. Whole-brain fMRI parametrical modulation analyses revealed that higher task HRV covaried with dorso-medial prefrontal activation for reappraisal, and dorso-medial prefrontal, anterior cingulate and temporo-parietal junction activation for acceptance. Subjects with high resting HRV, compared to subjects with low resting HRV, showed higher activation in the pre-supplementary motor area during ER using a region of interest approach. This study demonstrates that while resting and task HRV exhibit a positive correlation, resting HRV seems to be a better predictor of ER capacity. Resting and task HRV were associated with ER brain activation in mid-line frontal cortex (i.e. DMPFC).
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Affiliation(s)
- Simón Guendelman
- Clinical Psychology of Social Interaction, Institute of Psychology, Humboldt-Universität Zu Berlin, Berlin, Germany.
- Berlin School of Mind and Brain, Humboldt-Universität Zu Berlin, Berlin, Germany.
| | - Laura Kaltwasser
- Berlin School of Mind and Brain, Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Mareike Bayer
- Clinical Psychology of Social Interaction, Institute of Psychology, Humboldt-Universität Zu Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Vittorio Gallese
- Department of Medicine & Surgery, Unit of Neuroscience, University of Parma, Parma, Italy
- Italian Academy for Advanced Studies in America, Columbia University, New York, USA
| | - Isabel Dziobek
- Clinical Psychology of Social Interaction, Institute of Psychology, Humboldt-Universität Zu Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität Zu Berlin, Berlin, Germany
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2
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Rosell-Hidalgo A, Bruhn C, Shardlow E, Barton R, Ryder S, Samatov T, Hackmann A, Aquino GR, Fernandes Dos Reis M, Galatenko V, Fritsch R, Dohrmann C, Walker PA. In-depth mechanistic analysis including high-throughput RNA sequencing in the prediction of functional and structural cardiotoxicants using hiPSC cardiomyocytes. Expert Opin Drug Metab Toxicol 2024; 20:685-707. [PMID: 37995132 DOI: 10.1080/17425255.2023.2273378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/05/2023] [Accepted: 09/15/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Cardiotoxicity remains one of the most reported adverse drug reactions that lead to drug attrition during pre-clinical and clinical drug development. Drug-induced cardiotoxicity may develop as a functional change in cardiac electrophysiology (acute alteration of the mechanical function of the myocardium) and/or as a structural change, resulting in loss of viability and morphological damage to cardiac tissue. RESEARCH DESIGN AND METHODS Non-clinical models with better predictive value need to be established to improve cardiac safety pharmacology. To this end, high-throughput RNA sequencing (ScreenSeq) was combined with high-content imaging (HCI) and Ca2+ transience (CaT) to analyze compound-treated human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). RESULTS Analysis of hiPSC-CMs treated with 33 cardiotoxicants and 9 non-cardiotoxicants of mixed therapeutic indications facilitated compound clustering by mechanism of action, scoring of pathway activities related to cardiomyocyte contractility, mitochondrial integrity, metabolic state, diverse stress responses and the prediction of cardiotoxicity risk. The combination of ScreenSeq, HCI and CaT provided a high cardiotoxicity prediction performance with 89% specificity, 91% sensitivity and 90% accuracy. CONCLUSIONS Overall, this study introduces mechanism-driven risk assessment approach combining structural, functional and molecular high-throughput methods for pre-clinical risk assessment of novel compounds.
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Ouyang W, Kilner KJ, Xavier RMP, Liu Y, Lu Y, Feller SM, Pitts KM, Wu M, Ausra J, Jones I, Wu Y, Luan H, Trueb J, Higbee-Dempsey EM, Stepien I, Ghoreishi-Haack N, Haney CR, Li H, Kozorovitskiy Y, Heshmati M, Banks AR, Golden SA, Good CH, Rogers JA. An implantable device for wireless monitoring of diverse physio-behavioral characteristics in freely behaving small animals and interacting groups. Neuron 2024; 112:1764-1777.e5. [PMID: 38537641 PMCID: PMC11256974 DOI: 10.1016/j.neuron.2024.02.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/08/2024] [Accepted: 02/28/2024] [Indexed: 06/09/2024]
Abstract
Comprehensive, continuous quantitative monitoring of intricately orchestrated physiological processes and behavioral states in living organisms can yield essential data for elucidating the function of neural circuits under healthy and diseased conditions, for defining the effects of potential drugs and treatments, and for tracking disease progression and recovery. Here, we report a wireless, battery-free implantable device and a set of associated algorithms that enable continuous, multiparametric physio-behavioral monitoring in freely behaving small animals and interacting groups. Through advanced analytics approaches applied to mechano-acoustic signals of diverse body processes, the device yields heart rate, respiratory rate, physical activity, temperature, and behavioral states. Demonstrations in pharmacological, locomotor, and acute and social stress tests and in optogenetic studies offer unique insights into the coordination of physio-behavioral characteristics associated with healthy and perturbed states. This technology has broad utility in neuroscience, physiology, behavior, and other areas that rely on studies of freely moving, small animal models.
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Affiliation(s)
- Wei Ouyang
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA; Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
| | - Keith J Kilner
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA; NeuroLux Inc., Northfield, IL 60093, USA
| | | | - Yiming Liu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA
| | - Yinsheng Lu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA
| | | | - Kayla M Pitts
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
| | - Mingzheng Wu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA
| | | | - Ian Jones
- NeuroLux Inc., Northfield, IL 60093, USA
| | - Yunyun Wu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA
| | - Haiwen Luan
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA
| | - Jacob Trueb
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA
| | | | - Iwona Stepien
- Developmental Therapeutics Core, Northwestern University, Evanston, IL 60208, USA
| | | | - Chad R Haney
- Center for Advanced Molecular Imaging, Northwestern University, Evanston, IL 60208, USA
| | - Hao Li
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Evanston, IL 60208, USA; Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Evanston, IL 60208, USA
| | - Yevgenia Kozorovitskiy
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
| | - Mitra Heshmati
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA; Center of Excellence in Neurobiology of Addiction, Pain, and Emotion (NAPE), University of Washington, Seattle, WA 98195, USA; Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98195, USA
| | - Anthony R Banks
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA; NeuroLux Inc., Northfield, IL 60093, USA
| | - Sam A Golden
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA; Center of Excellence in Neurobiology of Addiction, Pain, and Emotion (NAPE), University of Washington, Seattle, WA 98195, USA.
| | - Cameron H Good
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA; NeuroLux Inc., Northfield, IL 60093, USA.
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA; Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA; Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA; Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA; Department of Chemistry, Northwestern University, Evanston, IL 60208, USA; Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Evanston, IL 60208, USA.
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Kaisti M, Panula T, Sirkiä J, Pänkäälä M, Koivisto T, Niiranen T, Kantola I. Hemodynamic Bedside Monitoring Instrument with Pressure and Optical Sensors: Validation and Modality Comparison. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307718. [PMID: 38647263 PMCID: PMC11200005 DOI: 10.1002/advs.202307718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 01/19/2024] [Indexed: 04/25/2024]
Abstract
Results from two independent clinical validation studies for measuring hemodynamics at the patient's bedside using a compact finger probe are reported. Technology comprises a barometric pressure sensor, and in one implementation, additionally, an optical sensor for photoplethysmography (PPG) is developed, which can be used to measure blood pressure and analyze rhythm, including the continuous detection of atrial fibrillation. The capabilities of the technology are shown in several form factors, including a miniaturized version resembling a common pulse oximeter to which the technology could be integrated in. Several main results are presented: i) the miniature finger probe meets the accuracy requirements of non-invasive blood pressure instrument validation standard, ii) atrial fibrillation can be detected during the blood pressure measurement and in a continuous recording, iii) a unique comparison between optical and pressure sensing mechanisms is provided, which shows that the origin of both modalities can be explained using a pressure-volume model and that recordings are close to identical between the sensors. The benefits and limitations of both modalities in hemodynamic monitoring are further discussed.
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Affiliation(s)
- Matti Kaisti
- Department of ComputingUniversity of Turku, Faculty of TechnologyVesilinnantie 5Turku20500Finland
| | - Tuukka Panula
- Department of ComputingUniversity of Turku, Faculty of TechnologyVesilinnantie 5Turku20500Finland
| | - Jukka‐Pekka Sirkiä
- Department of ComputingUniversity of Turku, Faculty of TechnologyVesilinnantie 5Turku20500Finland
| | - Mikko Pänkäälä
- Department of ComputingUniversity of Turku, Faculty of TechnologyVesilinnantie 5Turku20500Finland
| | - Tero Koivisto
- Department of ComputingUniversity of Turku, Faculty of TechnologyVesilinnantie 5Turku20500Finland
| | - Teemu Niiranen
- Department of Internal MedicineUniversity of TurkuKiinamyllynkatu 4‐8Turku20521Finland
| | - Ilkka Kantola
- Division of MedicineTurku University HospitalKiinamyllynkatu 4‐8Turku20521Finland
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Haddad F, Saraste A, Santalahti KM, Pänkäälä M, Kaisti M, Kandolin R, Simonen P, Nammas W, Jafarian Dehkordi K, Koivisto T, Knuuti J, Mahaffey KW, Blomster JI. Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors. JACC. HEART FAILURE 2024; 12:1030-1040. [PMID: 38573263 DOI: 10.1016/j.jchf.2024.01.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND Heart failure (HF) is the leading cause of hospitalization in individuals over 65 years of age. Identifying noninvasive methods to detect HF may address the epidemic of HF. Seismocardiography which measures cardiac vibrations transmitted to the chest wall has recently emerged as a promising technology to detect HF. OBJECTIVES In this multicenter study, the authors examined whether seismocardiography using commercially available smartphones can differentiate control subjects from patients with stage C HF. METHODS Both inpatients and outpatients with HF were enrolled from Finland and the United States. Inpatients with HF were assessed within 2 days of admission, and outpatients were assessed in the ambulatory setting. In a prespecified pooled data analysis, algorithms were derived using logistic regression and then validated using a bootstrap aggregation method. RESULTS A total of 217 participants with HF (174 inpatients and 172 outpatients) and 786 control subjects from cardiovascular clinics were enrolled. The mean age of participants with acute HF was 64 ± 13 years, 64.9% were male, left ventricular ejection fraction was 39% ± 15%, and median N-terminal pro-B-type natriuretic peptide was 5,778 ng/L (Q1-Q3: 1,933-6,703). The majority (74%) of participants with HF had reduced EF, and 38% had atrial fibrillation. Across both HF cohorts, the algorithms had an area under the receiver operating characteristic curve of 0.95 with a sensitivity of 85%, specificity of 90%, and accuracy of 89% for the detection of HF, with a decision threshold of 0.5. The positive and negative likelihood ratios were 8.50 and 0.17, respectively. The accuracy of the algorithms was not significantly different in subgroups based on age, sex, body mass index, and atrial fibrillation. CONCLUSIONS Smartphone-based assessment of cardiac function using seismocardiography is feasible and differentiates patients with HF from control subjects with high diagnostic accuracy. (Recognition of Heart Failure With Micro Electro-mechanical Sensors FI; NCT04444583; Recognition of Heart Failure With Micro Electro-mechanical Sensors [NCT04378179]; Detection of Coronary Artery Disease With Micro Electro-mechanical Sensors; NCT04290091).
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Affiliation(s)
- Francois Haddad
- Stanford Center for Clinical Research, Stanford University School of Medicine, Palo Alto, California, USA.
| | - Antti Saraste
- Heart Center, Turku University Hospital, Turku, Finland; University of Turku, Turku, Finland
| | | | - Mikko Pänkäälä
- University of Turku, Turku, Finland; CardioSignal, Turku, Finland
| | - Matti Kaisti
- University of Turku, Turku, Finland; CardioSignal, Turku, Finland
| | | | | | - Wail Nammas
- Heart Center, Turku University Hospital, Turku, Finland
| | | | - Tero Koivisto
- University of Turku, Turku, Finland; CardioSignal, Turku, Finland
| | - Juhani Knuuti
- University of Turku, Turku, Finland; Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Kenneth W Mahaffey
- Stanford Center for Clinical Research, Stanford University School of Medicine, Palo Alto, California, USA
| | - Juuso I Blomster
- University of Turku, Turku, Finland; CardioSignal, Turku, Finland; Research Services, Turku University Hospital, Turku, Finland
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Sirkiä J, Panula T, Kaisti M. Non-Invasive Hemodynamic Monitoring System Integrating Spectrometry, Photoplethysmography, and Arterial Pressure Measurement Capabilities. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2310022. [PMID: 38647403 PMCID: PMC11199981 DOI: 10.1002/advs.202310022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/12/2024] [Indexed: 04/25/2024]
Abstract
Minimally invasive and non-invasive hemodynamic monitoring technologies have recently gained more attention, driven by technological advances and the inherent risk of complications in invasive techniques. In this article, an experimental non-invasive system is presented that effectively combines the capabilities of spectrometry, photoplethysmography (PPG), and arterial pressure measurement. Both time- and wavelength-resolved optical signals from the fingertip are measured under external pressure, which gradually increased above the level of systolic blood pressure. The optical channels measured at 434-731 nm divided into three groups separated by a group of channels with wavelengths approximately between 590 and 630 nm. This group of channels, labeled transition band, is characterized by abrupt changes resulting from a decrease in the absorption coefficient of whole blood. External pressure levels of maximum pulsation showed that shorter wavelengths (<590 nm) probe superficial low-pressure blood vessels, whereas longer wavelengths (>630 nm) probe high-pressure arteries. The results on perfusion indices and DC component level changes showed clear differences between the optical channels, further highlighting the importance of wavelength selection in optical hemodynamic monitoring systems. Altogether, the results demonstrated that the integrated system presented has the potential to extract new hemodynamic information simultaneously from macrocirculation to microcirculation.
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Affiliation(s)
- Jukka‐Pekka Sirkiä
- Department of ComputingUniversity of TurkuVesilinnantie 5Turku20500Finland
| | - Tuukka Panula
- Department of ComputingUniversity of TurkuVesilinnantie 5Turku20500Finland
| | - Matti Kaisti
- Department of ComputingUniversity of TurkuVesilinnantie 5Turku20500Finland
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7
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Cerina L, Papini GB, Fonseca P, Overeem S, van Dijk JP, van Meulen F, Vullings R. Quantitative validation of the suprasternal pressure signal to assess respiratory effort during sleep. Physiol Meas 2024; 45:055020. [PMID: 38749433 DOI: 10.1088/1361-6579/ad4c35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 05/15/2024] [Indexed: 05/30/2024]
Abstract
Objective.Intra-esophageal pressure (Pes) measurement is the recommended gold standard to quantify respiratory effort during sleep, but used to limited extent in clinical practice due to multiple practical drawbacks. Respiratory inductance plethysmography belts (RIP) in conjunction with oronasal airflow are the accepted substitute in polysomnographic systems (PSG) thanks to a better usability, although they are partial views on tidal volume and flow rather than true respiratory effort and are often used without calibration. In their place, the pressure variations measured non-invasively at the suprasternal notch (SSP) may provide a better measure of effort. However, this type of sensor has been validated only for respiratory events in the context of obstructive sleep apnea syndrome (OSA). We aim to provide an extensive verification of the suprasternal pressure signal against RIP belts and Pes, covering both normal breathing and respiratory events.Approach.We simultaneously acquired suprasternal (207) and esophageal pressure (20) signals along with RIP belts during a clinical PSG of 207 participants. In each signal, we detected breaths with a custom algorithm, and evaluated the SSP in terms of detection quality, breathing rate estimation, and similarity of breathing patterns against RIP and Pes. Additionally, we examined how the SSP signal may diverge from RIP and Pes in presence of respiratory events scored by a sleep technician.Main results.The SSP signal proved to be a reliable substitute for both esophageal pressure (Pes) and respiratory inductance plethysmography (RIP) in terms of breath detection, with sensitivity and positive predictive value exceeding 75%, and low error in breathing rate estimation. The SSP was also consistent with Pes (correlation of 0.72, similarity 80.8%) in patterns of increasing pressure amplitude that are common in OSA.Significance.This work provides a quantitative analysis of suprasternal pressure sensors for respiratory effort measurements.
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Affiliation(s)
- Luca Cerina
- Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
| | - Gabriele B Papini
- Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
- Philips Research, Eindhoven, Noord Brabant, The Netherlands
| | - Pedro Fonseca
- Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
- Philips Research, Eindhoven, Noord Brabant, The Netherlands
| | - Sebastiaan Overeem
- Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
- Center for Sleep Medicine, Kempenhaeghe Foundation, Heeze, Noord Brabant, The Netherlands
| | - Johannes P van Dijk
- Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
- Center for Sleep Medicine, Kempenhaeghe Foundation, Heeze, Noord Brabant, The Netherlands
| | - Fokke van Meulen
- Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
- Center for Sleep Medicine, Kempenhaeghe Foundation, Heeze, Noord Brabant, The Netherlands
| | - Rik Vullings
- Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
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Liu L, Yu D, Lu H, Shan C, Wang W. Camera-Based Seismocardiogram for Heart Rate Variability Monitoring. IEEE J Biomed Health Inform 2024; 28:2794-2805. [PMID: 38412075 DOI: 10.1109/jbhi.2024.3370394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.
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Jeanningros L, Le Bloa M, Teres C, Herrera Siklody C, Porretta A, Pascale P, Luca A, Solana Muñoz J, Domenichini G, Meister TA, Soria Maldonado R, Tanner H, Vesin JM, Thiran JP, Lemay M, Rexhaj E, Pruvot E, Braun F. The influence of cardiac arrhythmias on the detection of heartbeats in the photoplethysmogram: benchmarking open-source algorithms. Physiol Meas 2024; 45:025005. [PMID: 38266291 DOI: 10.1088/1361-6579/ad2216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 01/24/2024] [Indexed: 01/26/2024]
Abstract
Objective.Cardiac arrhythmias are a leading cause of mortality worldwide. Wearable devices based on photoplethysmography give the opportunity to screen large populations, hence allowing for an earlier detection of pathological rhythms that might reduce the risks of complications and medical costs. While most of beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, the performance of these algorithms in patients with other cardiac arrhythmias, such as ventricular tachycardia or bigeminy, remain unknown to date.Approach. ThePPG-beatsopen-source framework, developed by Charlton and colleagues, evaluates the performance of the beat detectors namedQPPG,MSPTDandABDamong others. We applied thePPG-beatsframework on two newly acquired datasets, one containing seven different types of cardiac arrhythmia in hospital settings, and another dataset including two cardiac arrhythmias in ambulatory settings.Main Results. In a clinical setting, theQPPGbeat detector performed best on atrial fibrillation (with a medianF1score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked 2nd for bigeminy (75.7%) behindABDdetector (76.1%). In an ambulatory setting, theMSPTDbeat detector performed best on normal sinus rhythm (94.6%), and theQPPGdetector on atrial fibrillation (91.6%) and bigeminy (80.0%).Significance. Overall, the PPG beat detectorsQPPG,MSPTDandABDconsistently achieved higher performances than other detectors. However, the detection of beats from wrist-PPG signals is compromised in presence of bigeminy or ventricular tachycardia.
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Affiliation(s)
- Loïc Jeanningros
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
- Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
| | - Mathieu Le Bloa
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Cheryl Teres
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | | | | | - Patrizio Pascale
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Adrian Luca
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Jorge Solana Muñoz
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Giulia Domenichini
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Théo A Meister
- Department of Cardiology and Biomedical Research, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Rodrigo Soria Maldonado
- Department of Cardiology and Biomedical Research, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Hildegard Tanner
- Department of Cardiology and Biomedical Research, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Jean-Marc Vesin
- Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
| | | | - Mathieu Lemay
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
| | - Emrush Rexhaj
- Department of Cardiology and Biomedical Research, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Etienne Pruvot
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Fabian Braun
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
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Li D, Mao Y, Tu P, Shi H, Sun W, Zhao D, Chen C, Chen X. A robotic system for transthoracic puncture of pulmonary nodules based on gated respiratory compensation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107995. [PMID: 38157826 DOI: 10.1016/j.cmpb.2023.107995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND AND OBJECTIVE With the urgent demands for rapid and precise localization of pulmonary nodules in procedures such as transthoracic puncture biopsy and thoracoscopic surgery, many surgical navigation and robotic systems are applied in the clinical practice of thoracic operation. However, current available positioning methods have certain limitations, including high radiation exposure, large errors from respiratory, complicated and time-consuming procedures, etc. METHODS: To address these issues, a preoperative computed tomography (CT) image-guided robotic system for transthoracic puncture was proposed in this study. Firstly, an algorithm for puncture path planning based on constraints from clinical knowledge was developed. This algorithm enables the calculation of Pareto optimal solutions for multiple clinical targets concerning puncture angle, puncture length, and distance from hazardous areas. Secondly, to eradicate intraoperative radiation exposure, a fast registration method based on preoperative CT and gated respiration compensation was proposed. The registration process could be completed by the direct selection of points on the skin near the sternum using a hand-held probe. Gating detection and joint optimization algorithms are then performed on the collected point cloud data to compensate for errors from respiratory motion. Thirdly, to enhance accuracy and intraoperative safety, the puncture guide was utilized as an end effector to restrict the movement of the optically tracked needle, then risky actions with patient contact would be strictly limited. RESULTS The proposed system was evaluated through phantom experiments on our custom-designed simulation test platform for patient respiratory motion to assess its accuracy and feasibility. The results demonstrated an average target point error (TPE) of 2.46 ± 0.68 mm and an angle error (AE) of 1.49 ± 0.45° for the robotic system. CONCLUSIONS In conclusion, our proposed system ensures accuracy, surgical efficiency, and safety while also reducing needle insertions and radiation exposure in transthoracic puncture procedures, thus offering substantial potential for clinical application.
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Affiliation(s)
- Dongyuan Li
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Yuxuan Mao
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Puxun Tu
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Haochen Shi
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Weiyan Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
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11
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Wang X, Li J, Xu G, Wang X. A Novel Zero-Velocity Interval Detection Algorithm for a Pedestrian Navigation System with Foot-Mounted Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:838. [PMID: 38339555 PMCID: PMC10857087 DOI: 10.3390/s24030838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/15/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian's gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems.
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12
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Gao C, Zhao P, Fan Q, Jing H, Dang R, Sun W, Feng Y, Hu B, Wang Q. Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123086. [PMID: 37451210 DOI: 10.1016/j.saa.2023.123086] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/24/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023]
Abstract
Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.
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Affiliation(s)
- Chi Gao
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Zhao
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Fan
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Haonan Jing
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ruochen Dang
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weifeng Sun
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yutao Feng
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China
| | - Bingliang Hu
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Quan Wang
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China.
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13
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Delfan N, Forouzanfar M. Hybrid Deep Morpho-Temporal Framework for Oscillometric Blood Pressure Measurement. IEEE J Biomed Health Inform 2023; 27:5293-5301. [PMID: 37651480 DOI: 10.1109/jbhi.2023.3310868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Oscillometric blood pressure (BP) measurement devices are widely utilized as the primary automated BP measurement tools in non-specialist environments. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. An essential constraint of current oscillometric BP measurement devices is their analysis algorithms' incapacity to capture the BP information encoded in the pattern of recorded oscillometric pulses to its fullest extent. In this article, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.
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14
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Yao A, Chou Y, Yang L, Hu L, Liu J, Gu S. Research on heart rate extraction method based on mobile phone video. Med Eng Phys 2023; 120:104051. [PMID: 37838408 DOI: 10.1016/j.medengphy.2023.104051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/26/2023] [Accepted: 09/11/2023] [Indexed: 10/16/2023]
Abstract
As an important indicator of human health, heart rate is related to the diagnosis of cardiovascular diseases. In recent years, extracting the heart rate from the mobile phone image has become a research hotspot. However, the illumination intensity of the background, frame rate of the video, and resolution of the image influence heart rate detection accuracy. To overcome these problems, this study proposed a novel heart rate extraction method based on mobile video. Firstly, the mobile phone camera is engaged to record the finger video, the region of interest (ROI) is extracted through the iterative threshold, and the pulse signal is obtained according to the grayscale change of the resolution within the ROI. Then, a low-pass and a high-pass Butterworth filters are exploited to filter out the noise and interframes from the extracted pulse signal. Finally, an improved adaptive peak extraction algorithm is proposed to detect the pulse peaks and the heart rate derived from the difference in pulse peaks. The experimental results show that light intensity, frame rate and resolution all have an influence on the heart rate extraction accuracy, with the most obvious influence of light, the average accuracy of the experiment can reach 99.32 % under good lighting conditions, while only 72.23 % under poor lighting conditions. In terms of frame rate, increasing the frame rate from 30 fps to 60 fps, the accuracy is improved by 0.9 %. For the resolution, increasing the resolution from 1080 p to 2160 p, the accuracy is improved by 1.12 %. While comparing the proposed method with existing methods, the proposed method has a higher accuracy rate, which has important practical value and application prospects in telemedicine and daily monitoring.
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Affiliation(s)
- An Yao
- School of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224000, China; School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
| | - Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China.
| | - Liming Yang
- School of Internet of Things Application Technology, Changzhou College of Information Technology, Changzhou 213164, China
| | - Linqi Hu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China; School of Chemical Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
| | - Suhang Gu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
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15
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Franklin D, Tzavelis A, Lee JY, Chung HU, Trueb J, Arafa H, Kwak SS, Huang I, Liu Y, Rathod M, Wu J, Liu H, Wu C, Pandit JA, Ahmad FS, McCarthy PM, Rogers JA. Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography. Nat Biomed Eng 2023; 7:1229-1241. [PMID: 37783757 PMCID: PMC10653655 DOI: 10.1038/s41551-023-01098-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 08/18/2023] [Indexed: 10/04/2023]
Abstract
Cardiovascular health is typically monitored by measuring blood pressure. Here we describe a wireless on-skin system consisting of synchronized sensors for chest electrocardiography and peripheral multispectral photoplethysmography for the continuous monitoring of metrics related to vascular resistance, cardiac output and blood-pressure regulation. We used data from the sensors to train a support-vector-machine model for the classification of haemodynamic states (resulting from exposure to heat or cold, physical exercise, breath holding, performing the Valsalva manoeuvre or from vasopressor administration during post-operative hypotension) that independently affect blood pressure, cardiac output and vascular resistance. The model classified the haemodynamic states on the basis of an unseen subset of sensor data for 10 healthy individuals, 20 patients with hypertension undergoing haemodynamic stimuli and 15 patients recovering from cardiac surgery, with an average precision of 0.878 and an overall area under the receiver operating characteristic curve of 0.958. The multinodal sensor system may provide clinically actionable insights into haemodynamic states for use in the management of cardiovascular disease.
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Affiliation(s)
- Daniel Franklin
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada.
| | - Andreas Tzavelis
- Medical Scientist Training Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | | | | | - Jacob Trueb
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Hany Arafa
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Sung Soo Kwak
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Ivy Huang
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Yiming Liu
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Megh Rathod
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada
| | - Jonathan Wu
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada
| | - Haolin Liu
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada
| | - Changsheng Wu
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Jay A Pandit
- Scripps Research Translational Institute, San Diego, CA, USA
| | - Faraz S Ahmad
- Division of Cardiology, Department of Medicine, Bluhm Cardiovascular Institute, Northwestern University, Chicago, IL, USA
| | - Patrick M McCarthy
- Division of Cardiac Surgery, Department of Surgery, Bluhm Cardiovascular Institute, Northwestern University, Chicago, IL, USA
| | - John A Rogers
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA.
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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16
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Schönenberg-Tu AL, Cysarz D, Petzold B, Blümel CB, Raak C, Fricke O, Edelhäuser F, Scharbrodt W. Pressure Time Dose as a Representation of Intracranial Pressure Burden and Its Dependency on Intracranial Pressure Waveform Morphology at Different Time Intervals. SENSORS (BASEL, SWITZERLAND) 2023; 23:8051. [PMID: 37836881 PMCID: PMC10574990 DOI: 10.3390/s23198051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023]
Abstract
Intracranial pressure (ICP) burden or pressure time dose (PTD) is a valuable clinical indicator for pending intracranial hypertension, mostly based on threshold exceedance. Pulse frequency and waveform morphology (WFM) of the ICP signal contribute to PTD. The temporal resolution of the ICP signal has a great influence on PTD calculation but has not been systematically studied yet. Hence, the temporal resolution of the ICP signal on PTD calculation is investigated. We retrospectively analysed continuous 48 h ICP recordings with high temporal resolution obtained from 94 patients at the intensive care unit who underwent neurosurgery due to an intracranial haemorrhage and received an intracranial pressure probe (43 females, median age: 72 years, range: 23 to 88 years). The cumulative area under the curve above the threshold of 20 mmHg was compared for different temporal resolutions of the ICP signal (beat-to-beat, 1 s, 300 s, 1800 s, 3600 s). Events with prolonged ICP elevation were compared to those with few isolated threshold exceedances. PTD increased for lower temporal resolutions independent of WFM and frequency of threshold exceedance. PTDbeat-to-beat best reflected the impact of frequency of threshold exceedance and WFM. Events that could be distinguished in PTDbeat-to-beat became magnified more than 7-fold in PTD1s and more than 104 times in PTD1h, indicating an overestimation of PTD. PTD calculation should be standardised, and beat-by-beat PTD could serve as an easy-to-grasp indicator for the impact of frequency and WFM of ICP elevations on ICP burden.
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Affiliation(s)
- Anna-Li Schönenberg-Tu
- Chair of Integrative Neuro-Medicine, Gemeinschaftskrankenhaus Herdecke, 58313 Herdecke, Germany
| | - Dirk Cysarz
- Institute of Integrative Medicine, Witten/Herdecke University, 58313 Herdecke, Germany
- Integrated Curriculum for Anthroposophic Medicine, Witten/Herdecke University, 58313 Herdecke, Germany
| | - Benjamin Petzold
- Chair of Integrative Neuro-Medicine, Gemeinschaftskrankenhaus Herdecke, 58313 Herdecke, Germany
| | - Carl Benjamin Blümel
- Chair of Integrative Neuro-Medicine, Gemeinschaftskrankenhaus Herdecke, 58313 Herdecke, Germany
| | - Christa Raak
- Institute of Integrative Medicine, Witten/Herdecke University, 58313 Herdecke, Germany
| | - Oliver Fricke
- Faculty of Health, Department of Human Medicine, Witten/Herdecke University, 58455 Witten, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Klinikum Stuttgart, 70174 Stuttgart, Germany
| | - Friedrich Edelhäuser
- Institute of Integrative Medicine, Witten/Herdecke University, 58313 Herdecke, Germany
- Integrated Curriculum for Anthroposophic Medicine, Witten/Herdecke University, 58313 Herdecke, Germany
| | - Wolfram Scharbrodt
- Chair of Integrative Neuro-Medicine, Gemeinschaftskrankenhaus Herdecke, 58313 Herdecke, Germany
- Institute of Integrative Medicine, Witten/Herdecke University, 58313 Herdecke, Germany
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17
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Moussalli P, Li S, Geweid GGN, Zhu H, Chen JDZ. An efficient online peak detection algorithm for synchronized intestinal electrical stimulation and its application for treating diabetes. Med Biol Eng Comput 2023; 61:2317-2327. [PMID: 37060485 PMCID: PMC10461231 DOI: 10.1007/s11517-023-02832-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 04/05/2023] [Indexed: 04/16/2023]
Abstract
Obesity is one of leading risk factors for type 2 diabetes and other types of chronic diseases. Synchronized intestinal electrical stimulation (SIES) has been explored for treating obesity and diabetes. In SIES, electrical stimulation is delivered to the small intestine in synchronization with the intrinsic intestinal myoelectrical activity (its basic rhythm is called slow wave) and therefore, the accurate detection of intestinal slow waves is critically important for SIES. The aim of this study is to detect the peaks in intestinal slow waves in real-time based on the automatic multiscale peak detection (AMPD) method. In this paper, we introduce an efficient technique for real-time detection of peaks in intestinal slow waves. The presented method is based on peak estimation of a given quasi-periodic signal using the AMPD method. This method uses a multi-scale approach to identify the peaks of the intestinal slow waves with high detection accuracy and a minimal delay. Throughout the experiments, the multi-scale technique is used to estimate the quasi-periodic signals using different signal-to-noise ratio, λ (optimal scale), and the "lag" β (number of datapoints for right hand estimation) as important performance factors. The performance of the presented method is also calculated and utilized in the comparison process for 10 datasets of the intestinal slow waves from rats at λ = 150 ms and two values of β = 100 ms and 150 ms. The experimental results show that the presented method has good overall accuracy for online peak detection while maintaining low memory and computational complexity. Numerically, the overall accuracy is above 90%, and 98% for the rodent intestinal slow waves at a time-lag of 150 ms. The developed SIES system has been applied to successfully reduce postprandial blood glucose in a rodent model of hyperglycemia. In conclusion, the developed algorithm is adequate for on-line peak detection of the intestinal slow waves; the SIES method used the developed peak detection algorithm which is effective in reducing postprandial blood glucose in a rodent model of hyperglycemia.
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Affiliation(s)
- Philippe Moussalli
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, USA
- Department of Electrical Engineering, Catholic University of Leuven, Louvain, Belgium
| | - Shiying Li
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Gamal G N Geweid
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, USA
- Electrical Engineering Department, Faculty of Engineering, Benha University, Benha, Egypt
| | - Hongbing Zhu
- Transtimulation Research Inc, Oklahoma City, OK, USA
| | - Jiande D Z Chen
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, USA.
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18
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Odinaev I, Wong KL, Chin JW, Goyal R, Chan TT, So RHY. Robust Heart Rate Variability Measurement from Facial Videos. Bioengineering (Basel) 2023; 10:851. [PMID: 37508878 PMCID: PMC10376629 DOI: 10.3390/bioengineering10070851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/30/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess noisy contact-based PPG signals. WaveHRV is bench-marked against existing algorithms and public datasets. Our results show that WaveHRV is promising and achieves the lowest mean absolute error (MAE) of 10.5 ms and 6.15 ms for RMSSD and SDNN on the UBFCrPPG dataset.
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Affiliation(s)
| | - Kwan Long Wong
- PanopticAI Ltd., Hong Kong, China
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | | | - Raghav Goyal
- PanopticAI Ltd., Hong Kong, China
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | | | - Richard H Y So
- PanopticAI Ltd., Hong Kong, China
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
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19
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Zhang X, Cheng Z, Du L, Du Y. Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115090. [PMID: 37299817 DOI: 10.3390/s23115090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/19/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results.
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Affiliation(s)
- Xulong Zhang
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
| | - Zihao Cheng
- School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Li Du
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
| | - Yuan Du
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
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20
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Bizzego A, Esposito G. Performance Assessment of Heartbeat Detection Algorithms on Photoplethysmograph and Functional NearInfrared Spectroscopy Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:3668. [PMID: 37050728 PMCID: PMC10099230 DOI: 10.3390/s23073668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/21/2023] [Accepted: 03/29/2023] [Indexed: 06/19/2023]
Abstract
With wearable sensors, the acquisition of physiological signals has become affordable and feasible in everyday life. Specifically, Photoplethysmography (PPG), being a low-cost and highly portable technology, has attracted notable interest for measuring and diagnosing cardiac activity, one of the most important physiological and autonomic indicators. In addition to the technological development, several specific signal-processing algorithms have been designed to enable reliable detection of heartbeats and cope with the lower quality of the signals. In this study, we compare three heartbeat detection algorithms: Derivative-Based Detection (DBD), Recursive Combinatorial Optimization (RCO), and Multi-Scale Peak and Trough Detection (MSPTD). In particular, we considered signals from two datasets, namely, the PPG-DALIA dataset (N = 15) and the FANTASIA dataset (N = 20) which differ in terms of signal characteristics (sampling frequency and length) and type of acquisition devices (wearable and medical-grade). The comparison is performed both in terms of heartbeat detection performance and computational workload required to execute the algorithms. Finally, we explore the applicability of these algorithms on the cardiac component obtained from functional Near InfraRed Spectroscopy signals (fNIRS).The results indicate that, while the MSPTD algorithm achieves a higher F1 score in cases that involve body movements, such as cycling (MSPTD: Mean = 74.7, SD = 14.4; DBD: Mean = 54.4, SD = 21.0; DBD + RCO: Mean = 49.5, SD = 22.9) and walking up and down the stairs (MSPTD: Mean = 62.9, SD = 12.2; DBD: Mean = 50.5, SD = 11.9; DBD + RCO: Mean = 45.0, SD = 14.0), for all other activities the three algorithms perform similarly. In terms of computational complexity, the computation time of the MSPTD algorithm appears to grow exponentially with the signal sampling frequency, thus requiring longer computation times in the case of high-sampling frequency signals, where the usage of the DBD and RCO algorithms might be preferable. All three algorithms appear to be appropriate candidates for exploring the applicability of heartbeat detection on fNIRS data.
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Huels ER, Kafashan M, Hickman LB, Ching S, Lin N, Lenze EJ, Farber NB, Avidan MS, Hogan RE, Palanca BJA. Central-positive complexes in ECT-induced seizures: Possible evidence for thalamocortical mechanisms. Clin Neurophysiol 2023; 146:77-86. [PMID: 36549264 PMCID: PMC10273093 DOI: 10.1016/j.clinph.2022.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/20/2022] [Accepted: 11/27/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Central-positive complexes (CPCs) are elicited during electroconvulsive therapy (ECT) as generalized high-amplitude waveforms with maximum positive voltage over the vertex. While these complexes have been qualitatively assessed in previous literature, quantitative analyses are lacking. This study aims to characterize CPCs across temporal, spatial, and spectral domains. METHODS High-density 64-electrode electroencephalogram (EEG) recordings during 50 seizures acquired from 11 patients undergoing right unilateral ECT allowed for evaluation of spatiotemporal characteristics of CPCs via source localization and spectral analysis. RESULTS Peak-amplitude CPC scalp topology was consistent across seizures, showing maximal positive polarity over the midline fronto-central region and maximal negative polarity over the suborbital regions. The sources of these peak potentials were localized to the bilateral medial thalamus and cingulate cortical regions. Delta, beta, and gamma oscillations were correlated with the peak amplitude of CPCs during seizures induced during ketamine, whereas delta and gamma oscillations were associated with CPC peaks during etomidate anesthesia (excluding the dose-charge titration). CONCLUSIONS Our findings demonstrate the consistency of CPC presence across participant, stimulus charge, time, and anesthetic agent, with peaks localized to bilateral medial thalamus and cingulate cortical regions and associated with delta, beta, and gamma band oscillations (depending on the anesthetic condition). SIGNIFICANCE The consistency and reproducibility of CPCs offers ECT as a new avenue for studying the dynamics of generalized seizure activity and thalamocortical networks.
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Affiliation(s)
- Emma R Huels
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA; Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA
| | - MohammadMehdi Kafashan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - L Brian Hickman
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - ShiNung Ching
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Nan Lin
- Department of Mathematics and Statistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Nuri B Farber
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - R Edward Hogan
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ben Julian A Palanca
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
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22
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van Meulen FB, Grassi A, van den Heuvel L, Overeem S, van Gilst MM, van Dijk JP, Maass H, van Gastel MJH, Fonseca P. Contactless Camera-Based Sleep Staging: The HealthBed Study. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010109. [PMID: 36671681 PMCID: PMC9855193 DOI: 10.3390/bioengineering10010109] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/06/2023] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake-N1/N2/N3-REM) and 4 class (Wake-N1/N2-N3-REM) classification, with average κ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging.
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Affiliation(s)
- Fokke B. van Meulen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
- Correspondence:
| | - Angela Grassi
- Philips Research, 5656 AE Eindhoven, The Netherlands
| | | | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
| | - Merel M. van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
| | - Johannes P. van Dijk
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
| | - Henning Maass
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Mark J. H. van Gastel
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Philips Research, 5656 AE Eindhoven, The Netherlands
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Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Ibbini Z, Spicer JI, Truebano M, Bishop J, Tills O. HeartCV: a tool for transferrable, automated measurement of heart rate and heart rate variability in transparent animals. J Exp Biol 2022; 225:276574. [PMID: 36073614 PMCID: PMC9659326 DOI: 10.1242/jeb.244729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/25/2022] [Indexed: 11/20/2022]
Abstract
Heart function is a key component of whole-organismal physiology. Bioimaging is commonly, but not exclusively, used for quantifying heart function in transparent individuals, including early developmental stages of aquatic animals, many of which are transparent. However, a central limitation of many imaging-related methods is the lack of transferability between species, life-history stages and experimental approaches. Furthermore, locating the heart in mobile individuals remains challenging. Here, we present HeartCV: an open-source Python package for automated measurement of heart rate and heart rate variability that integrates automated localization and is transferrable across a wide range of species. We demonstrate the efficacy of HeartCV by comparing its outputs with measurements made manually for a number of very different species with contrasting heart morphologies. Lastly, we demonstrate the applicability of the software to different experimental approaches and to different dataset types, such as those corresponding to longitudinal studies.
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Affiliation(s)
- Ziad Ibbini
- Marine Biology and Ecology Research Centre, Plymouth University, Plymouth PL4 8AA, UK
- Author for correspondence ()
| | - John I. Spicer
- Marine Biology and Ecology Research Centre, Plymouth University, Plymouth PL4 8AA, UK
| | - Manuela Truebano
- Marine Biology and Ecology Research Centre, Plymouth University, Plymouth PL4 8AA, UK
| | - John Bishop
- Marine Biological Association of the UK, Citadel Hill Laboratory, Plymouth PL1 2PB, UK
| | - Oliver Tills
- Marine Biology and Ecology Research Centre, Plymouth University, Plymouth PL4 8AA, UK
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25
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Guo CY, Chang CC, Wang KJ, Hsieh TL. Assessment of a Calibration-Free Method of Cuffless Blood Pressure Measurement: A Pilot Study. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 11:318-329. [PMID: 38163041 PMCID: PMC10756135 DOI: 10.1109/jtehm.2022.3209754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/30/2022] [Accepted: 09/19/2022] [Indexed: 01/03/2024]
Abstract
This study proposes a low-cost, high-sensitivity sensor of beat-to-beat local pulse wave velocity (PWV), to be used in a cuffless blood pressure monitor (BPM). OBJECTIVE We design an adaptive algorithm to detect the feature of the pulse wave, making it possible for two sensors to measure the local PWV in the radial artery at a short distance. Unlike the cuffless BPM that needs to use a regression model for calibration. METHOD We encapsulate the piezoelectric sensor material in a cavity and design an analog front-end circuit. This study used color ultrasound imaging equipment to measure radial arterial parameters, including the diameter and wall thickness, to aid the estimation of blood pressure (BP) using the Moens-Korteweg (MK) equation of hemodynamics. RESULTS We compared the blood pressure estimated by the MK equation with the reference BP measured using an aneroid sphygmomanometer in a test group of 32 people, resulting in a mean difference of systolic BP of -0.63 mmHg, and a standard deviation of ±5.14 mmHg, a mean difference of mean arterial pressure (MAP) of 0.97 mmHg, with a standard deviation of ±3.54 mmHg, and a mean difference of diastolic BP of -1.14 mmHg, with a standard deviation of ±4.08 mmHg. This study has verified its compliance with ISO 81060-2. CONCLUSIONS A new type of wearable continuous calibration-free BPM can replace the situation that requires the use of traditional ambulatory BPM and reduce patient discomfort. CLINICAL IMPACT In this study can provide long-term continuous blood pressure monitoring in the hospital.
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Affiliation(s)
| | | | | | - Tung-Li Hsieh
- Department of Electronic EngineeringNational Kaohsiung University of Science and Technology, SanminKaohsiung City807618Taiwan
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26
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Polak AG, Klich B, Saganowski S, Prucnal MA, Kazienko P. Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability. SENSORS (BASEL, SWITZERLAND) 2022; 22:7047. [PMID: 36146394 PMCID: PMC9502353 DOI: 10.3390/s22187047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.
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Affiliation(s)
- Adam G. Polak
- Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, 50-317 Wrocław, Poland
| | - Bartłomiej Klich
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Stanisław Saganowski
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Monika A. Prucnal
- Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, 50-317 Wrocław, Poland
| | - Przemysław Kazienko
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
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27
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Bonnard E, Liu J, Zjacic N, Alvarez L, Scholz M. Automatically tracking feeding behavior in populations of foraging C. elegans. eLife 2022; 11:e77252. [PMID: 36083280 PMCID: PMC9462848 DOI: 10.7554/elife.77252] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Caenorhabditis elegans feeds on bacteria and other small microorganisms which it ingests using its pharynx, a neuromuscular pump. Currently, measuring feeding behavior requires tracking a single animal, indirectly estimating food intake from population-level metrics, or using restrained animals. To enable large throughput feeding measurements of unrestrained, crawling worms on agarose plates at a single worm resolution, we developed an imaging protocol and a complementary image analysis tool called PharaGlow. We image up to 50 unrestrained crawling worms simultaneously and extract locomotion and feeding behaviors. We demonstrate the tool's robustness and high-throughput capabilities by measuring feeding in different use-case scenarios, such as through development, with genetic and chemical perturbations that result in faster and slower pumping, and in the presence or absence of food. Finally, we demonstrate that our tool is capable of long-term imaging by showing behavioral dynamics of mating animals and worms with different genetic backgrounds. The low-resolution fluorescence microscopes required are readily available in C. elegans laboratories, and in combination with our python-based analysis workflow makes this methodology easily accessible. PharaGlow therefore enables the observation and analysis of the temporal dynamics of feeding and locomotory behaviors with high-throughput and precision in a user-friendly system.
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Affiliation(s)
- Elsa Bonnard
- Max Planck Research Group Neural Information Flow, Max Planck Institute for Neurobiology of Behavior – caesarBonnGermany
| | - Jun Liu
- Max Planck Research Group Neural Information Flow, Max Planck Institute for Neurobiology of Behavior – caesarBonnGermany
| | - Nicolina Zjacic
- Max Planck Research Group Neural Information Flow, Max Planck Institute for Neurobiology of Behavior – caesarBonnGermany
- Institute of Medical Genetics, University of ZurichZurichSwitzerland
| | - Luis Alvarez
- Max Planck Research Group Neural Information Flow, Max Planck Institute for Neurobiology of Behavior – caesarBonnGermany
| | - Monika Scholz
- Max Planck Research Group Neural Information Flow, Max Planck Institute for Neurobiology of Behavior – caesarBonnGermany
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28
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Jacquelin N, Vuillemot R, Duffner S. Periodicity counting in videos with unsupervised learning of cyclic embeddings. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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29
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Charlton PH, Kotzen K, Mejía-Mejía E, Aston PJ, Budidha K, Mant J, Pettit C, Behar JA, Kyriacou PA. Detecting beats in the photoplethysmogram: benchmarking open-source algorithms. Physiol Meas 2022; 43:085007. [PMID: 35853440 PMCID: PMC9393905 DOI: 10.1088/1361-6579/ac826d] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 11/12/2022]
Abstract
The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best.Objective:This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology.Approach:Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using theF1score, which combines sensitivity and positive predictive value.Main results:Eight beat detectors performed well in the absence of movement withF1scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise withF1scores of 55%-91%; poorer in neonates than adults withF1scores of 84%-96% in neonates compared to 98%-99% in adults; and poorer in atrial fibrillation (AF) withF1scores of 92%-97% in AF compared to 99%-100% in normal sinus rhythm.Significance:Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Kevin Kotzen
- Faculty of Biomedical Engineering, Technion-IIT, Israel
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Philip J Aston
- Department of Mathematics, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom
| | - Karthik Budidha
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Jonathan Mant
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
| | - Callum Pettit
- Department of Mathematics, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom
| | | | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
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30
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Sosa J, Montiel-Nelson JA. Novel Deep-Water Tidal Meter for Offshore Aquaculture Infrastructures. SENSORS (BASEL, SWITZERLAND) 2022; 22:5513. [PMID: 35898017 PMCID: PMC9331778 DOI: 10.3390/s22155513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 02/05/2023]
Abstract
This paper presents a tidal current meter that is based on the inertial acceleration principle for offshore infrastructures in deep water. Focusing on the marine installations of the aquaculture industry, we studied the forces of tides at a depth of 15 m by measuring the acceleration. In addition, we used a commercial MEMS triaxial accelerometer to record the acceleration values. A prototype of the tidal measurement unit was developed and tested at a real offshore aquaculture infrastructure in Gran Canaria, which is one of the Canary Islands in the Atlantic Ocean. The proposed tidal measurement unit was used as a recorder to assess the complexity of measuring the frequency of tidal currents in the short (10 min), medium (one day) and long term (one week). The acquired data were studied in detail, in both the time and frequency domains, to determine the frequency of the forces that were involved. Finally, the complexity of the frequency measurements from the captured data was analyzed in terms of sampling ratio and recording duration, from the point of view of using our proposed measurement unit as an ultra-low-power embedded system. The proposed device was tested for more than 180 days using a lithium-ion battery. This working period was three times greater than the best alternative in the literature because of the ultra-low-power design of the on-board embedded system. The measurement accuracy error was lower than 1% and the resolution was 0.01 cm/s for the 0.8 m/s velocity scale. This performance was similar to the best Doppler solution that was found in the literature.
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Affiliation(s)
- Javier Sosa
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35015 Las Palmas de Gran Canaria, Spain;
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31
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Comparison of CRT and LCD monitors for objective estimation of visual acuity using the sweep VEP. Doc Ophthalmol 2022; 145:133-145. [PMID: 35788850 PMCID: PMC9470625 DOI: 10.1007/s10633-022-09883-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 06/09/2022] [Indexed: 11/29/2022]
Abstract
Purpose To investigate the applicability of liquid crystal displays (LCD) as suitable replacement for cathode ray tube monitors (CRT) as stimulator for the sweep VEP for estimating visual acuity. Methods In a first experiment, sweep VEPs were recorded in 13 healthy volunteers with best-corrected visual acuity with an LCD and a CRT monitor, respectively. Time-to-peak after stimulus and peak-to-trough amplitudes as well as the visual acuity, estimated using a second-order polynomial and the modified Ricker model, were compared between both monitor types. In a second experiment, sweep VEPs were recorded in six healthy volunteers with two levels of stimulus contrast using artificially reduced visual acuities as well as best-corrected with the same monitors as in the first experiment and additionally, a modern LCD gaming monitor with a response time of 1 ms. Time-to-peak after stimulus and peak-to-trough amplitudes were compared between the different combinations of monitors and contrasts. Finally, visual acuities estimated using the modified Ricker model were compared to subjective visual acuities determined using the Freiburg Visual Acuity and Contrast Test (FrACT). Results In the first experiment, the time-to-peak after stimulus presentation was statistically significantly delayed for LCD displays (mean difference [confidence interval]: 60.0 [54.0, 65.9] ms; t(516) = 19.7096, p < 0.0001). Likewise, peak-to-trough amplitudes were statistically significantly smaller for the LCD stimulator, however, not clinically relevant (mean difference [confidence interval]: − 0.89 [– 1.59, − 0.20] µV; t(516) = − 2.5351, p = 0.0115). No statistically significant effect of the monitor type on the estimated visual acuity was found for neither method, second-order polynomial, nor the modified Ricker model. In the second experiment, statistically significant delays of the time-to-peak after stimulus onset were found for all combinations of monitor and contrast compared to the CRT monitor. A statistically significant, but not clinically relevant, difference of the peak-to-trough amplitudes was only found between the CRT monitor and the LCD gaming monitor (mean difference [confidence interval]: 2.6 [1.2, 4.0] µV; t(814) = 4.66, p < 0.0001). Visual acuities estimated from LCD stimulation significantly underestimated the subjective visual acuity up to 0.2 logMAR using the conversion formula of the first experiment. No statistically significant difference was found when using conversion formulas adjusted for each combination of monitor and contrast. Conclusions Based on the results of this study, LCD monitors may substitute CRT monitors for presenting the stimuli for the sweep VEP to objectively estimate visual acuity. Nevertheless, it is advisable to perform a calibration and to collect normative data of healthy volunteers using best-corrected and artificially reduced visual acuity for establishing a conversion formula between sweep VEP outcome and the subjective visual acuity before replacing a CRT with an LCD stimulator. Supplementary Information The online version contains supplementary material available at 10.1007/s10633-022-09883-x.
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Kang K, Cho H, Jeong S, Jeon S, Lee M, Lee S, Baek Y, Oh J, Seo S. Application of a hand-held laser methane detector for measuring enteric methane emissions from cattle in intensive farming. J Anim Sci 2022; 100:6603902. [PMID: 35671336 DOI: 10.1093/jas/skac211] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/06/2022] [Indexed: 11/12/2022] Open
Abstract
The hand-held laser methane detector (LMD) technique has been suggested as an alternative method for measuring methane (CH4) emissions from enteric fermentation of ruminants in the field. This study aimed to establish a standard procedure for using LMD to assess CH4 production in cattle and evaluate the efficacy of the protocol to detect differences in CH4 emissions from cattle fed with diets of different forage-to-concentrate (FC) ratios. Experiment 1 was conducted with four Hanwoo steers (584 ± 57.4 kg body weight (BW)) individually housed in metabolic cages. The LMD was installed on a tripod aimed at the animal's nostril, and the CH4 concentration in the exhaled gas was measured for 6 min every hour for two consecutive days. For the data processing, the CH4 concentration peaks were identified by the automatic multi-scale peak detection algorithm. The peaks were then separated into those from respiration and eructation by fitting combinations of two of the four distribution functions (normal, log-normal, gamma, and Weibull) using the mixdist R package. In addition, the most appropriate time and number of consecutive measurements to represent the daily average CH4 concentration were determined. In Experiment 2, 30 Hanwoo growing steers (343 ± 24.6 kg BW), blocked by body weight, were randomly divided into three groups. Three different diets were provided to each group: high FC ratio (35:65) with low energy concentrate (HFC-LEC), high FC ratio with high energy concentrate (HFC-HEC), and low FC ratio (25:75) with high energy concentrate (LFC-HEC). After ten days of feeding the diets, the CH4 concentrations for all steers were measured and analyzed in duplicate according to the protocol established in Experiment 1. In Experiment 1, the mean correlation coefficient between the CH4 concentration from respiration and eructation was highest when a combination of two normal distributions was assumed (r = 0.79). The most appropriate measurement times were four times, two hours and one hour before, and one hour and two hours after morning feeding. Compared with LFC-HEC, HFC-LEC showed 49% and 57% higher CH4 concentrations in exhaled gas from respiration and eructation, respectively (P < 0.01). In conclusion, the LMD method can be applied to evaluate differences in CH4 emissions in cattle using the protocol established in this study.
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Affiliation(s)
- Kyewon Kang
- Division of Animal and Dairy Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Hyunjin Cho
- Division of Animal and Dairy Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Sinyong Jeong
- Division of Animal and Dairy Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Seoyoung Jeon
- Division of Animal and Dairy Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Mingyung Lee
- Division of Animal and Dairy Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Seul Lee
- Division of Animal and Dairy Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Yulchang Baek
- National Institute of Animal Science, Kongjwipatjwi-ro, Iseo-myeon, Wanju-Gun, Jeollabuk-do, Republic of Korea
| | - Joonpyo Oh
- Cargill Animal Nutrition Korea, Seongnam, Republic of Korea
| | - Seongwon Seo
- Division of Animal and Dairy Sciences, Chungnam National University, Daejeon, Republic of Korea
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Wang Y, Cengiz K. Implementation of the Spark technique in a matrix distributed computing algorithm. JOURNAL OF INTELLIGENT SYSTEMS 2022. [DOI: 10.1515/jisys-2022-0051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Two analyzes of Spark engine performance strategies to implement the Spark technique in a matrix distributed computational algorithm, the multiplication of a sparse multiplication operational test model. The dimensions of the two input sparse matrices have been fixed to 30,000 × 30,000, and the density of the input matrix have been changed. The experimental results show that when the density reaches about 0.3, the original dense matrix multiplication performance can outperform the sparse-sparse matrix multiplication, which is basically consistent with the relationship between the sparse matrix multiplication implementation in the single-machine sparse matrix test and the computational performance of the local native library. When the density of the fixed sparse matrix is 0.01, the distributed density-sparse matrix multiplication outperforms the same sparsity but uses the density matrix storage, and the acceleration ratio increases from 1.88× to 5.71× with the increase in dimension. The overall performance of distributed operations is improved.
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Affiliation(s)
- Ying Wang
- Department of Information Engineering, Tianjin Maritime College , Tianjin , 300350 , China
| | - Korhan Cengiz
- College of Information Technology, University of Fujairah , Fujairah , United Arab Emirates
- Department of Electrical – Electronics Engineering, Trakya University , 22030 , Edirne , Turkey
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Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients. SENSORS 2022; 22:s22093318. [PMID: 35591007 PMCID: PMC9105312 DOI: 10.3390/s22093318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 01/15/2023]
Abstract
Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.
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Charlton PH, Kyriacou PA, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:355-381. [PMID: 35356509 PMCID: PMC7612541 DOI: 10.1109/jproc.2022.3149785] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 05/29/2023]
Abstract
Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.
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Affiliation(s)
- Peter H. Charlton
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Panicos A. Kyriacou
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
| | - Jonathan Mant
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Vaidotas Marozas
- Department of Electronics Engineering and the Biomedical Engineering Institute, Kaunas University of Technology44249KaunasLithuania
| | - Phil Chowienczyk
- Department of Clinical PharmacologyKing’s College LondonLondonSE1 7EHU.K.
| | - Jordi Alastruey
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
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36
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Park J, Seok HS, Kim SS, Shin H. Photoplethysmogram Analysis and Applications: An Integrative Review. Front Physiol 2022; 12:808451. [PMID: 35300400 PMCID: PMC8920970 DOI: 10.3389/fphys.2021.808451] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/21/2021] [Indexed: 12/03/2022] Open
Abstract
Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
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Affiliation(s)
- Junyung Park
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hyeon Seok Seok
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Sang-Su Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hangsik Shin
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
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Pinheiro GPM, Miranda RK, Praciano BJG, Santos GA, Mendonça FLL, Javidi E, da Costa JPJ, de Sousa RT. Multi-Sensor Wearable Health Device Framework for Real-Time Monitoring of Elderly Patients Using a Mobile Application and High-Resolution Parameter Estimation. Front Hum Neurosci 2022; 15:750591. [PMID: 35111004 PMCID: PMC8802457 DOI: 10.3389/fnhum.2021.750591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health devices (MWHD), high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate (HR), and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography (PPG) sensor.
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Affiliation(s)
- Gabriel P. M. Pinheiro
- Department of Mechanical Engineering, University of Brasília, Brasília, Brazil
- *Correspondence: Gabriel P. M. Pinheiro
| | - Ricardo K. Miranda
- Department of Mechanical Engineering, University of Brasília, Brasília, Brazil
| | | | - Giovanni A. Santos
- Department of Electrical Engineering, University of Brasília, Brasília, Brazil
| | | | - Elnaz Javidi
- Department of Mechanical Engineering, University of Brasília, Brasília, Brazil
| | - João Paulo Javidi da Costa
- Department of Mechanical Engineering, University of Brasília, Brasília, Brazil
- Department of Electrical Engineering, University of Brasília, Brasília, Brazil
- Department 2-Campus Lippstadt, Hamm-Lippstadt University of Applied Sciences, Hamm, Germany
| | - Rafael T. de Sousa
- Department of Electrical Engineering, University of Brasília, Brasília, Brazil
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Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals. ENERGIES 2022. [DOI: 10.3390/en15020480] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.
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Wójcikowski M. Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices. SENSORS 2021; 22:s22010164. [PMID: 35009705 PMCID: PMC8749621 DOI: 10.3390/s22010164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/17/2021] [Accepted: 12/23/2021] [Indexed: 11/29/2022]
Abstract
This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.
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Affiliation(s)
- Marek Wójcikowski
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdansk, Poland
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40
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Manullang MCT, Lin YH, Lai SJ, Chou NK. Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:7777. [PMID: 34883780 PMCID: PMC8659982 DOI: 10.3390/s21237777] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/06/2021] [Accepted: 11/19/2021] [Indexed: 01/03/2023]
Abstract
Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.
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Affiliation(s)
- Martin Clinton Tosima Manullang
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan; (M.C.T.M.); (S.-J.L.)
- Department of Informatics, Institut Teknologi Sumatera, South Lampung Regency 35365, Indonesia
| | - Yuan-Hsiang Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan; (M.C.T.M.); (S.-J.L.)
| | - Sheng-Jie Lai
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan; (M.C.T.M.); (S.-J.L.)
| | - Nai-Kuan Chou
- Department of Cardiovascular Surgery, National Taiwan University Hospital, Taipei 10002, Taiwan
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41
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Kechris C, Delopoulos A. RMSSD Estimation From Photoplethysmography and Accelerometer Signals Using a Deep Convolutional Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:228-231. [PMID: 34891278 DOI: 10.1109/embc46164.2021.9629595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Heart Rate Variability is a significant indicator of the Autonomic Neural System's functioning, traditionally evaluated from electrocardiogram recordings. Photoplethysmography sensors, like electrocardiograph devices, track the heart's activity and have been widely popularized by their use in smart watches and fitness trackers. In this study we develop a deep learning based approach which is able to successfully estimate the patient's Root Mean Square of the Successive Differences, a common heart rate variability metric, from lower quality, less expensive photoplethysmography sensors under a wide range of conditions.
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42
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Zhong J, Hai D, Cheng J, Jiao C, Gou S, Liu Y, Zhou H, Zhu W. Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2021; 21:7163. [PMID: 34770469 PMCID: PMC8587957 DOI: 10.3390/s21217163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/05/2021] [Accepted: 10/25/2021] [Indexed: 01/16/2023]
Abstract
Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.
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Affiliation(s)
- Jun Zhong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (J.Z.); (D.H.); (H.Z.); (W.Z.)
- Suzhou Institute of Biomedical Engineering and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Dong Hai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (J.Z.); (D.H.); (H.Z.); (W.Z.)
| | - Jiaxin Cheng
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi’an 710071, China; (J.C.); (C.J.); (S.G.)
| | - Changzhe Jiao
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi’an 710071, China; (J.C.); (C.J.); (S.G.)
| | - Shuiping Gou
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi’an 710071, China; (J.C.); (C.J.); (S.G.)
| | - Yongfeng Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (J.Z.); (D.H.); (H.Z.); (W.Z.)
| | - Hong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (J.Z.); (D.H.); (H.Z.); (W.Z.)
| | - Wenliang Zhu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (J.Z.); (D.H.); (H.Z.); (W.Z.)
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Neshitov A, Tyapochkin K, Smorodnikova E, Pravdin P. Wavelet Analysis and Self-Similarity of Photoplethysmography Signals for HRV Estimation and Quality Assessment. SENSORS (BASEL, SWITZERLAND) 2021; 21:6798. [PMID: 34696011 PMCID: PMC8538953 DOI: 10.3390/s21206798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/01/2021] [Accepted: 10/06/2021] [Indexed: 11/20/2022]
Abstract
Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person's movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals' self-similarity to identify corrupted parts. We tested the algorithm on three different datasets: a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets: TROIKAand PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.
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Affiliation(s)
- Alexander Neshitov
- Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA 94063, USA; (E.S.); (P.P.)
| | - Konstantin Tyapochkin
- Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA 94063, USA; (E.S.); (P.P.)
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Sheridan DC, Baker S, Dehart R, Lin A, Hansen M, Tereshchenko LG, Le N, Newgard CD, Nagel B. Heart Rate Variability and Its Ability to Detect Worsening Suicidality in Adolescents: A Pilot Trial of Wearable Technology. Psychiatry Investig 2021; 18:928-935. [PMID: 34555890 PMCID: PMC8542751 DOI: 10.30773/pi.2021.0057] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/27/2021] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE Suicide is the 2nd leading cause of death in adolescence, and acute pediatric mental health emergency department (ED) visits have doubled in the past decade. The objective of this study was to evaluate physiologic parameters relationship to suicide severity. METHODS This was a prospective, observational study from April 2018 thru November 2019 in a tertiary care pediatric emergency department (ED) and inpatient pediatric psychiatric unit enrolling acutely suicidal adolescent patients. Patients wore a wrist device that used photoplethysmography for 7 days during their acute hospitalization to measure heart rate variability (HRV). During that time, Columbia Suicide Severity Scores (CSSRS) were assessed at 3 time points. RESULTS There was complete device data and follow-up for 51 patients. There was an increase in the high frequency (HF) component of HRV in patients that had a 25% or greater decrease in their CSSRS (mean difference 11.89 ms/ Hz ; p-value 0.005). Patients with a CSSRS≥15 on day of enrollment had a lower, although not statistically significant, HF component (mean difference -8.34 ms/ Hz; p-value 0.071). CONCLUSION We found an inverse correlation between parasympathetic activity measured through the HF component and suicidality in an acutely suicidal population of adolescents. Wearable technology may have the ability to improve outpatient monitoring for earlier detection and intervention.
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Affiliation(s)
- David C Sheridan
- Department of Emergency Medicine, Oregon Health & Science University, Oregon, USA.,Center of Policy and Research in Emergency Medicine, Oregon Health & Science University, Oregon, USA
| | - Steven Baker
- Department of Emergency Medicine, Oregon Health & Science University, Oregon, USA.,Alpha Bravo Connectivity, LLC, Oregon, USA
| | - Ryan Dehart
- Department of Emergency Medicine, Oregon Health & Science University, Oregon, USA
| | - Amber Lin
- Department of Emergency Medicine, Oregon Health & Science University, Oregon, USA.,Center of Policy and Research in Emergency Medicine, Oregon Health & Science University, Oregon, USA
| | - Matthew Hansen
- Department of Emergency Medicine, Oregon Health & Science University, Oregon, USA.,Center of Policy and Research in Emergency Medicine, Oregon Health & Science University, Oregon, USA
| | - Larisa G Tereshchenko
- Department of Medicine, Division of Cardiology, Oregon Health & Science University, Oregon, USA
| | - Nancy Le
- Department of Emergency Medicine, Oregon Health & Science University, Oregon, USA.,Center of Policy and Research in Emergency Medicine, Oregon Health & Science University, Oregon, USA
| | - Craig D Newgard
- Department of Emergency Medicine, Oregon Health & Science University, Oregon, USA.,Center of Policy and Research in Emergency Medicine, Oregon Health & Science University, Oregon, USA
| | - Bonnie Nagel
- Department of Psychiatry, Oregon Health & Science University, Oregon, USA.,Department of Behavioral Neuroscience, Oregon Health & Science University, Oregon, USA
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Glasstetter M, Böttcher S, Zabler N, Epitashvili N, Dümpelmann M, Richardson MP, Schulze-Bonhage A. Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor. SENSORS (BASEL, SWITZERLAND) 2021; 21:6017. [PMID: 34577222 PMCID: PMC8470979 DOI: 10.3390/s21186017] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 11/16/2022]
Abstract
Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.
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Affiliation(s)
- Martin Glasstetter
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Nicolas Zabler
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Nino Epitashvili
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Mark P. Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience King’s College London, London SE5 9RT, UK;
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
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McKnight JC, Mulder E, Ruesch A, Kainerstorfer JM, Wu J, Hakimi N, Balfour S, Bronkhorst M, Horschig JM, Pernett F, Sato K, Hastie GD, Tyack P, Schagatay E. When the human brain goes diving: using near-infrared spectroscopy to measure cerebral and systemic cardiovascular responses to deep, breath-hold diving in elite freedivers. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200349. [PMID: 34176327 DOI: 10.1098/rstb.2020.0349] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Continuous measurements of haemodynamic and oxygenation changes in free living animals remain elusive. However, developments in biomedical technologies may help to fill this knowledge gap. One such technology is continuous-wave near-infrared spectroscopy (CW-NIRS)-a wearable and non-invasive optical technology. Here, we develop a marinized CW-NIRS system and deploy it on elite competition freedivers to test its capacity to function during deep freediving to 107 m depth. We use the oxyhaemoglobin and deoxyhaemoglobin concentration changes measured with CW-NIRS to monitor cerebral haemodynamic changes and oxygenation, arterial saturation and heart rate. Furthermore, using concentration changes in oxyhaemoglobin engendered by cardiac pulsation, we demonstrate the ability to conduct additional feature exploration of cardiac-dependent haemodynamic changes. Freedivers showed cerebral haemodynamic changes characteristic of apnoeic diving, while some divers also showed considerable elevations in venous blood volumes close to the end of diving. Some freedivers also showed pronounced arterial deoxygenation, the most extreme of which resulted in an arterial saturation of 25%. Freedivers also displayed heart rate changes that were comparable to diving mammals both in magnitude and patterns of change. Finally, changes in cardiac waveform associated with heart rates less than 40 bpm were associated with changes indicative of a reduction in vascular compliance. The success here of CW-NIRS to non-invasively measure a suite of physiological phenomenon in a deep-diving mammal highlights its efficacy as a future physiological monitoring tool for human freedivers as well as free living animals. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.
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Affiliation(s)
- J Chris McKnight
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, St Andrews, UK.,Department of Health Sciences, Mid Sweden University, Östersund, Sweden
| | - Eric Mulder
- Department of Health Sciences, Mid Sweden University, Östersund, Sweden
| | - Alexander Ruesch
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Jana M Kainerstorfer
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.,Neuroscience Institute, Carnegie Mellon University, 4400 Forbes Ave., Pittsburgh, PA 15213, USA
| | - Jingyi Wu
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Naser Hakimi
- Artinis Medical Systems BV, Einsteinweg 17, 6662 PW Elst, The Netherlands
| | - Steve Balfour
- Sea Mammal Research Unit Instrumentation Group, Scottish Oceans Institute, University of St Andrews, St Andrews, UK
| | - Mathijs Bronkhorst
- Artinis Medical Systems BV, Einsteinweg 17, 6662 PW Elst, The Netherlands
| | - Jörn M Horschig
- Artinis Medical Systems BV, Einsteinweg 17, 6662 PW Elst, The Netherlands
| | - Frank Pernett
- Department of Health Sciences, Mid Sweden University, Östersund, Sweden
| | - Katsufumi Sato
- Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8564, Japan
| | - Gordon D Hastie
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, St Andrews, UK
| | - Peter Tyack
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, St Andrews, UK
| | - Erika Schagatay
- Department of Health Sciences, Mid Sweden University, Östersund, Sweden.,Swedish Winter Sport Research Center (SWSRC), Mid Sweden University, Östersund, Sweden
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47
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Salinet J, Moura FSD, Romanelli R, Dos Santos PMN, Zamai M, Panerai RB, Duarte AM, Bor-Seng-Shu E, Salinet ASM. CAAos platform: an integrated platform for analysis of cerebral hemodynamics data. Physiol Meas 2021; 42. [PMID: 34134102 DOI: 10.1088/1361-6579/ac0c0b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 06/16/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The purpose of this article is to introduce the readers to the concept and structure of CAAos (Cerebral Autoregulation Assessment Open Source) platform, and provide evidence of its functionality. APPROACH CAAos platform is a new open-source software research tool, developed in Python 3 language, that combines existing and novel methods for interactive visual inspection, batch processing and analysis of multichannel records. The platform is scalable, allowing for customization and inclusion of new tools. MAIN RESULTS Currently CAAos platform is composed of two main modules, preprocessing (containing artefact removal, filtering and signal beat to beat extraction tools) and cerebral autoregulation (CA) analysis modules. Two methods for assessing CA have been implemented into CAAos platform: transfer function analysis (TFA) and autoregulation index (ARI). In order to provide validation of TFA and ARI estimates derived from CAAos platform, the results were compared with those derived from two other algorithms. Validation was performed using data from twenty-eight participants, corresponding to 13 acute ischemic stroke patients and 13 age- and sex-matched control subjects. Agreement between estimates was assessed by intraclass correlation coefficient and Bland-Altman analysis. No significant statistical difference between algorithms was found. Moreover, there was an excellent correspondence between the curves of all parameters analysed, with intraclass correlation coefficient ranging from 0.98 (95%CI 0.976-0.999) to 1.00 (95%CI 1 -1). The mean differences revealed a very small magnitude bias indicating an excellent agreement between the estimates. SIGNIFICANCE As open-source software, the source code for the software is freely available for non-commercial use, reducing barriers to performing CA analysis, allowing inspection of the inner-workings of the algorithms, and facilitating networked activities with common standards. CAAos platform is a tailored software solution for the scientific community in the cerebral hemodynamic field and contributes to increasing use and reproducibility of CA assessment.
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Affiliation(s)
- João Salinet
- Federal University of the ABC Engineering Modeling and Applied Social Sciences Center Sao Bernardo do Campo, Sao Bernardo do Campo, São Paulo, BRAZIL
| | - Fernando Silva de Moura
- Biomedical Engineering, Engineering, Modelling and Applied Social Sciences Centre, Federal University of the ABC Engineering Modeling and Applied Social Sciences Center Sao Bernardo do Campo, Sao Bernardo do Campo, São Paulo, BRAZIL
| | - Renata Romanelli
- Biomedical Engineering, Engineering, Modelling and Applied Social Sciences Centre, Federal University of the ABC Engineering Modeling and Applied Social Sciences Center Sao Bernardo do Campo, Sao Bernardo do Campo, São Paulo, BRAZIL
| | - Pedro Machado Nery Dos Santos
- Biomedical Engineering, Engineering, Modelling and Applied Social Sciences Centre, Federal University of the ABC Engineering Modeling and Applied Social Sciences Center Sao Bernardo do Campo, Sao Bernardo do Campo, São Paulo, BRAZIL
| | - Matheus Zamai
- Federal University of the ABC Engineering Modeling and Applied Social Sciences Center Sao Bernardo do Campo, Sao Bernardo do Campo, São Paulo, BRAZIL
| | - Ronney B Panerai
- Department of Medical Physics and Clinical Engineering, Leicester Royal Infirmary, Infirmary Square, LEICESTER, LE1 5WW, Leicester, Leicestershire, LE2 7LX, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Andre M Duarte
- Biomedical Engineering, Engineering, Modelling and Applied Social Sciences Centre, Federal University of the ABC Engineering Modeling and Applied Social Sciences Center Sao Bernardo do Campo, Sao Bernardo do Campo, São Paulo, BRAZIL
| | - Edson Bor-Seng-Shu
- Neurology, University of Sao Paulo Hospital of Clinics, Sao Paulo, São Paulo, BRAZIL
| | - Angela Salomao Macedo Salinet
- Biomedical Engineering, Engineering, Modelling and Applied Social Sciences Centre, Federal University of the ABC Engineering Modeling and Applied Social Sciences Center Sao Bernardo do Campo, Sao Bernardo do Campo, São Paulo, BRAZIL
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48
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Ren P, Elyasi F, Manduchi R. Smartphone-Based Inertial Odometry for Blind Walkers. SENSORS (BASEL, SWITZERLAND) 2021; 21:4033. [PMID: 34208112 PMCID: PMC8230905 DOI: 10.3390/s21124033] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/07/2021] [Accepted: 06/09/2021] [Indexed: 11/24/2022]
Abstract
Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.
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Affiliation(s)
- Peng Ren
- Computer Science and Engineering, UC Santa Cruz, Santa Cruz, CA 95064, USA; (F.E.); (R.M.)
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49
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Akbari G, Nikkhoo M, Wang L, Chen CPC, Han DS, Lin YH, Chen HB, Cheng CH. Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach. SENSORS 2021; 21:s21124017. [PMID: 34200838 PMCID: PMC8230520 DOI: 10.3390/s21124017] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 11/30/2022]
Abstract
Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.
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Affiliation(s)
- Ghasem Akbari
- Department of Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin 341851416, Iran;
| | - Mohammad Nikkhoo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran;
- Bone and Joint Research Center, Chang Gung Memorial Hospital, Taoyuan 33333, Taiwan
| | - Lizhen Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China;
| | - Carl P. C. Chen
- Department of Physical Medicine & Rehabilitation, Chang Gung Memorial Hospital at Linkou and College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
| | - Der-Sheng Han
- Department of Physical Medicine and Rehabilitation, Bei-Hu Branch, National Taiwan University Hospital, Taipei 10845, Taiwan;
| | - Yang-Hua Lin
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; (Y.-H.L.); (H.-B.C.)
| | - Hung-Bin Chen
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; (Y.-H.L.); (H.-B.C.)
| | - Chih-Hsiu Cheng
- Bone and Joint Research Center, Chang Gung Memorial Hospital, Taoyuan 33333, Taiwan
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; (Y.-H.L.); (H.-B.C.)
- Correspondence: ; Tel.: +886-3211-8800-3714
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50
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Cabeleira M, Fedriga M, Smielewski P. Automatic Pulse Classification for Artefact Removal Using SAX Strings, a CENTER-TBI Study. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 131:231-234. [PMID: 33839850 DOI: 10.1007/978-3-030-59436-7_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
High-resolution, waveform-level data from bedside monitors carry important information about a patient's physiology but is also polluted with artefactual data. Manual mark-up is the standard practice for detecting and eliminating artefacts, but it is time-consuming, prone to errors, biased and not suitable for real-time processing.In this paper we present a novel automatic artefact detection technique based on a Symbolic Aggregate approXimation (SAX) technique which makes it possible to represent individual pulses as 'words'. It does that by coding each pulse with a specified number of letters (here six) from a predefined alphabet of characters (here six). The word is then fed to a support vector machine (SVM) and classified as artefactual or physiological.To define the universe of acceptable pulses, the arterial blood pressure from 50 patients was analysed, and acceptable pulses were manually chosen by looking at the average pulse that each 'word' generated. This was then used to train a SVM classifier. To test this algorithm, a dataset with a balanced ratio of clean and artefactual pulses was built, classified and independently evaluated by two observers achieving a sensitivity of 0.972 and 0.954 and a specificity of 0.837 and 0.837 respectively.
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Affiliation(s)
- Manuel Cabeleira
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK. .,Neurosurgery Unit, Addenbrooke's Hospital, Cambridge, UK.
| | - Marta Fedriga
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.,Neurosurgery Unit, Addenbrooke's Hospital, Cambridge, UK.,Department of Anesthesia, Critical Care and Emergency, Spedali Civili University Hospital, Brescia, Italy
| | - Peter Smielewski
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.,Neurosurgery Unit, Addenbrooke's Hospital, Cambridge, UK
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