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Abdulsadig RS, Devani N, Singh S, Patel Z, Pramono RXA, Mandal S, Rodriguez-Villegas E. Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device. J Clin Med 2024; 13:7199. [PMID: 39685655 DOI: 10.3390/jcm13237199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Revised: 11/15/2024] [Accepted: 11/23/2024] [Indexed: 12/18/2024] Open
Abstract
Objectives: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies. Methods: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study. Results: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively. Conclusions: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR.
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Affiliation(s)
- Rawan S Abdulsadig
- Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, UK
| | - Nikesh Devani
- Thoracic Medicine, Royal Free London NHS Foundation Trust, London NW3 2QG, UK
| | - Sukhpreet Singh
- Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, UK
| | - Zaibaa Patel
- Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, UK
| | - Renard Xaviero Adhi Pramono
- Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, UK
| | - Swapna Mandal
- Thoracic Medicine, Royal Free London NHS Foundation Trust, London NW3 2QG, UK
| | - Esther Rodriguez-Villegas
- Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, UK
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Duverger JE, Bellemin V, Forcier P, Decaens J, Gagnon G, Saidi A. A Quantitative Method to Guide the Integration of Textile Inductive Electrodes in Automotive Applications for Respiratory Monitoring. SENSORS (BASEL, SWITZERLAND) 2024; 24:7483. [PMID: 39686021 DOI: 10.3390/s24237483] [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: 10/30/2024] [Revised: 11/15/2024] [Accepted: 11/21/2024] [Indexed: 12/18/2024]
Abstract
Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver's alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject's waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement.
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Affiliation(s)
- James Elber Duverger
- Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail, Montréal, QC H3A 3C2, Canada
| | - Victor Bellemin
- Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, QC H3C 1K3, Canada
| | | | | | - Ghyslain Gagnon
- Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, QC H3C 1K3, Canada
| | - Alireza Saidi
- Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail, Montréal, QC H3A 3C2, Canada
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3
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Jiang Z, Bakker OJ, Bartolo PJ. Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions. SENSORS (BASEL, SWITZERLAND) 2024; 24:5734. [PMID: 39275645 PMCID: PMC11398138 DOI: 10.3390/s24175734] [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: 08/08/2024] [Revised: 08/29/2024] [Accepted: 09/01/2024] [Indexed: 09/16/2024]
Abstract
Chronic obstructive pulmonary disease (COPD) is among prevalent occupational diseases, causing early retirement and disabilities. This paper looks into occupational-related COPD prevention and intervention in the workplace for Industry 4.0-compliant occupation health and safety management. The economic burden and other severe problems caused by COPD are introduced. Subsequently, seminal research in relevant areas is reviewed. The prospects and challenges are introduced and discussed based on critical management approaches. An initial design of an Industry 4.0-compliant occupational COPD prevention system is presented at the end.
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Affiliation(s)
- Zhihao Jiang
- Faculty of Science & Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Otto Jan Bakker
- Faculty of Science & Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Paulo Jds Bartolo
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
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4
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Stevens G, Larmuseau M, Damme AV, Vanoverschelde H, Heerman J, Verdonck P. Feasibility study of the use of a wearable vital sign patch in an intensive care unit setting. J Clin Monit Comput 2024:10.1007/s10877-024-01207-5. [PMID: 39158782 DOI: 10.1007/s10877-024-01207-5] [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: 05/03/2024] [Accepted: 08/05/2024] [Indexed: 08/20/2024]
Abstract
Multiple studies and review papers have concluded that early warning systems have a positive effect on clinical outcomes, patient safety and clinical performances. Despite the substantial evidence affirming the efficacy of EWS applications, persistent barriers hinder their seamless integration into clinical practice. Notably, EWS, such as the National Early Warning Score, simplify multifaceted clinical conditions into singular numerical indices, thereby risking the oversight of critical clinical indicators and nuanced fluctuations in patients' health status. Furthermore, the optimal deployment of EWS within clinical contexts remains elusive. Manual assessment of EWS parameters exacts a significant temporal toll on healthcare personnel. Addressing these impediments necessitates innovative approaches. In this regard, wearable medical technologies emerge as promising solutions capable of continual monitoring of hospitalized patients' vital signs. To overcome the barriers of the use of early warning scores, wearable medical technology has the potential to continuously monitor vital signs of hospitalised patients. However, a fundamental inquiry arises regarding the comparability of their reliability to the current used golden standards. This inquiry underscores the imperative for rigorous evaluation and validation of wearable medical technologies to ascertain their efficacy in augmenting extant clinical practices. This prospective, single-center study aimed to evaluate the accuracy of heart rate and respiratory rate measurements obtained from the Vivalink Cardiac patch in comparison to the ECG-based monitoring system utilized at AZ Maria Middelares Hospital in Ghent. Specifically, the study focused on assessing the concordance between the data obtained from the Vivalink Cardiac patch and the established ECG-based monitoring system among a cohort of ten post-surgical intensive care unit (ICU) patients. Of these patients, five were undergoing mechanical ventilation post-surgery, while the remaining five were not. The study proceeded by initially comparing the data recorded by the Vivalink Cardiac patch with that of the ECG-based monitoring system. Subsequently, the data obtained from both the Vivalink Cardiac patch and the ECG-based monitoring system were juxtaposed with the information derived from the ventilation machine, thereby providing a comprehensive analysis of the patch's performance in monitoring vital signs within the ICU setting. For heart rate, the Vivalink Cardiac patch was on average within a 5% error range of the ECG-based monitoring system during 85.11±10.81% of the measured time. For respiratory rate this was during 40.55±17.28% of the measured time. Spearman's correlation coefficient showed a very high correlation of ρ = 0.9 8 for heart rate and a moderate correlation of ρ = 0.66 for respiratory rate. In comparison with the ventilated respiratory rate (ventilation machine) the Vivalink and ECG-based monitoring system both had a moderate correlation of ρ = 0.68 . A very high correlation was found between the heart rate measured by the Vivalink Cardiac patch and that of the ECG-based monitoring system of the hospital. Concerning respiratory rate the correlation between the data from the Vivalink Cardiac patch, the ECG-based monitoring system and the ventilation machine was found to be moderate.
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Affiliation(s)
- Guylian Stevens
- Departement of Electronics and Information Systems - IBiTech, Ghent University, Korneel Heymanslaan, Gent, 9000, East-Flanders, Belgium.
- H3CareSolutions, Henegouwenstraat 41, Gent, 9000, East-Flanders, Belgium.
| | - Michiel Larmuseau
- Partnership of Anesthesia, AZ Maria Middelares Hospital, Buitenring Sint-Denijs 30, Gent, 9000, East-Flanders, Belgium
| | - Annelies Van Damme
- Partnership of Anesthesia, AZ Maria Middelares Hospital, Buitenring Sint-Denijs 30, Gent, 9000, East-Flanders, Belgium
| | - Henk Vanoverschelde
- Partnership of Anesthesia, AZ Maria Middelares Hospital, Buitenring Sint-Denijs 30, Gent, 9000, East-Flanders, Belgium
| | - Jan Heerman
- Partnership of Anesthesia, AZ Maria Middelares Hospital, Buitenring Sint-Denijs 30, Gent, 9000, East-Flanders, Belgium
| | - Pascal Verdonck
- Departement of Electronics and Information Systems - IBiTech, Ghent University, Korneel Heymanslaan, Gent, 9000, East-Flanders, Belgium
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5
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Contreras-Briceño F, Cancino J, Espinosa-Ramírez M, Fernández G, Johnson V, Hurtado DE. Estimation of ventilatory thresholds during exercise using respiratory wearable sensors. NPJ Digit Med 2024; 7:198. [PMID: 39060511 PMCID: PMC11282229 DOI: 10.1038/s41746-024-01191-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
Ventilatory thresholds (VTs) are key physiological parameters used to evaluate physical performance and determine aerobic and anaerobic transitions during exercise. Current assessment of these parameters requires ergospirometry, limiting evaluation to laboratory or clinical settings. In this work, we introduce a wearable respiratory system that continuously tracks breathing during exercise and estimates VTs during ramp tests. We validate the respiratory rate and VTs predictions in 17 healthy adults using ergospirometry analysis. In addition, we use the wearable system to evaluate VTs in 107 recreational athletes during ramp tests outside the laboratory and show that the mean population values agree with physiological variables traditionally used to exercise prescription. We envision that respiratory wearables can be useful in determining aerobic and anaerobic parameters with promising applications in health telemonitoring and human performance.
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Affiliation(s)
- Felipe Contreras-Briceño
- Laboratory of Exercise Physiology, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Jorge Cancino
- Laboratory of Exercise Physiology & Metabolism, Faculty of Medicine, Universidad Finis Terrae, Santiago, Chile
| | - Maximiliano Espinosa-Ramírez
- Laboratory of Exercise Physiology, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | | | - Daniel E Hurtado
- IC Innovations SpA, Santiago, Chile.
- Department of Structural and Geotechnical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine, and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Sato H, Nagano T, Izumi S, Yamada J, Hazama D, Katsurada N, Yamamoto M, Tachihara M, Nishimura Y, Kobayashi K. Prospective observational study of 2 wearable strain sensors for measuring the respiratory rate. Medicine (Baltimore) 2024; 103:e38818. [PMID: 39029069 PMCID: PMC11398755 DOI: 10.1097/md.0000000000038818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/13/2024] [Indexed: 07/21/2024] Open
Abstract
The respiratory rate is an important factor for assessing patient status and detecting changes in the severity of illness. Real-time determination of the respiratory rate will enable early responses to changes in the patient condition. Several methods of wearable devices have enabled remote respiratory rate monitoring. However, gaps persist in large-scale validation, patient-specific calibration, standardization and their usefulness in clinical practice has not been fully elucidated. The aim of this study was to evaluate the accuracy of 2 wearable stretch sensors, C-STRECH® which is used in clinical practice and a novel stretchable capacitor in measuring the respiratory rate. The respiratory rate of 20 healthy subjects was measured by a spirometer with the stretch sensor applied to 1 of 5 locations (umbilicus, lateral abdomen, epigastrium, lateral chest, or chest) of their body at rest while they were in a sitting or supine position before or after exercise. The sensors detected the largest amplitudes at the epigastrium and umbilicus compared to other sites of measurement for the sitting and supine positions, respectively. At rest, the respiratory rate of the sensors had an error of 0.06 to 2.39 breaths/minute, whereas after exercise, an error of 1.57 to 3.72 breaths/minute was observed compared to the spirometer. The sensors were able to detect the respiratory rate of healthy volunteers in the sitting and supine positions, but there was a need for improvement in detection after exercise.
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Affiliation(s)
- Hiroki Sato
- Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Tatsuya Nagano
- Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Shintaro Izumi
- Graduate School of System Informatics, Kobe University, Hyogo, Japan
| | - Jun Yamada
- Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Daisuke Hazama
- Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Naoko Katsurada
- Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Masatsugu Yamamoto
- Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Motoko Tachihara
- Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | | | - Kazuyuki Kobayashi
- Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
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7
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Innocenti L, Romano C, Greco G, Nuccio S, Bellini A, Mari F, Silvestri S, Schena E, Sacchetti M, Massaroni C, Nicolò A. Breathing Monitoring in Soccer: Part I-Validity of Commercial Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:4571. [PMID: 39065970 PMCID: PMC11280907 DOI: 10.3390/s24144571] [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: 06/06/2024] [Revised: 07/06/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024]
Abstract
Growing evidence suggests that respiratory frequency (fR) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor fR in soccer with unobtrusive devices. This study assessed the validity of three strain-based commercial wearable devices measuring fR during soccer-specific movements. On two separate visits to the soccer pitch, 15 players performed a 30 min validation protocol wearing either a ComfTech® (CT) vest or a BioharnessTM (BH) 3.0 strap and a Tyme WearTM (TW) vest. fR was extracted from the respiratory waveform of the three commercial devices with custom-made algorithms and compared with that recorded with a reference face mask. The fR time course of the commercial devices generally resembled that of the reference system. The mean absolute percentage error was, on average, 7.03% for CT, 8.65% for TW, and 14.60% for BH for the breath-by-breath comparison and 1.85% for CT, 3.27% for TW, and 7.30% for BH when comparison with the reference system was made in 30 s windows. Despite the challenging measurement scenario, our findings show that some of the currently available wearable sensors are indeed suitable to unobtrusively measure fR in soccer.
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Affiliation(s)
- Lorenzo Innocenti
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy; (L.I.); (G.G.); (S.N.); (A.B.); (F.M.); (M.S.); (A.N.)
| | - Chiara Romano
- Department of Engineering, Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy (S.S.); (E.S.)
| | - Giuseppe Greco
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy; (L.I.); (G.G.); (S.N.); (A.B.); (F.M.); (M.S.); (A.N.)
| | - Stefano Nuccio
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy; (L.I.); (G.G.); (S.N.); (A.B.); (F.M.); (M.S.); (A.N.)
| | - Alessio Bellini
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy; (L.I.); (G.G.); (S.N.); (A.B.); (F.M.); (M.S.); (A.N.)
| | - Federico Mari
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy; (L.I.); (G.G.); (S.N.); (A.B.); (F.M.); (M.S.); (A.N.)
| | - Sergio Silvestri
- Department of Engineering, Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy (S.S.); (E.S.)
| | - Emiliano Schena
- Department of Engineering, Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy (S.S.); (E.S.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Massimo Sacchetti
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy; (L.I.); (G.G.); (S.N.); (A.B.); (F.M.); (M.S.); (A.N.)
| | - Carlo Massaroni
- Department of Engineering, Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy (S.S.); (E.S.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Andrea Nicolò
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy; (L.I.); (G.G.); (S.N.); (A.B.); (F.M.); (M.S.); (A.N.)
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Hao Z, Wang Y, Li F, Ding G, Gao Y. mmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar. SENSORS (BASEL, SWITZERLAND) 2024; 24:4315. [PMID: 39001094 PMCID: PMC11243972 DOI: 10.3390/s24134315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/11/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024]
Abstract
Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.
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Affiliation(s)
- Zhanjun Hao
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
- Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, China
| | - Yue Wang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
| | - Fenfang Li
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
| | - Guozhen Ding
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
| | - Yifei Gao
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
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Kontaxis S, Kanellos F, Ntanis A, Kostikis N, Konitsiotis S, Rigas G. An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep. SENSORS (BASEL, SWITZERLAND) 2024; 24:4139. [PMID: 39000917 PMCID: PMC11244494 DOI: 10.3390/s24134139] [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: 04/26/2024] [Revised: 06/17/2024] [Accepted: 06/22/2024] [Indexed: 07/16/2024]
Abstract
This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.
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Affiliation(s)
| | - Foivos Kanellos
- PD Neurotechnology Ltd., 45500 Ioannina, Greece
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | | | | | - Spyridon Konitsiotis
- University Hospital of Ioannina and Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
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10
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Huang S, Jafari R, Mortazavi BJ. Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:330-338. [PMID: 38899025 PMCID: PMC11186651 DOI: 10.1109/ojemb.2024.3398444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/09/2024] [Accepted: 04/19/2024] [Indexed: 06/21/2024] Open
Abstract
Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. Methods: We proposed an end-to-end data preprocessing framework that adapts multiple pulsatile signal modalities and generates machine-learning-ready datasets agnostic to downstream medical tasks. Results: a dataset preprocessed by Pulse2AI improved systolic blood pressure estimation by 29.58%, from 11.41 to 8.03 mmHg in root-mean-square-error (RMSE) and its diastolic counterpart by 26.01%, from 7.93 to 5.87 mmHg in RMSE. For respiration rate (RR) estimation, Pulse2AI boosted performance by 19.69%, from 1.47 to 1.18 breaths per minute (BrPM) in mean-absolute-error (MAE). Conclusion: Pulse2AI turns pulsatile signals into machine learning (ML) ready datasets for arbitrary remote health monitoring tasks. We tested Pulse2AI on multiple pulsatile modalities and demonstrated its efficacy in two medical applications. This work bridges valuable assets in remote sensing and internet of medical things to ML-ready datasets for medical modeling.
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Affiliation(s)
- Sicong Huang
- Department of Computer Science and EngineeringTexas A&M UniversityCollege StationTX77840USA
| | - Roozbeh Jafari
- Lincoln LaboratoryMassachusetts Institute of TechnologyLexingtonMA02139USA
- Laboratory for Information and Decision Systems (LIDS)Massachusetts Institute of TechnologyCambridgeMA02139USA
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTX77843USA
- School of Engineering MedicineTexas A&M UniversityHoustonTX77843USA
| | - Bobak J. Mortazavi
- Department of Computer Science and EngineeringTexas A&M UniversityCollege StationTX77840USA
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11
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McErlean J, Malik J, Lin YT, Talmon R, Wu HT. Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration. Physiol Meas 2024; 45:035008. [PMID: 38350132 DOI: 10.1088/1361-6579/ad290b] [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: 06/01/2023] [Accepted: 02/13/2024] [Indexed: 02/15/2024]
Abstract
Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.
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Affiliation(s)
- Jacob McErlean
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
| | - John Malik
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
| | - Yu-Ting Lin
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ronen Talmon
- Faculty of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, Israel
| | - Hau-Tieng Wu
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
- Department of Statistical Science, Duke University, Durham, North Carolina, United States of America
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12
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Ceccarelli M, D'Onofrio M, Ambrogi V, Russo M. A numerical analysis of ventilation motion after chest surgery with a RESPIRholter device. Respir Med Case Rep 2024; 49:102005. [PMID: 38576859 PMCID: PMC10992684 DOI: 10.1016/j.rmcr.2024.102005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/09/2023] [Accepted: 03/06/2024] [Indexed: 04/06/2024] Open
Abstract
This case report presents a numerical evaluation of respiration in terms of biomechanical parameters of chest motion. This experimental evaluation is performed with RESPIRholter, a wearable device specifically developed to monitor the movement in the ribcage through the motion of the sixth rib whose characteristic motion is considered as representative of the motion of the thorax. Here we present test results acquired with a RESPIRholter device in a 6-h acquisition. These results characterize respiration biomechanics for diagnostic purposes in a chest surgery patient, highlighting the diagnostic utility of RESPIRholter in the identification of post-operation respiratory problem.
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Affiliation(s)
- Marco Ceccarelli
- Department of Industrial Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy
| | - Manuel D'Onofrio
- Department of Surgical Sciences, University of Rome Tor Vergata, 00133, Rome, Italy
| | - Vincenzo Ambrogi
- Department of Surgical Sciences, University of Rome Tor Vergata, 00133, Rome, Italy
| | - Matteo Russo
- Department of Industrial Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy
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13
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Poorzargar K, Pham C, Panesar D, Riazi S, Lee K, Parotto M, Chung F. Video plethysmography for contactless measurement of respiratory rate in surgical patients. J Clin Monit Comput 2024; 38:47-55. [PMID: 37698697 DOI: 10.1007/s10877-023-01064-8] [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: 03/03/2023] [Accepted: 07/24/2023] [Indexed: 09/13/2023]
Abstract
The accurate recording of respiratory rate (RR) without contact is important for patient care. The current methods for RR measurement such as capnography, pneumography, and plethysmography require patient contact, are cumbersome, or not accurate for widespread clinical use. Video Plethysmography (VPPG) is a novel automated technology that measures RR using a facial video without contact. The objective of our study was to determine whether VPPG can feasibly and accurately measure RR without contact in surgical patients at a clinical setting. After research ethics approval, 216 patients undergoing ambulatory surgery consented to the study. Patients had a 1.5 min video of their faces taken via an iPad preoperatively, which was analyzed using VPPG to obtain RR information. The RR prediction by VPPG was compared to 60-s manual counting of breathing by research assistants. We found that VPPG predicted RR with 88.8% accuracy and a bias of 1.40 ± 1.96 breaths per minute. A significant and high correlation (0.87) was observed between VPPG-predicted and manually recorded RR. These results did not change with the ethnicity of patients. The success rate of the VPPG technology was 99.1%. Contactless RR monitoring of surgical patients at a hospital setting using VPPG is accurate and feasible, making this technology an attractive alternative to the current approaches to RR monitoring. Future developments should focus on improving reliability of the technology.
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Affiliation(s)
- Khashayar Poorzargar
- Department of Anesthesia and Pain Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Chi Pham
- Department of Anesthesia and Pain Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Darshan Panesar
- Ontario Institute for Studies in Education, University of Toronto, Toronto, ON, Canada
| | - Sheila Riazi
- Department of Anesthesia and Pain Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Kang Lee
- Ontario Institute for Studies in Education, University of Toronto, Toronto, ON, Canada
| | - Matteo Parotto
- Department of Anesthesia and Pain Medicine, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Frances Chung
- Department of Anesthesia and Pain Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
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14
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Chin WJ, Kwan BH, Lim WY, Tee YK, Darmaraju S, Liu H, Goh CH. A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model. Diagnostics (Basel) 2024; 14:284. [PMID: 38337800 PMCID: PMC10855057 DOI: 10.3390/diagnostics14030284] [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: 11/30/2023] [Revised: 12/29/2023] [Accepted: 01/26/2024] [Indexed: 02/12/2024] Open
Abstract
Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train-test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study's model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.
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Affiliation(s)
- Wee Jian Chin
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
- Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
| | - Ban-Hoe Kwan
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
- Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
| | - Wei Yin Lim
- Electrical and Computer Systems Engineering, School of Engineering and Advanced Engineering Platform, Monash University Malaysia, Bandar Sunway 47500, Selangor, Malaysia;
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
- Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
| | - Shalini Darmaraju
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
| | - Haipeng Liu
- Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK;
| | - Choon-Hian Goh
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
- Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
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15
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Liu Z, Su J, Zhou K, Yu B, Lin Y, Li KH. Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring. NANO LETTERS 2023; 23:10674-10681. [PMID: 37712616 DOI: 10.1021/acs.nanolett.3c02071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Respiratory pattern is one of the most crucial indicators for accessing human health, but there has been limited success in implementing fast-responsive, affordable, and miniaturized platforms with the capability for smart recognition. Herein, a fully integrated and flexible patch for wireless intelligent respiratory monitoring based on a lamellar porous film functionalized GaN optoelectronic chip with a desirable response to relative humidity (RH) variation is reported. The submillimeter-sized GaN device exhibits a high sensitivity of 13.2 nA/%RH at 2-70%RH and 61.5 nA/%RH at 70-90%RH, and a fast response/recovery time of 12.5 s/6 s. With the integration of a wireless data transmission module and the assistance of machine learning based on 1-D convolutional neural networks, seven breathing patterns are identified with an overall classification accuracy of >96%. This integrated and flexible on-mask sensing platform successfully demonstrates real-time and intelligent respiratory monitoring capability, showing great promise for practical healthcare applications.
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Affiliation(s)
- Zecong Liu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, P.R. China
| | - Junjie Su
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, P.R. China
| | - Kemeng Zhou
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, P.R. China
| | - Binlu Yu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, P.R. China
| | - Yuanjing Lin
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, P.R. China
| | - Kwai Hei Li
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, P.R. China
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16
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Lorenz AL, Zhang S. Human Respiration Rate Measurement with High-Speed Digital Fringe Projection Technique. SENSORS (BASEL, SWITZERLAND) 2023; 23:9000. [PMID: 37960698 PMCID: PMC10648030 DOI: 10.3390/s23219000] [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: 10/03/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023]
Abstract
This paper proposes a non-contact continuous respiration monitoring method based on Fringe Projection Profilometry (FPP). This method aims to overcome the limitations of traditional intrusive techniques by providing continuous monitoring without interfering with normal breathing. The FPP sensor captures three-dimensional (3D) respiratory motion from the chest wall and abdomen, and the analysis algorithms extract respiratory parameters. The system achieved a high Signal-to-Noise Ratio (SNR) of 37 dB with an ideal sinusoidal respiration signal. Experimental results demonstrated that a mean correlation of 0.95 and a mean Root-Mean-Square Error (RMSE) of 0.11 breaths per minute (bpm) were achieved when comparing to a reference signal obtained from a spirometer.
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Affiliation(s)
- Anna Lena Lorenz
- Institute of Biomedical Engineering, Karlsruher Institute of Technology, 76131 Karlsruhe, Germany;
| | - Song Zhang
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
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17
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Xia F, Li H, Li Y, Liu X, Xu Y, Fang C, Hou Q, Lin S, Zhang Z, Yang J, Sawan M. Minimally Invasive Hypoglossal Nerve Stimulator Enabled by ECG Sensor and WPT to Manage Obstructive Sleep Apnea. SENSORS (BASEL, SWITZERLAND) 2023; 23:8882. [PMID: 37960581 PMCID: PMC10648123 DOI: 10.3390/s23218882] [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: 09/23/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023]
Abstract
A hypoglossal nerve stimulator (HGNS) is an invasive device that is used to treat obstructive sleep apnea (OSA) through electrical stimulation. The conventional implantable HGNS device consists of a stimuli generator, a breathing sensor, and electrodes connected to the hypoglossal nerve via leads. However, this implant is bulky and causes significant trauma. In this paper, we propose a minimally invasive HGNS based on an electrocardiogram (ECG) sensor and wireless power transfer (WPT), consisting of a wearable breathing monitor and an implantable stimulator. The breathing external monitor utilizes an ECG sensor to identify abnormal breathing patterns associated with OSA with 88.68% accuracy, achieved through the utilization of a convolutional neural network (CNN) algorithm. With a skin thickness of 5 mm and a receiving coil diameter of 9 mm, the power conversion efficiency was measured as 31.8%. The implantable device, on the other hand, is composed of a front-end CMOS power management module (PMM), a binary-phase-shift-keying (BPSK)-based data demodulator, and a bipolar biphasic current stimuli generator. The PMM, with a silicon area of 0.06 mm2 (excluding PADs), demonstrated a power conversion efficiency of 77.5% when operating at a receiving frequency of 2 MHz. Furthermore, it offers three-voltage options (1.2 V, 1.8 V, and 3.1 V). Within the data receiver component, a low-power BPSK demodulator was ingeniously incorporated, consuming only 42 μW when supplied with a voltage of 0.7 V. The performance was achieved through the implementation of the self-biased phase-locked-loop (PLL) technique. The stimuli generator delivers biphasic constant currents, providing a 5 bit programmable range spanning from 0 to 2.4 mA. The functionality of the proposed ECG- and WPT-based HGNS was validated, representing a highly promising solution for the effective management of OSA, all while minimizing the trauma and space requirements.
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Affiliation(s)
- Fen Xia
- Zhejiang University, Hangzhou 310024, China;
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Hanrui Li
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
- SAMA Labs, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Department of Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Yixi Li
- State Key Laboratory of Superlattices, Microstructures Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100045, China;
| | - Xing Liu
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Yankun Xu
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Chaoming Fang
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Qiming Hou
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Siyu Lin
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Zhao Zhang
- SAMA Labs, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Department of Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Jie Yang
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Mohamad Sawan
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
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18
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Xian X. Frontiers of Wearable Biosensors for Human Health Monitoring. BIOSENSORS 2023; 13:964. [PMID: 37998139 PMCID: PMC10669529 DOI: 10.3390/bios13110964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023]
Abstract
Wearable biosensors offer noninvasive, real-time, and continuous monitoring of diverse human health data, making them invaluable for remote patient tracking, early diagnosis, and personalized medicine [...].
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Affiliation(s)
- Xiaojun Xian
- The Department of Electrical Engineering and Computer Science, Jerome J. Lohr College of Engineering, South Dakota State University, Brookings, SD 57007, USA
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19
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Vaussenat F, Bhattacharya A, Payette J, Benavides-Guerrero JA, Perrotton A, Gerlein LF, Cloutier SG. Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study. JMIR BIOMEDICAL ENGINEERING 2023; 8:e47146. [PMID: 38875670 PMCID: PMC11041423 DOI: 10.2196/47146] [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: 03/10/2023] [Revised: 08/22/2023] [Accepted: 09/07/2023] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea. OBJECTIVE The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard. METHODS We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods. RESULTS The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89. CONCLUSIONS Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.
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Affiliation(s)
- Fabrice Vaussenat
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Abhiroop Bhattacharya
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Julie Payette
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | | | - Alexandre Perrotton
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Luis Felipe Gerlein
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Sylvain G Cloutier
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
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20
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Hwang CS, Kim YH, Hyun JK, Kim JH, Lee SR, Kim CM, Nam JW, Kim EY. Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit. Bioengineering (Basel) 2023; 10:1222. [PMID: 37892952 PMCID: PMC10604201 DOI: 10.3390/bioengineering10101222] [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: 08/21/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
The respiratory rate (RR) is a significant indicator to evaluate a patient's prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) to primarily monitor peripheral circulation while capturing indirect information about intrathoracic pressure changes. This study aims to apply and evaluate several deep learning models using a PPG for the continuous and accurate estimation of the RRs of patients. The dataset was collected twice for 2 min each in 100 patients aged 18 years and older from the surgical intensive care unit of a tertiary referral hospital. The BIDMC and CapnoBase public datasets were also analyzed. The collected dataset was preprocessed and split according to the 5-fold cross-validation. We used seven deep learning models, including our own Dilated Residual Neural Network, to check how accurately the RR estimates match the ground truth using the mean absolute error (MAE). As a result, when validated using the collected dataset, our model showed the best results with a 1.2628 ± 0.2697 MAE on BIDMC and RespNet and with a 3.1268 ± 0.6363 MAE on our dataset, respectively. In conclusion, RR estimation using PPG-derived models is still challenging and has many limitations. However, if there is an equal amount of data from various breathing groups to train, we expect that various models, including our Dilated ResNet model, which showed good results, can achieve better results than the current ones.
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Affiliation(s)
- Chi Shin Hwang
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Yong Hwan Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Jung Kyun Hyun
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Joon Hwang Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Seo Rak Lee
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Choong Min Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Jung Woo Nam
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Eun Young Kim
- Division of Trauma and Surgical Critical Care, Department of Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea
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21
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Duan X, Song X, Yang C, Li Y, Wei L, Gong Y, Li Y. Evaluation of three approaches used for respiratory measurement in healthy subjects. Physiol Meas 2023; 44:105004. [PMID: 37729923 DOI: 10.1088/1361-6579/acfbd7] [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: 03/24/2023] [Accepted: 09/20/2023] [Indexed: 09/22/2023]
Abstract
Objective. Respiration is one of the critical vital signs of human health status, and accurate respiratory monitoring has important clinical significance. There is substantial evidence that alterations in key respiratory parameters can be used to determine a patient's health status, aid in the selection of appropriate treatments, predict potentially serious clinical events and control respiratory activity. Although various approaches have been developed for respiration monitoring, no definitive conclusions have been drawn regarding the accuracy of these approaches because each has different advantages and limitations. In the present study, we evaluated the performance of three non-invasive respiratory measurement approaches, including transthoracic impedance (IMP), surface diaphragm electromyography-derived respiration (EMGDR) and electrocardiogram-derived respiration (ECGDR), and compared them with the direct measurement of airflow (FLW) in 33 male and 38 female healthy subjects in the resting state.Approach. The accuracy of six key respiratory parameters, including onset of inspiration (Ion), onset of expiration (Eon), inspiratory time (It), expiratory time (Et), respiratory rate (RR) and inspiratory-expiratory ratio (I:E), measured from the IMP, EMGDR and ECGDR, were compared with those annotated from the reference FLW.Main results. The correlation coefficients between the estimated inspiratory volume and reference value were 0.72 ± 0.20 for IMP, 0.62 ± 0.23 for EMGDR and 0.46 ± 0.21 for ECGDR (p< 0.01 among groups). The positive predictive value and sensitivity for respiration detection were 100% and 100%, respectively, for IMP, which were significantly higher than those of the EMGDR (97.2% and 95.5%,p< 0.001) and the ECGDR (96.9% and 90.0%,p< 0.001). Additionally, the mean error (ME) forIon,Eon,It,EtandRRdetection were markedly lower for IMP than for EMGDR and ECGDR (p< 0.001).Significance. Compared with EMGDR and ECGDR, the IMP signal had a higher positive predictive value, higher sensitivity and lower ME for respiratory parameter detection. This suggests that IMP is more suitable for dedicated respiratory monitoring and parameter evaluation.
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Affiliation(s)
- Xiaojuan Duan
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, People's Republic of China
| | - Xin Song
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, People's Republic of China
| | - Caidie Yang
- Department of Respiratory Medicine, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China
| | - Yunchi Li
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, People's Republic of China
| | - Liang Wei
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, People's Republic of China
| | - Yushun Gong
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, People's Republic of China
| | - Yongqin Li
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, People's Republic of China
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22
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Jung H, Kim D, Choi J, Joo EY. Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:7976. [PMID: 37766031 PMCID: PMC10536355 DOI: 10.3390/s23187976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/10/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023]
Abstract
Wrist-based respiratory rate (RR) measurement during sleep faces accuracy limitations. This study aimed to assess the accuracy of the RR estimation function during sleep based on the severity of obstructive sleep apnea (OSA) using the Samsung Galaxy Watch (GW) series. These watches are equipped with accelerometers and photoplethysmography sensors for RR estimation. A total of 195 participants visiting our sleep clinic underwent overnight polysomnography while wearing the GW, and the RR estimated by the GW was compared with the reference RR obtained from the nasal thermocouple. For all participants, the root mean squared error (RMSE) of the average overnight RR and continuous RR measurements were 1.13 bpm and 1.62 bpm, respectively, showing a small bias of 0.39 bpm and 0.37 bpm, respectively. The Bland-Altman plots indicated good agreement in the RR measurements for the normal, mild, and moderate OSA groups. In participants with normal-to-moderate OSA, both average overnight RR and continuous RR measurements achieved accuracy rates exceeding 90%. However, for patients with severe OSA, these accuracy rates decreased to 79.45% and 75.8%, respectively. The study demonstrates the GW's ability to accurately estimate RR during sleep, even though accuracy may be compromised in patients with severe OSA.
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Affiliation(s)
- Hyunjun Jung
- Samsung Electronics, Suwon 16677, Republic of Korea
| | - Dongyeop Kim
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul 07804, Republic of Korea
| | - Jongmin Choi
- Samsung Electronics, Suwon 16677, Republic of Korea
| | - Eun Yeon Joo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
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23
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Szankin M, Kwasniewska A, Ruminski J. Thermal Image Processing for Respiratory Estimation from Cubical Data with Expandable Depth. J Imaging 2023; 9:184. [PMID: 37754948 PMCID: PMC10532126 DOI: 10.3390/jimaging9090184] [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: 07/13/2023] [Revised: 08/24/2023] [Accepted: 09/07/2023] [Indexed: 09/28/2023] Open
Abstract
As healthcare costs continue to rise, finding affordable and non-invasive ways to monitor vital signs is increasingly important. One of the key metrics for assessing overall health and identifying potential issues early on is respiratory rate (RR). Most of the existing methods require multiple steps that consist of image and signal processing. This might be difficult to deploy on edge devices that often do not have specialized digital signal processors (DSP). Therefore, the goal of this study is to develop a single neural network realizing the entire process of RR estimation in a single forward pass. The proposed solution builds on recent advances in video recognition, capturing both spatial and temporal information in a multi-path network. Both paths process the data at different sampling rates to capture rapid and slow changes that are associated with differences in the temperature of the nostril area during the breathing episodes. The preliminary results show that the introduced end-to-end solution achieves better performance compared to state-of-the-art methods, without requiring additional pre/post-processing steps and signal-processing techniques. In addition, the presented results demonstrate its robustness on low-resolution thermal video sequences that are often used at the embedded edge due to the size and power constraints of such systems. Taking that into account, the proposed approach has the potential for efficient and convenient respiratory rate estimation across various markets in solutions deployed locally, close to end users.
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Affiliation(s)
- Maciej Szankin
- Intel Corporation, 16409 W Bernardo Dr Suite 100, San Diego, CA 92127, USA
| | | | - Jacek Ruminski
- Department of Biomedical Engineering, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80233 Gdansk, Poland;
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24
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Bachir W, Ismael FS, Alaineya NHA. Laser spectroscopic method for remote sensing of respiratory rate. Phys Eng Sci Med 2023; 46:1249-1258. [PMID: 37358781 PMCID: PMC10480269 DOI: 10.1007/s13246-023-01292-x] [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: 02/10/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023]
Abstract
Noncontact sensing methods for measuring vital signs have recently gained interest, particularly for long-term monitoring. This study introduces a new method for measuring respiratory rate remotely. The proposed method is based on the reflection of a laser beam off a striped card attached to a moving platform simulating chest wall displacements. A wide range of frequencies (n = 35) from 0.06 to 2.2 Hz corresponding to both normal and pathological human respiratory rates were simulated using a moving mechanical platform. Reflected spectra (n = 105) were collected by a spectrometer in a dynamic mode. Fourier analysis was performed to retrieve the breathing frequency. The results show a striking agreement between measurements and reference frequencies. The results also show that low frequencies corresponding to respiratory rates can be detected with high accuracy (uncertainty is well below 5%). A validation test of the measuring method on a human subject demonstrated a great potential for remote respiration rate monitoring of adults and neonates in a clinical environment.
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Affiliation(s)
- Wesam Bachir
- Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Św. A. Boboli 8 St, 02-525, Warsaw, Poland.
- Biomedical Photonics Laboratory, Higher Institute for Laser Research and Applications, Damascus University, Damascus, Syria.
| | - Fatimah Samie Ismael
- Biomedical Photonics Laboratory, Higher Institute for Laser Research and Applications, Damascus University, Damascus, Syria
| | - Nour Hasan Arry Alaineya
- Biomedical Photonics Laboratory, Higher Institute for Laser Research and Applications, Damascus University, Damascus, Syria
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25
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Liao S, Liu H, Lin WH, Zheng D, Chen F. Filtering-induced changes of pulse transmit time across different ages: a neglected concern in photoplethysmography-based cuffless blood pressure measurement. Front Physiol 2023; 14:1172150. [PMID: 37560157 PMCID: PMC10407099 DOI: 10.3389/fphys.2023.1172150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/12/2023] [Indexed: 08/11/2023] Open
Abstract
Background: Pulse transit time (PTT) is a key parameter in cuffless blood pressure measurement based on photoplethysmography (PPG) signals. In wearable PPG sensors, raw PPG signals are filtered, which can change the timing of PPG waveform feature points, leading to inaccurate PTT estimation. There is a lack of comprehensive investigation of filtering-induced PTT changes in subjects with different ages. Objective: This study aimed to quantitatively investigate the effects of aging and PTT definition on the infinite impulse response (IIR) filtering-induced PTT changes. Methods: One hundred healthy subjects in five different ranges of age (i.e., 20-29, 30-39, 40-49, 50-59, and over 60 years old, 20 subjects in each) were recruited. Electrocardiogram (ECG) and PPG signals were recorded simultaneously for 120 s. PTT was calculated from the R wave of ECG and PPG waveform features. Eight PTT definitions were developed from different PPG waveform feature points. The raw PPG signals were preprocessed then further low-pass filtered. The difference between PTTs derived from preprocessed and filtered PPG signals, and the relative difference, were calculated and compared among five age groups and eight PTT definitions using the analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. Linear regression analysis was used to investigate the relationship between age and filtering-induced PTT changes. Results: Filtering-induced PTT difference and the relative difference were significantly influenced by age and PTT definition (p < 0.001 for both). Aging effect on filtering-induced PTT changes was consecutive with a monotonous trend under all PTT definitions. The age groups with maximum and minimum filtering-induced PTT changes depended on the definition. In all subjects, the PTT defined by maximum peak of PPG had the minimum filtering-induced PTT changes (mean: 16.16 ms and 5.65% for PTT difference and relative difference). The changes of PTT defined by maximum first PPG derivative had the strongest linear relationship with age (R-squared: 0.47 and 0.46 for PTT difference relative difference). Conclusion: The filtering-induced PTT changes are significantly influenced by age and PTT definition. These factors deserve further consideration to improve the accuracy of PPG-based cuffless blood pressure measurement using wearable sensors.
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Affiliation(s)
- Shangdi Liao
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Wan-Hua Lin
- Chinese Academy of Sciences Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Fei Chen
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
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26
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Ariyanti W, Liu KC, Chen KY, Yu-Tsao. Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083782 DOI: 10.1109/embc40787.2023.10341036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.
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27
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Angelucci A, Aliverti A. An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions. Cardiovasc Eng Technol 2023; 14:351-363. [PMID: 36849621 PMCID: PMC9970135 DOI: 10.1007/s13239-023-00657-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 01/24/2023] [Indexed: 03/01/2023]
Abstract
PURPOSE Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions. METHODS This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate respiratory rate (RR) in static and dynamic conditions and perform human activity recognition (HAR) with the same sensing principle. Two units are aimed at detecting chest wall breathing-related movements (one on the thorax, one on the abdomen); the third is on the lower back. All units compute the quaternions describing the subject's movement and send data continuously with the ANT transmission protocol to an app. The 20 healthy subjects involved in the research (9 men, 11 women) were between 23 and 54 years old, with mean age 26.8, mean height 172.5 cm and mean weight 66.9 kg. Data from these subjects during different postures or activities were collected and analyzed to extract RR. RESULTS Statistically significant differences between dynamic activities ("walking slow", "walking fast", "running" and "cycling") and static postures were detected (p < 0.05), confirming the obtained measurements are in line with physiology even during dynamic activities. Data from the reference unit only and from all three units were used as inputs to artificial intelligence methods for HAR. When the data from the reference unit were used, the Gated Recurrent Unit was the best performing method (97% accuracy). With three units, a 1D Convolutional Neural Network was the best performing (99% accuracy). CONCLUSION Overall, the proposed solution shows it is possible to perform simultaneous HAR and RR measurements in static and dynamic conditions with the same sensor system.
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Affiliation(s)
- Alessandra Angelucci
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133, Milan, Italy.
| | - Andrea Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133, Milan, Italy
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28
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A deep-learning approach to assess respiratory effort with a chest-worn accelerometer during sleep. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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29
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Linschmann O, Uguz DU, Romanski B, Baarlink I, Gunaratne P, Leonhardt S, Walter M, Lueken M. A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver's Vital Signs. SENSORS (BASEL, SWITZERLAND) 2023; 23:4002. [PMID: 37112341 PMCID: PMC10144144 DOI: 10.3390/s23084002] [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: 02/28/2023] [Revised: 04/02/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
With higher levels of automation in vehicles, the need for robust driver monitoring systems increases, since it must be ensured that the driver can intervene at any moment. Drowsiness, stress and alcohol are still the main sources of driver distraction. However, physiological problems such as heart attacks and strokes also exhibit a significant risk for driver safety, especially with respect to the ageing population. In this paper, a portable cushion with four sensor units with multiple measurement modalities is presented. Capacitive electrocardiography, reflective photophlethysmography, magnetic induction measurement and seismocardiography are performed with the embedded sensors. The device can monitor the heart and respiratory rates of a vehicle driver. The promising results of the first proof-of-concept study with twenty participants in a driving simulator not only demonstrate the accuracy of the heart (above 70% of medical-grade heart rate estimations according to IEC 60601-2-27) and respiratory rate measurements (around 30% with errors below 2 BPM), but also that the cushion might be useful to monitor morphological changes in the capacitive electrocardiogram in some cases. The measurements can potentially be used to detect drowsiness and stress and thus the fitness of the driver, since heart rate variability and breathing rate variability can be captured. They are also useful for the early prediction of cardiovascular diseases, one of the main reasons for premature death. The data are publicly available in the UnoVis dataset.
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Affiliation(s)
- Onno Linschmann
- Medical Information Technology, Helmholtz Institute, RWTH Aachen University, 52074 Aachen, Germany
| | - Durmus Umutcan Uguz
- Medical Information Technology, Helmholtz Institute, RWTH Aachen University, 52074 Aachen, Germany
| | - Bianca Romanski
- Medical Information Technology, Helmholtz Institute, RWTH Aachen University, 52074 Aachen, Germany
| | - Immo Baarlink
- Medical Information Technology, Helmholtz Institute, RWTH Aachen University, 52074 Aachen, Germany
| | - Pujitha Gunaratne
- Toyota Collaborative Safety Research Center, Toyota Motors Corporation, Ann Arbor, MI 48105, USA
| | - Steffen Leonhardt
- Medical Information Technology, Helmholtz Institute, RWTH Aachen University, 52074 Aachen, Germany
| | - Marian Walter
- Medical Information Technology, Helmholtz Institute, RWTH Aachen University, 52074 Aachen, Germany
| | - Markus Lueken
- Medical Information Technology, Helmholtz Institute, RWTH Aachen University, 52074 Aachen, Germany
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30
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Lin YD, Tan YK, Ku T, Tian B. A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example. SENSORS (BASEL, SWITZERLAND) 2023; 23:3785. [PMID: 37112125 PMCID: PMC10145328 DOI: 10.3390/s23083785] [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: 02/10/2023] [Revised: 04/02/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.
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Affiliation(s)
- Yue-Der Lin
- Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
- Ph.D. Program of Electrical and Communications Engineering, Feng Chia University, Taichung 40724, Taiwan
| | - Yong-Kok Tan
- Ph.D. Program of Electrical and Communications Engineering, Feng Chia University, Taichung 40724, Taiwan
| | - Tienhsiung Ku
- Department of Anesthesiology, Changhua Christian Hospital, Changhua 50051, Taiwan
- Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua 50051, Taiwan
| | - Baofeng Tian
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
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31
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Liu H, Pan F, Lei X, Hui J, Gong R, Feng J, Zheng D. Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study. Front Physiol 2023; 14:1085871. [PMID: 37007991 PMCID: PMC10060556 DOI: 10.3389/fphys.2023.1085871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/24/2023] [Indexed: 03/18/2023] Open
Abstract
Background: Intracranial photoplethysmography (PPG) signals can be measured from extracranial sites using wearable sensors and may enable long-term non-invasive monitoring of intracranial pressure (ICP). However, it is still unknown if ICP changes can lead to waveform changes in intracranial PPG signals.Aim: To investigate the effect of ICP changes on the waveform of intracranial PPG signals of different cerebral perfusion territories.Methods: Based on lump-parameter Windkessel models, we developed a computational model consisting three interactive parts: cardiocerebral artery network, ICP model, and PPG model. We simulated ICP and PPG signals of three perfusion territories [anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA), all left side] in three ages (20, 40, and 60 years) and four intracranial capacitance conditions (normal, 20% decrease, 50% decrease, and 75% decrease). We calculated following PPG waveform features: maximum, minimum, mean, amplitude, min-to-max time, pulsatility index (PI), resistive index (RI), and max-to-mean ratio (MMR).Results: The simulated mean ICPs in normal condition were in the normal range (8.87–11.35 mm Hg), with larger PPG fluctuations in older subject and ACA/PCA territories. When intracranial capacitance decreased, the mean ICP increased above normal threshold (>20 mm Hg), with significant decreases in maximum, minimum, and mean; a minor decrease in amplitude; and no consistent change in min-to-max time, PI, RI, or MMR (maximal relative difference less than 2%) for PPG signals of all perfusion territories. There were significant effects of age and territory on all waveform features except age on mean.Conclusion: ICP values could significantly change the value-relevant (maximum, minimum, and amplitude) waveform features of PPG signals measured from different cerebral perfusion territories, with negligible effect on shape-relevant features (min-to-max time, PI, RI, and MMR). Age and measurement site could also significantly influence intracranial PPG waveform.
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Affiliation(s)
- Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Fan Pan
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Xinyue Lei
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Jiyuan Hui
- Brain Injury Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ru Gong
- Brain Injury Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Junfeng Feng
- Brain Injury Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Junfeng Feng, ; Dingchang Zheng,
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
- *Correspondence: Junfeng Feng, ; Dingchang Zheng,
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Vainer BG. Radial artery pulse wave velocity: a new characterization technique and the instabilities associated with the respiratory phase and breath-holding. Physiol Meas 2023; 44. [PMID: 36657177 DOI: 10.1088/1361-6579/acb4dd] [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: 06/28/2022] [Accepted: 01/19/2023] [Indexed: 01/20/2023]
Abstract
Objective. Pulse wave velocity (PWV) is a key diagnostic parameter of the cardiovascular system's state. However, approaches aimed at PWV characterization often suffer from inevitable drawbacks. Statistical results demonstrating how closely PWV in the radial artery (RA) and the respiration phase correlate, as well as RA PWV evolution during breath-holding (BH), have not yet been presented in the literature. The aims of this study are (a) to propose a simple robust technique for measuring RA PWV, (b) to reveal the phase relation between the RA PWV and spontaneous breathing, and (c) to disclose the influence of BH on the RA PWV.Approach.The high-resolution remote breathing monitoring method Sorption-Enhanced Infrared Thermography (SEIRT) and the new technique aimed at measuring RA PWV described in this paper were used synchronously, and their measurement data were processed simultaneously.Main results. Spontaneous breathing leaves a synchronous 'trace' on the RA PWV. The close linear correlation of the respiration phase and the phase of concomitant RA PWV changes is statistically confirmed in five tested people (Pearson's r is of the order of 0.5-0.8, P < 0.05). The BH appreciably affects the RA PWV. A phenomenon showing that the RA PWV is not indifferent to hypoxia is observed for the first time.Significance.The proposed technique for RA PWV characterization has high prospects in biomedical diagnostics. The presented pilot study deserves attention in the context of the mutual interplay between respiratory and cardiovascular systems. It may also be useful in cases where peripheral pulse wave propagation helps assess respiratory function.
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Affiliation(s)
- Boris G Vainer
- Novosibirsk State University, Novosibirsk, Russia.,Rzhanov Institute of Semiconductor Physics SB RAS, Novosibirsk, Russia
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33
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De Fazio R, Greco MR, De Vittorio M, Visconti P. A Differential Inertial Wearable Device for Breathing Parameter Detection: Hardware and Firmware Development, Experimental Characterization. SENSORS (BASEL, SWITZERLAND) 2022; 22:9953. [PMID: 36560322 PMCID: PMC9787627 DOI: 10.3390/s22249953] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/03/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Breathing monitoring is crucial for evaluating a patient's health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient's chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR-respiration rate; TI/TE-inhalation/exhalation time; IER-inhalation-to-exhalation time; V-flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland-Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation (r¯ = 0.92) and low mean difference (MD¯ = -0.27 BrPM (breaths per minute)), limits of agreement (LoA¯ = +1.16/-1.75 BrPM), and mean absolute error (MAE¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users.
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Affiliation(s)
- Roberto De Fazio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
| | - Maria Rosaria Greco
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
| | - Massimo De Vittorio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
- Center for Biomolecular Nanotechnologies, Italian Institute of Technology IIT, 73010 Arnesano, Italy
| | - Paolo Visconti
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
- Center for Biomolecular Nanotechnologies, Italian Institute of Technology IIT, 73010 Arnesano, Italy
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Wichum F, Wiede C, Seidl K. Depth-Based Measurement of Respiratory Volumes: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:9680. [PMID: 36560048 PMCID: PMC9785978 DOI: 10.3390/s22249680] [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: 10/26/2022] [Revised: 11/25/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation.
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Affiliation(s)
| | | | - Karsten Seidl
- Fraunhofer IMS, 47057 Duisburg, Germany
- Department of Electronic Components and Circuits, University of Duisburg-Essen, 47047 Duisburg, Germany
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Jin X, Zha L, Wang F, Wang Y, Zhang X. Fully integrated wearable humidity sensor for respiration monitoring. Front Bioeng Biotechnol 2022; 10:1070855. [PMID: 36532567 PMCID: PMC9755200 DOI: 10.3389/fbioe.2022.1070855] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 11/21/2022] [Indexed: 08/27/2023] Open
Abstract
Respiration monitoring is a promising alternative to medical diagnosis of several diseases. However, current techniques of respiration monitoring often require expensive and cumbersome devices which greatly limit their medical applications. Here, we present a fully integrated wearable device consisting of a flexible LCP-copper interdigital electrode, a sensing layer and a wireless electrochemical analysis system. The developed humidity sensor exhibits a high sensitivity, a good repeatability and a rapid response/recover time. The long-term stability is over 30 days at different relative humidity. By integrating the flexible humidity sensor with miniaturized electrochemical analysis system (0.8 cm × 1.8 cm), response current concerning respiration can be wirelessly transmitted to App-assisted smartphone in real time. Furthermore, the fabricated humidity sensor can realize skin moisture monitoring in a touch-less way. The large-scale production of miniaturized flexible sensor (4 mm × 6 mm) has significantly contributed to commercial deployment.
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Affiliation(s)
- Xiaofeng Jin
- School of Life Sciences, Anhui University, Hefei, China
- Key Laboratory of Human Microenvironment and Precision Medicine of Anhui Higher Education Institutes, Anhui University, Hefei, China
| | - Lin Zha
- Department of Oncology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Fan Wang
- Department of Radiotherapy, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yongzhong Wang
- School of Life Sciences, Anhui University, Hefei, China
- Key Laboratory of Human Microenvironment and Precision Medicine of Anhui Higher Education Institutes, Anhui University, Hefei, China
| | - Xueji Zhang
- School of Biomedical Engineering, Shenzhen University, Shenzhen, China
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El Gharbi M, Fernández-García R, Gil I. Wireless Communication Platform Based on an Embroidered Antenna-Sensor for Real-Time Breathing Detection. SENSORS (BASEL, SWITZERLAND) 2022; 22:8667. [PMID: 36433264 PMCID: PMC9699000 DOI: 10.3390/s22228667] [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: 09/19/2022] [Revised: 10/27/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Wearable technology has been getting more attention for monitoring vital signs in various medical fields, particularly in breathing monitoring. To monitor respiratory patterns, there is a current set of challenges related to the lack of user comfort, reliability, and rigidity of the systems, as well as challenges related to processing data. Therefore, the need to develop user-friendly and reliable wireless approaches to address these problems is required. In this paper, a novel, full, compact textile breathing sensor is investigated. Specifically, an embroidered meander dipole antenna sensor integrated into an e-textile T-shirt with a Bluetooth transmitter for real-time breathing monitoring was developed and tested. The proposed antenna-based sensor is designed to transmit data over wireless communication networks at 2.4 GHz and is made of a silver-coated nylon thread. The sensing mechanism of the proposed system is based on the detection of a received signal strength indicator (RSSI) transmitted wirelessly by the antenna-based sensor, which is found to be sensitive to stretch. The respiratory system is placed on the middle of the human chest; the area of the proposed system is 4.5 × 0.48 cm2, with 2.36 × 3.17 cm2 covered by the transmitter module. The respiratory signal is extracted from the variation of the RSSI signal emitted at 2.4 GHz from the detuned embroidered antenna-based sensor embedded into a commercial T-shirt and detected using a laptop. The experimental results demonstrated that breathing signals can be acquired wirelessly by the RSSI via Bluetooth. The RSSI range change was from -80 dBm to -72 dBm, -88 dBm to -79 dBm and -85 dBm to -80 dBm during inspiration and expiration for normal breathing, speaking and movement, respectively. We tested the feasibility assessment for breathing monitoring and we demonstrated experimentally that the standard wireless networks, which measure the RSSI signal via standard Bluetooth protocol, can be used to detect human respiratory status and patterns in real time.
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Li B, Jiang W, Peng J, Li X. Deep learning-based remote-photoplethysmography measurement from short-time facial video. Physiol Meas 2022; 43. [PMID: 36215976 DOI: 10.1088/1361-6579/ac98f1] [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: 06/02/2022] [Accepted: 10/10/2022] [Indexed: 02/07/2023]
Abstract
Objective. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manually designed regions of interest (ROIs) and the skin reflection model.Approach. This paper presents a short-time end to end HR estimation framework based on facial features and temporal relationships of video frames. In the proposed method, a deep 3D multi-scale network with cross-layer residual structure is designed to construct an autoencoder and extract robust rPPG features. Then, a spatial-temporal fusion mechanism is proposed to help the network focus on features related to rPPG signals. Both shallow and fused 3D spatial-temporal features are distilled to suppress redundant information in the complex environment. Finally, a data augmentation strategy is presented to solve the problem of uneven distribution of HR in existing datasets.Main results. The experimental results on four face-rPPG datasets show that our method overperforms the state-of-the-art methods and requires fewer video frames. Compared with the previous best results, the proposed method improves the root mean square error (RMSE) by 5.9%, 3.4% and 21.4% on the OBF dataset (intra-test), COHFACE dataset (intra-test) and UBFC dataset (cross-test), respectively.Significance. Our method achieves good results on diverse datasets (i.e. highly compressed video, low-resolution and illumination variation), demonstrating that our method can extract stable rPPG signals in short time.
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Affiliation(s)
- Bin Li
- School of Information Science and Technology, Northwest University, Xi'an, People's Republic of China
| | - Wei Jiang
- School of Information Science and Technology, Northwest University, Xi'an, People's Republic of China
| | - Jinye Peng
- School of Information Science and Technology, Northwest University, Xi'an, People's Republic of China
| | - Xiaobai Li
- Center for Machine Vision and Signal Analysis, University of Oulu, Oulu
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Huang WK, Chung YM, Wang YB, Mandel JE, Wu HT. Airflow recovery from thoracic and abdominal movements using synchrosqueezing transform and locally stationary Gaussian process regression. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2021.107384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Respiratory analysis during sleep using a chest-worn accelerometer: A machine learning approach. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Owida HA, Al-Ayyad M, Al-Nabulsi JI. Emerging Development of Auto-Charging Sensors for Respiration Monitoring. Int J Biomater 2022; 2022:7098989. [PMID: 36071953 PMCID: PMC9444417 DOI: 10.1155/2022/7098989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/30/2022] [Accepted: 07/08/2022] [Indexed: 11/23/2022] Open
Abstract
In recent years, the development of biomedical monitoring systems, including respiration monitoring systems, has been accelerated. Wearable and implantable medical devices are becoming increasingly important in the diagnosis and management of disease and illness. Respiration can be monitored using a variety of biosensors and systems. Auto-charged sensors have a number of advantages, including low cost, ease of preparation, design flexibility, and a wide range of applications. It is possible to use the auto-charged sensors to directly convert mechanical energy from the airflow into electricity. The ability to monitor and diagnose one's own health is a major goal of auto-charged sensors and systems. Respiratory disease model output signals have not been thoroughly investigated and clearly understood. As a result, figuring out their exact interrelationship is a difficult and important research question. This review summarized recent developments in auto-charged respiratory sensors and systems in terms of their device principle, output property, detecting index, and so on. Researchers with an interest in auto-charged sensors can use the information presented here to better understand the difficulties and opportunities that lie ahead.
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Affiliation(s)
- Hamza Abu Owida
- Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan
| | - Muhammad Al-Ayyad
- Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan
| | - Jamal I. Al-Nabulsi
- Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan
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Selvadass S, Paul JJ, Bella Mary I T, Packiavathy ISV, Gautam S. IoT-Enabled smart mask to detect COVID19 outbreak. HEALTH AND TECHNOLOGY 2022; 12:1025-1036. [PMID: 36000088 PMCID: PMC9388365 DOI: 10.1007/s12553-022-00695-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/10/2022] [Indexed: 11/25/2022]
Abstract
Introduction Internet of Things (IoT) has dominated various sectors over human effort with its liberal dimensions of innovations. Shielding various utilizations in terms of extensity, IoT integrated with the cloud has obtained a far-reaching spectrum. Backgrounds Nevertheless, the SARS-CoV-2 has become an imperil, at the present moment causing remarkable demands on health technologies across the globe and gravely snarling the entire world populace. Whilst, the front-runners are striving enormously to uncover this virus, in the event of medications and evolving vaccines, it is also imperative to explore the existing systems dealing with medical emergence, mitigating its spread, and supremely the planning for thwarting this virus. The extant passive face masks provide effective and feasible protection by screening all the air particles entering the nasal passage. Methodology This paper aims to enucleate a new "smart mask" paradigm for telehealth. As the vital health parameters like temperature, respiratory rate(RR), and heart rate(HR) are being easily affected by this deadly virus, this paper envisions a wearable mask equipped with an active sensor (LM35 temperature sensor) that would continuously monitor these health parameters of the person wearing the mask and provide real-time analysis of the data through the cloud. Result This proposed methodology also incorporates a vigilant system that would alert the person, if the necessary physical distance is not maintained. Besides, this application provides a person with a detailed record of his health, sending doctors and hospitals for teleconsultation. Conclusion Experimental results from a functional prototype have proved it a constructive low-cost system.
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Affiliation(s)
- Salomi Selvadass
- Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu India
| | - J. John Paul
- Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu India
| | | | | | - Sneha Gautam
- Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu India
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Toften S, Kjellstadli JT, Thu OKF, Ellingsen OJ. Noncontact Longitudinal Respiratory Rate Measurements in Healthy Adults Using Radar-Based Sleep Monitor (Somnofy): Validation Study. JMIR BIOMEDICAL ENGINEERING 2022; 7:e36618. [PMID: 38875674 PMCID: PMC11041471 DOI: 10.2196/36618] [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: 01/20/2022] [Revised: 06/21/2022] [Accepted: 07/23/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Respiratory rate (RR) is arguably the most important vital sign to detect clinical deterioration. Change in RR can also, for example, be associated with the onset of different diseases, opioid overdoses, intense workouts, or mood. However, unlike for most other vital parameters, an easy and accurate measuring method is lacking. OBJECTIVE This study aims to validate the radar-based sleep monitor, Somnofy, for measuring RRs and investigate whether events affecting RR can be detected from personalized baselines calculated from nightly averages. METHODS First, RRs from Somnofy for 37 healthy adults during full nights of sleep were extensively validated against respiratory inductance plethysmography. Then, the night-to-night consistency of a proposed filtered average RR was analyzed for 6 healthy participants in a pilot study in which they used Somnofy at home for 3 months. RESULTS Somnofy measured RR 84% of the time, with mean absolute error of 0.18 (SD 0.05) respirations per minute, and Bland-Altman 95% limits of agreement adjusted for repeated measurements ranged from -0.99 to 0.85. The accuracy and coverage were substantially higher in deep and light sleep than in rapid eye movement sleep and wake. The results were independent of age, sex, and BMI, but dependent on supine sleeping position for some radar orientations. For nightly filtered averages, the 95% limits of agreement ranged from -0.07 to -0.04 respirations per minute. In the longitudinal part of the study, the nightly average was consistent from night to night, and all substantial deviations coincided with self-reported illnesses. CONCLUSIONS RRs from Somnofy were more accurate than those from any other alternative method suitable for longitudinal measurements. Moreover, the nightly averages were consistent from night to night. Thus, several factors affecting RR should be detectable as anomalies from personalized baselines, enabling a range of applications. More studies are necessary to investigate its potential in children and older adults or in a clinical setting.
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Affiliation(s)
- Ståle Toften
- Department of Data Science and Research, VitalThings AS, Tønsberg, Norway
| | | | - Ole Kristian Forstrønen Thu
- VitalThings AS, Tønsberg, Norway
- Department of Anesthesiology and Intensive Care Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
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Vats V, Nagori A, Singh P, Dutt R, Bandhey H, Wason M, Lodha R, Sethi T. Early Prediction of Hemodynamic Shock in Pediatric Intensive Care Units With Deep Learning on Thermal Videos. Front Physiol 2022; 13:862411. [PMID: 35923238 PMCID: PMC9340772 DOI: 10.3389/fphys.2022.862411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Shock is one of the major killers in intensive care units, and early interventions can potentially reverse it. In this study, we advance a noncontact thermal imaging modality for continuous monitoring of hemodynamic shock working on 1,03,936 frames from 406 videos recorded longitudinally upon 22 pediatric patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with the heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 h. Our models achieved the best area under the receiver operating characteristic curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 h, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives.
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Affiliation(s)
- Vanshika Vats
- Indraprastha Institute of Information Technology, Delhi, India
| | - Aditya Nagori
- Indraprastha Institute of Information Technology, Delhi, India
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Pradeep Singh
- Indraprastha Institute of Information Technology, Delhi, India
| | - Raman Dutt
- Computer Science and Engineering, Shiv Nadar University, Greater Noida, India
| | - Harsh Bandhey
- Indraprastha Institute of Information Technology, Delhi, India
| | - Mahika Wason
- Indraprastha Institute of Information Technology, Delhi, India
| | - Rakesh Lodha
- Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, Delhi, India
- Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India
- *Correspondence: Tavpritesh Sethi,
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Blanco-Almazan D, Groenendaal W, Catthoor F, Jane R. The Effect of Walking on the Estimation of Breathing Pattern Parameters using Wearable Bioimpedance. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3257-3260. [PMID: 36085642 DOI: 10.1109/embc48229.2022.9871633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Wearable bioimpedance is a technique proposed to estimate breathing parameters such as respiratory rate (RR). However, its potential application lies in clinical investigation of daily-life activities like walking. This study evaluated the effect of the walking interference on the estimation of breathing parameters. 50 chronic obstructive pulmonary disease patients performed static and active measurements during thoracic bioimpedance acquisition. The static measurements included respiratory airflow for reference. The active measurements were used to estimate the walking interference from bioimpedance, and the obtained signals were added to static measurements for comparison with the reference. Afterward, we applied four different preprocessing methods to remove this walking interference and the resulting signals were used to detect the respiratory cycles and estimate breathing parameters (inspiratory time, expiratory time, duty cycle, and RR). The methods performed differently in terms of accuracy and mean average percentage error (MAPE), showing the need for specific preprocessing for active measurements. Furthermore, the MAPE values in the RR estimation were close to 3 % indicating that breathing parameters can be accurately estimated during walking. Accordingly, the present study reinforces the applicability of wearable bioimpedance for respiratory monitoring. Clinical relevance- This study exhibits the suitability of wearable bioimpedance to estimate accurate breathing param-eters during walking activities.
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Selvaraju V, Spicher N, Wang J, Ganapathy N, Warnecke JM, Leonhardt S, Swaminathan R, Deserno TM. Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:4097. [PMID: 35684717 PMCID: PMC9185528 DOI: 10.3390/s22114097] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/18/2022] [Accepted: 05/18/2022] [Indexed: 02/04/2023]
Abstract
In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO2 is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO2 monitoring.
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Affiliation(s)
- Vinothini Selvaraju
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, D-38106 Braunschweig, Germany; (V.S.); (N.S.); (J.W.); (N.G.); (J.M.W.)
- Non-Invasive Imaging and Diagnostic Laboratory, Biomedical Engineering, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai 600036, India;
| | - Nicolai Spicher
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, D-38106 Braunschweig, Germany; (V.S.); (N.S.); (J.W.); (N.G.); (J.M.W.)
| | - Ju Wang
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, D-38106 Braunschweig, Germany; (V.S.); (N.S.); (J.W.); (N.G.); (J.M.W.)
| | - Nagarajan Ganapathy
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, D-38106 Braunschweig, Germany; (V.S.); (N.S.); (J.W.); (N.G.); (J.M.W.)
| | - Joana M. Warnecke
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, D-38106 Braunschweig, Germany; (V.S.); (N.S.); (J.W.); (N.G.); (J.M.W.)
| | - Steffen Leonhardt
- Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, D-52074 Aachen, Germany;
| | - Ramakrishnan Swaminathan
- Non-Invasive Imaging and Diagnostic Laboratory, Biomedical Engineering, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai 600036, India;
| | - Thomas M. Deserno
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, D-38106 Braunschweig, Germany; (V.S.); (N.S.); (J.W.); (N.G.); (J.M.W.)
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Lo Presti D, Bianchi D, Massaroni C, Gizzi A, Schena E. A Soft and Skin-Interfaced Smart Patch Based on Fiber Optics for Cardiorespiratory Monitoring. BIOSENSORS 2022; 12:363. [PMID: 35735511 PMCID: PMC9221342 DOI: 10.3390/bios12060363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/23/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
Wearables are valuable solutions for monitoring a variety of physiological parameters. Their application in cardiorespiratory monitoring may significantly impact global health problems and the economic burden related to cardiovascular and respiratory diseases. Here, we describe a soft biosensor capable of monitoring heart (HR) and respiratory (RR) rates simultaneously. We show that a skin-interfaced biosensor based on fiber optics (i.e., the smart patch) is capable of estimating HR and RR by detecting local ribcage strain caused by breathing and heart beating. The system addresses some of the main technical challenges that limit the wide-scale use of wearables, such as the simultaneous monitoring of HR and RR via single sensing modalities, their limited skin compliance, and low sensitivity. We demonstrate that the smart patch estimates HR and RR with high fidelity under different respiratory conditions and common daily body positions. We highlight the system potentiality of real-time cardiorespiratory monitoring in a broad range of home settings.
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Affiliation(s)
- Daniela Lo Presti
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy; (D.L.P.); (C.M.)
| | - Daniele Bianchi
- Unit of Nonlinear Physics and Mathematical Models, Department of Engineering, University of Rome Campus Bio-Medico, 00128 Rome, Italy; (D.B.); (A.G.)
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy; (D.L.P.); (C.M.)
| | - Alessio Gizzi
- Unit of Nonlinear Physics and Mathematical Models, Department of Engineering, University of Rome Campus Bio-Medico, 00128 Rome, Italy; (D.B.); (A.G.)
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy; (D.L.P.); (C.M.)
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Brinza C, Floria M, Covic A, Covic A, Scripcariu DV, Burlacu A. The Usefulness of Assessing Heart Rate Variability in Patients with Acute Myocardial Infarction (HeaRt-V-AMI). SENSORS (BASEL, SWITZERLAND) 2022; 22:3571. [PMID: 35591260 PMCID: PMC9103554 DOI: 10.3390/s22093571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Heart rate variability (HRV) could have independent and critical prognostic values in patients admitted for ST segment elevation myocardial infarction (STEMI). There are limited data in the literature regarding HRV assessment in STEMI setting. Thus, we aim to investigate the potential correlations between HRV and adverse outcomes in a contemporary cohort of patients presenting with STEMI undergoing primary percutaneous coronary intervention (PCI). METHODS We will perform a prospective, observational cohort study in a single healthcare center. Adult patients aged ≥18 years presenting with STEMI in sinus rhythm will be enrolled for primary PCI within 12 h from symptoms onset. Time domain, frequency domain, and nonlinear HRV parameters will be measured using a medically approved wrist-wearable device for 5 min segments during myocardial revascularization by primary PCI. Additional HRV measurements will be performed one and six months from the index event. The primary composite outcome will include all-cause mortality and major adverse cardiovascular events (during the hospital stay, one month, and one year following admission). Several secondary outcomes will be analyzed: individual components of the primary composite outcome, target lesion revascularization, hospitalizations for heart failure, ventricular arrhythmias, left ventricular ejection fraction, and left ventricular diastolic function. CONCLUSIONS Our study will enlighten the reliability and usefulness of HRV evaluation as a prognostic marker in contemporary patients with STEMI. The potential validation of HRV as a risk marker for adverse outcomes following STEMI will ensure a background for including HRV parameters in future risk scores and guidelines.
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Affiliation(s)
- Crischentian Brinza
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T Popa”, 700115 Iasi, Romania; (C.B.); (M.F.); (A.C.); (A.C.); (A.B.)
- Department of Interventional Cardiology, Institute of Cardiovascular Diseases “Prof. Dr. George I.M. Georgescu”, 700503 Iasi, Romania
| | - Mariana Floria
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T Popa”, 700115 Iasi, Romania; (C.B.); (M.F.); (A.C.); (A.C.); (A.B.)
- Internal Medicine Clinic, “Dr. Iacob Czihac” Military Emergency Clinical Hospital, 700483 Iasi, Romania
| | - Adrian Covic
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T Popa”, 700115 Iasi, Romania; (C.B.); (M.F.); (A.C.); (A.C.); (A.B.)
- Dialysis and Renal Transplant Center, Nephrology Clinic, “C.I. Parhon” University Hospital, 700503 Iasi, Romania
| | - Andreea Covic
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T Popa”, 700115 Iasi, Romania; (C.B.); (M.F.); (A.C.); (A.C.); (A.B.)
- Dialysis and Renal Transplant Center, Nephrology Clinic, “C.I. Parhon” University Hospital, 700503 Iasi, Romania
| | - Dragos-Viorel Scripcariu
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T Popa”, 700115 Iasi, Romania; (C.B.); (M.F.); (A.C.); (A.C.); (A.B.)
| | - Alexandru Burlacu
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T Popa”, 700115 Iasi, Romania; (C.B.); (M.F.); (A.C.); (A.C.); (A.B.)
- Department of Interventional Cardiology, Institute of Cardiovascular Diseases “Prof. Dr. George I.M. Georgescu”, 700503 Iasi, Romania
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Performance Index for in Home Assessment of Motion Abilities in Ataxia Telangiectasia: A Pilot Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12084093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background. It has been shown in the very recent literature that human walking generates rhythmic motor patterns with hidden time harmonic structures that are represented (at the subject’s comfortable speed) by the occurrence of the golden ratio as the the ratio of the durations of specific walking gait subphases. Such harmonic proportions may be affected—partially or even totally destroyed—by several neurological and/or systemic disorders, thus drastically reducing the smooth, graceful, and melodic flow of movements and altering gait self-similarities. Aim. In this paper we aim at, preliminarily, showing the reliability of a technologically assisted methodology—performed with an easy to use wearable motion capture system—for the evaluation of motion abilities in Ataxia-Telangiectasia (AT), a rare infantile onset neurodegenerative disorder, whose typical neurological manifestations include progressive gait unbalance and the disturbance of motor coordination. Methods. Such an experimental methodology relies, for the first time, on the most recent accurate and objective outcome measures of gait recursivity and harmonicity and symmetry and double support subphase consistency, applied to three AT patients with different ranges of AT severity. Results. The quantification of the level of the distortions of harmonic temporal proportions is shown to include the qualitative evaluations of the three AT patients provided by clinicians. Conclusions. Easy to use wearable motion capture systems might be used to evaluate AT motion abilities through recursivity and harmonicity and symmetry (quantitative) outcome measures.
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49
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Işilay Zeybek ZM, Racca V, Pezzano A, Tavanelli M, Di Rienzo M. Can Seismocardiogram Fiducial Points Be Used for the Routine Estimation of Cardiac Time Intervals in Cardiac Patients? Front Physiol 2022; 13:825918. [PMID: 35399285 PMCID: PMC8986454 DOI: 10.3389/fphys.2022.825918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/10/2022] [Indexed: 11/18/2022] Open
Abstract
The indexes of cardiac mechanics can be derived from the cardiac time intervals, CTIs, i.e., the timings among the opening and closure of the aortic and mitral valves and the Q wave in the ECG. Traditionally, CTIs are estimated by ultrasound (US) techniques, but they may also be more easily assessed by the identification of specific fiducial points (FPs) inside the waveform of the seismocardiogram (SCG), i.e., the measure of the thorax micro-accelerations produced by the heart motion. While the correspondence of the FPs with the valve movements has been verified in healthy subjects, less information is available on whether this methodology may be routinely employed in the clinical practice for the monitoring of cardiac patients, in which an SCG waveform distortion is expected because of the heart dysfunction. In this study we checked the SCG shape in 90 patients with myocardial infarction (MI), heart failure (HF), or transplanted heart (TX), referred to our hospital for rehabilitation after an acute event or after surgery. The SCG shapes were classified as traditional (T) or non-traditional (NT) on whether the FPs were visible or not on the basis of nomenclature previously proposed in literature. The T shape was present in 62% of the patients, with a higher ∓ prevalence in MI (79%). No relationship was found between T prevalence and ejection fraction (EF). In 20 patients with T shape, we checked the FPs correspondence with the real valve movements by concomitant SCG and US measures. When compared with reference values in healthy subjects available in the literature, we observed that the Echo vs. FP differences are significantly more dispersed in the patients than in the healthy population with higher differences for the estimation of the mitral valve closure (−17 vs. 4 ms on average). Our results indicate that not every cardiac patient has an SCG waveform suitable for the CTI estimation, thus before starting an SCG-based CTI monitoring a preliminary check by a simultaneous SCG-US measure is advisable to verify the applicability of the methodology.
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Affiliation(s)
| | - Vittorio Racca
- Cardiac Rehabilitation Unit, IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Antonio Pezzano
- Cardiac Rehabilitation Unit, IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Monica Tavanelli
- Cardiac Rehabilitation Unit, IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Marco Di Rienzo
- WeST Lab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
- *Correspondence: Marco Di Rienzo,
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50
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Suzuki R, Takada T, Takeshima T, Hayashi M, Miyashita J, Azuma T, Usui M, Hamaguchi S, Fukuma S, Maehara K, Fukuhara S. Usefulness of a mobile phone application for respiratory rate measurement in adult patients. Jpn J Nurs Sci 2022; 19:e12481. [PMID: 35289085 DOI: 10.1111/jjns.12481] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 12/01/2022]
Abstract
AIMS Respiratory rate measurement is one of the core nursing skills for early detection of deterioration of a patient's condition. Nevertheless, it is sometimes bothersome to visually measure respiratory rate over 1 min. Respiratory rate measurement using a mobile phone application "RRate" has been reported to be accurate and completed in a short time. However, it has only been investigated in children. The aim of this study was to validate the "RRate" compared with the 1-min method in adult patients. METHODS This was a cross-sectional study in the setting of a nursing school. Videos of the movement of the thorax during respiration of adult patients were made. Nursing students watched these videos and measured respiratory rate with each method. Bland-Altman analysis was used to calculate bias and limits of agreement. The times taken for the measurements were compared using a t test. RESULTS A total of 59 nursing students participated. When compared to the reference measurement, the one measured using "RRate" and the one measured over 1 min showed a bias of 0.40 breaths per minute and 0.65 breaths per minute, limits of agreement of -2.86 to 3.67 breaths per minute and -2.11 to 3.41 breaths per minute, respectively. The mean measurement time for "RRate" was 22.8 s (95% CI 13.9-36.6), which was significantly shorter than the 65.8 s (95% CI 61.0-73.2) for the measurement over 1 min (p < .001). CONCLUSIONS Respiratory rate can be measured accurately in a shorter time using a mobile phone application in adult patients.
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Affiliation(s)
- Ryuji Suzuki
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan
| | - Toshihiko Takada
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan.,Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Taro Takeshima
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan.,Center for Innovative Research for Communities and Clinical Excellence (CiRC2LE), Fukushima Medical University, Fukushima, Japan
| | - Michio Hayashi
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan
| | - Jun Miyashita
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan.,Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Teruhisa Azuma
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan
| | - Michiko Usui
- Shirakawa Kosei General Hospital Affiliated Nursing School, Fukushima, Japan
| | - Sugihiro Hamaguchi
- Department of General Internal Medicine, Fukushima Medical University, Fukushima, Japan
| | - Shingo Fukuma
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan.,Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Human Health Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuhira Maehara
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan
| | - Shunichi Fukuhara
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan.,Section of Clinical Epidemiology, Department of Community Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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