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Idrobo-Ávila E, Bognár G, Krefting D, Penzel T, Kovács P, Spicher N. Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:250-260. [PMID: 38766543 PMCID: PMC11100950 DOI: 10.1109/ojemb.2024.3379733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/22/2024] [Accepted: 03/12/2024] [Indexed: 05/22/2024] Open
Abstract
Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis - which involves their joint analysis - can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. Conclusions: The majority of data within VitalDB is suitable for multimodal analysis.
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Affiliation(s)
- Ennio Idrobo-Ávila
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
| | - Gergő Bognár
- Department of Numerical Analysis, Faculty of InformaticsEötvös Loránd University1117BudapestHungary
| | - Dagmar Krefting
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
| | - Thomas Penzel
- Interdisciplinary Center of Sleep MedicineCharité - Universitätsmedizin Berlin10117BerlinGermany
| | - Péter Kovács
- Department of Numerical Analysis, Faculty of InformaticsEötvös Loránd University1117BudapestHungary
| | - Nicolai Spicher
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
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Dong S, Wang Q, Wang S, Zhou C, Wang H. Hypotension prediction index for the prevention of hypotension during surgery and critical care: A narrative review. Comput Biol Med 2024; 170:107995. [PMID: 38325215 DOI: 10.1016/j.compbiomed.2024.107995] [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: 10/01/2023] [Revised: 12/17/2023] [Accepted: 01/13/2024] [Indexed: 02/09/2024]
Abstract
Surgeons and anesthesia clinicians commonly face a hemodynamic disturbance known as intraoperative hypotension (IOH), which has been linked to more severe postoperative outcomes and increases mortality rates. Increased occurrence of IOH has been positively associated with mortality and incidence of myocardial infarction, stroke, and organ dysfunction hypertension. Hence, early detection and recognition of IOH is meaningful for perioperative management. Currently, when hypotension occurs, clinicians use vasopressor or fluid therapy to intervene as IOH develops but interventions should be taken before hypotension occurs; therefore, the Hypotension Prediction Index (HPI) method can be used to help clinicians further react to the IOH process. This literature review evaluates the HPI method, which can reliably predict hypotension several minutes before a hypotensive event and is beneficial for patients' outcomes.
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Affiliation(s)
- Siwen Dong
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Qing Wang
- Department of Anesthesiology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
| | - Shuai Wang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Congcong Zhou
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China; Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Hongwei Wang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China; Department of Anesthesiology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China.
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Mohammed H, Chen HB, Li Y, Sabor N, Wang JG, Wang G. Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:1257-1281. [PMID: 38015673 DOI: 10.1109/tbcas.2023.3334960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.
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Park S, Lee S, Park E, Lee J, Kim IY. Quantitative analysis of pulse arrival time and PPG morphological features based cuffless blood pressure estimation: a comparative study between diabetic and non-diabetic groups. Biomed Eng Lett 2023; 13:625-636. [PMID: 37872987 PMCID: PMC10590356 DOI: 10.1007/s13534-023-00284-w] [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: 02/15/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 10/25/2023] Open
Abstract
Pulse arrival time (PAT) and PPG morphological features have attracted much interest in cuffless blood pressure (BP) estimation, but their effects are not clearly understood when vascular characteristics are affected by diseases such as diabetes. This work quantitatively analyzes the effect of diabetic disease on the PAT and PPG morphological features-based BP estimation. We selected 112 diabetic patients and 308 non-diabetic subjects from VitalDB, and extracted 16 features including PAT, PPG morphological features, and heart rate. BP estimation performance was statistically compared between groups using linear regression models with several feature sets, and the relative importance of each feature in the optimal feature set was extracted. As a result, the standard deviation of the error and mean absolute error of PAT-based BP estimation were significantly higher in the diabetic group than in the non-diabetic group (p < 0.01). A feature set containing PAT and PPG morphological features achieved the best performance in both groups. However, the relative importance of each feature for BP estimation differed notably between groups. The results indicate that different features are important depending on the vascular characteristics, which could help to construct different models to accommodate specific diseases.
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Affiliation(s)
- Seongryul Park
- Department of Electronic Engineering, Hanyang University, Seoul, 04763 South Korea
| | | | - Eunkyoung Park
- Department of Biomedical Engineering, Soonchunhyang University, Asan, 31538 South Korea
| | - Jongshill Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, 04763 South Korea
| | - In Young Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, 04763 South Korea
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Yoon YH, Kim J, Lee KJ, Cho D, Oh JK, Kim M, Roh JH, Park HW, Lee JH. Blood Pressure Measurement Based on the Camera and Inertial Measurement Unit of a Smartphone: Instrument Validation Study. JMIR Mhealth Uhealth 2023; 11:e44147. [PMID: 37694382 PMCID: PMC10503482 DOI: 10.2196/44147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/13/2023] [Accepted: 07/21/2023] [Indexed: 09/12/2023] Open
Abstract
Background Even though several mobile apps that can measure blood pressure have been developed, the data about the accuracy of these apps are limited. Objective We assessed the accuracy of AlwaysBP (test) in blood pressure measurement compared with the standard, cuff-based, manual method of brachial blood pressure measurement (reference). Methods AlwaysBP is a smartphone software that estimates systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on pulse transit time (PTT). PTT was calculated with a finger photoplethysmogram and seismocardiogram using, respectively, the camera and inertial measurement unit sensor of a commercially available smartphone. After calculating PTT, SBP and DBP were estimated via the Bramwell-Hill and Moens-Korteweg equations. A calibration process was carried out 3 times for each participant to determine the input parameters of the equations. This study was conducted from March to August 2021 at Chungnam National University Sejong Hospital with 87 participants aged between 19 and 70 years who met specific conditions. The primary analysis aimed to evaluate the accuracy of the test method compared with the reference method for the entire study population. The secondary analysis was performed to confirm the stability of the test method for up to 4 weeks in 15 participants. At enrollment, gender, arm circumference, and blood pressure distribution were considered according to current guidelines. Results Among the 87 study participants, 45 (52%) individuals were male, and the average age was 35.6 (SD 10.4) years. Hypertension was diagnosed in 14 (16%) participants before this study. The mean test and reference SBPs were 120.0 (SD 18.8) and 118.7 (SD 20.2) mm Hg, respectively (difference: mean 1.2, SD 7.1 mm Hg). The absolute differences between the test and reference SBPs were <5, <10, and <15 mm Hg in 57.5% (150/261), 84.3% (220/261 ), and 94.6% (247/261) of measurements. The mean test and reference DBPs were 80.1 (SD 12.6) and 81.1 (SD 14.4) mm Hg, respectively (difference: mean -1.0, SD 6.0 mm Hg). The absolute differences between the test and reference DBPs were <5, <10, and <15 mm Hg in 75.5% (197/261), 93.9% (245/261), and 97.3% (254/261) of measurements, respectively. The secondary analysis showed that after 4 weeks, the differences between SBP and DBP were 0.1 (SD 8.8) and -2.4 (SD 7.6) mm Hg, respectively. Conclusions AlwaysBP exhibited acceptable accuracy in SBP and DBP measurement compared with the standard measurement method, according to the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization protocol criteria. However, further validation studies with a specific validation protocol designed for cuffless blood pressure measuring devices are required to assess clinical accuracy. This technology can be easily applied in everyday life and may improve the general population's awareness of hypertension, thus helping to control it.
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Affiliation(s)
- Yong-Hoon Yoon
- Chungnam National University Sejong Hospital, Sejong-Si, Republic of Korea
| | - Jongin Kim
- Deepmedi Research Institute of Technology, Seoul, Republic of Korea
| | - Kwang Jin Lee
- Deepmedi Research Institute of Technology, Seoul, Republic of Korea
| | - Dongrae Cho
- Deepmedi Research Institute of Technology, Seoul, Republic of Korea
| | - Jin Kyung Oh
- Chungnam National University Sejong Hospital, Sejong-Si, Republic of Korea
| | - Minsu Kim
- Chungnam National University Sejong Hospital, Sejong-Si, Republic of Korea
| | - Jae-Hyung Roh
- Chungnam National University Sejong Hospital, Sejong-Si, Republic of Korea
| | - Hyun Woong Park
- Chungnam National University Sejong Hospital, Sejong-Si, Republic of Korea
| | - Jae-Hwan Lee
- Chungnam National University Sejong Hospital, Sejong-Si, Republic of Korea
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Nordine M, Pille M, Kraemer J, Berger C, Brandhorst P, Kaeferstein P, Kopetsch R, Wessel N, Trauzeddel RF, Treskatsch S. Intraoperative Beat-to-Beat Pulse Transit Time (PTT) Monitoring via Non-Invasive Piezoelectric/Piezocapacitive Peripheral Sensors Can Predict Changes in Invasively Acquired Blood Pressure in High-Risk Surgical Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:3304. [PMID: 36992016 PMCID: PMC10059272 DOI: 10.3390/s23063304] [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: 02/16/2023] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Non-invasive tracking of beat-to-beat pulse transit time (PTT) via piezoelectric/piezocapacitive sensors (PES/PCS) may expand perioperative hemodynamic monitoring. This study evaluated the ability for PTT via PES/PCS to correlate with systolic, diastolic, and mean invasive blood pressure (SBPIBP, DBPIBP, and MAPIBP, respectively) and to detect SBPIBP fluctuations. METHODS PES/PCS and IBP measurements were performed in 20 patients undergoing abdominal, urological, and cardiac surgery. A Pearson's correlation analysis (r) between 1/PTT and IBP was performed. The predictive ability of 1/PTT with changes in SBPIBP was determined by area under the curve (reported as AUC, sensitivity, specificity). RESULTS Significant correlations between 1/PTT and SBPIBP were found for PES (r = 0.64) and PCS (r = 0.55) (p < 0.01), as well as MAPIBP/DBPIBP for PES (r = 0.6/0.55) and PCS (r = 0.5/0.45) (p < 0.05). A 7% decrease in 1/PTTPES predicted a 30% SBPIBP decrease (0.82, 0.76, 0.76), while a 5.6% increase predicted a 30% SBPIBP increase (0.75, 0.7, 0.68). A 6.6% decrease in 1/PTTPCS detected a 30% SBPIBP decrease (0.81, 0.72, 0.8), while a 4.8% 1/PTTPCS increase detected a 30% SBPIBP increase (0.73, 0.64, 0.68). CONCLUSIONS Non-invasive beat-to-beat PTT via PES/PCS demonstrated significant correlations with IBP and detected significant changes in SBPIBP. Thus, PES/PCS as a novel sensor technology may augment intraoperative hemodynamic monitoring during major surgery.
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Affiliation(s)
- Michael Nordine
- Department of Anesthesiology and Intensive Care Medicine, Hindenburgdamm 30, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 12203 Berlin, Germany; (M.N.)
| | - Marius Pille
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Physics, Humboldt University zu Berlin, 10115 Berlin, Germany
| | - Jan Kraemer
- Department of Physics, Humboldt University zu Berlin, 10115 Berlin, Germany
| | - Christian Berger
- Department of Anesthesiology and Intensive Care Medicine, Hindenburgdamm 30, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 12203 Berlin, Germany; (M.N.)
| | - Philipp Brandhorst
- Department of Anesthesiology and Intensive Care Medicine, Hindenburgdamm 30, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 12203 Berlin, Germany; (M.N.)
| | | | | | - Niels Wessel
- Department of Physics, Humboldt University zu Berlin, 10115 Berlin, Germany
- Department of Human Medicine, MSB Medical School Berlin GmbH, 14197 Berlin, Germany
| | - Ralf Felix Trauzeddel
- Department of Anesthesiology and Intensive Care Medicine, Hindenburgdamm 30, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 12203 Berlin, Germany; (M.N.)
| | - Sascha Treskatsch
- Department of Anesthesiology and Intensive Care Medicine, Hindenburgdamm 30, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 12203 Berlin, Germany; (M.N.)
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Wang W, Mohseni P, Kilgore KL, Najafizadeh L. PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods. Front Digit Health 2023; 4:1090854. [PMID: 36844249 PMCID: PMC9944565 DOI: 10.3389/fdgth.2022.1090854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 12/28/2022] [Indexed: 02/10/2023] Open
Abstract
There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents "PulseDB," the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality 10 -s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects' identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals, making PulseDB directly usable for training deep learning models with minimal data pre-processing. Additionally, using this dataset, we conduct the first study to provide insights about the performance gap between calibration-based and calibration-free testing approaches for evaluating generalizability of the BP estimation models. We expect PulseDB, as a user-friendly, large, comprehensive and multi-functional dataset, to be used as a reliable source for the evaluation of cuff-less BP estimation methods.
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Affiliation(s)
- Weinan Wang
- Integrated Systems and NeuroImaging Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, United States
| | - Pedram Mohseni
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Kevin L. Kilgore
- Department of Physical Medicine & Rehabilitation, Case Western Reserve University and The MetroHealth System, Cleveland, OH, United States
| | - Laleh Najafizadeh
- Integrated Systems and NeuroImaging Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, United States
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Cluster analysis of clinical phenotypic heterogeneity in obstructive sleep apnea assessed using photoplethysmography. Sleep Med 2023; 102:134-141. [PMID: 36641931 DOI: 10.1016/j.sleep.2022.12.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/15/2022] [Accepted: 12/28/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND We evaluated heterogeneity in clinical phenotypes among patients with obstructive sleep apnea syndrome (OSAHS) using photoplethysmography (PPG) in cluster analysis. METHODS All enrolled patients underwent polysomnography (PSG) monitoring while wearing a PPG device. Pulse wave signals were recorded with a modified pulse oximetry probe in the PPG device. The pulse wave-derived cardiac risk composite parameter (CRI) and eight derived signal parameters were used to assess OSAHS phenotype. We defined a high cardiovascular risk OSAHS group (CRI ≥0.5) and low cardiovascular risk OSAHS group (CRI <0.5). K-means clustering was performed for analysis of clinical phenotype heterogeneity in OSAHS by combining the CRI and its derived signals. RESULTS The OSAHS group had high cardiovascular risk for sex, age, body mass index, systolic and diastolic blood pressure, apnea hypopnea index, and obstructive arousal index and higher risk of developing hypertension, diabetes, and cerebrovascular comorbidities. The low cardiovascular risk OSAHS group had higher blood oxygen levels. Three clinical phenotypes were identified in CRI clustering: 1) typical OSAHS with high risk of hypertension (characterized by middle age, obesity, hypertension with severe OSAHS); 2) older women and mild OSAHS; 3) older men and mild OSAHS. Three subtypes were obtained based on the eight cardiac risk-derived parameters: 1) hypoxia combined with decreased pulse wave amplitude variation; 2) decreased vascular pulse wave amplitude combined with decreased pulse frequency; 3) arrhythmia combined with hypoxia. CONCLUSIONS Establishing OSAHS clinical phenotypes with the CRI and derived parameters using PPG may help in establishing multi-dimensional assessment of cardiovascular risk in OSAHS.
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Herranz Olazabal J, Wieringa F, Hermeling E, Van Hoof C. Comparing Remote Speckle Plethysmography and Finger-Clip Photoplethysmography with Non-Invasive Finger Arterial Pressure Pulse Waves, Regarding Morphology and Arrival Time. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010101. [PMID: 36671673 PMCID: PMC9854800 DOI: 10.3390/bioengineering10010101] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/08/2023] [Indexed: 01/14/2023]
Abstract
OBJECTIVE The goal was to compare Speckle plethysmography (SPG) and Photoplethysmography (PPG) with non-invasive finger Arterial Pressure (fiAP) regarding Pulse Wave Morphology (PWM) and Pulse Arrival Time (PAT). METHODS Healthy volunteers (n = 8) were connected to a Non-Invasive Blood Pressure (NIBP) monitor providing fiAP pulse wave and PPG from a clinical transmission-mode SpO2 finger clip. Biopac recorded 3-lead ECG. A camera placed at a 25 cm distance recorded a video stream (100 fps) of a finger illuminated by a laser diode at 639 nm. A chest belt (Polar) monitored respiration. All signals were recorded simultaneously during episodes of spontaneous breathing and paced breathing. ANALYSIS Post-processing was performed in Matlab to obtain SPG and analyze the SPG, PPG and fiAP mean absolute deviations (MADs) on PWM, plus PAT modulation. RESULTS Across 2599 beats, the average fiAP MAD with PPG was 0.17 (0-1) and with SPG 0.09 (0-1). PAT derived from ECG-fiAP correlated as follows: 0.65 for ECG-SPG and 0.67 for ECG-PPG. CONCLUSION Compared to the clinical NIBP monitor fiAP reference, PWM from an experimental camera-derived non-contact reflective-mode SPG setup resembled fiAP significantly better than PPG from a simultaneously recorded clinical transmission-mode finger clip. For PAT values, no significant difference was found between ECG-SPG and ECG-PPG compared to ECG-fiAP.
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Affiliation(s)
- Jorge Herranz Olazabal
- IMEC, 3000 Leuven, Belgium
- Faculty of Engineering Science, Katholieke Universiteit Leuven (KUL), 3000 Leuven, Belgium
- IMEC NL, 5656 AE Eindhoven, The Netherlands
| | - Fokko Wieringa
- IMEC NL, 5656 AE Eindhoven, The Netherlands
- Division of Internal Medicine, Department of Nephrology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | | | - Chris Van Hoof
- IMEC, 3000 Leuven, Belgium
- Faculty of Engineering Science, Katholieke Universiteit Leuven (KUL), 3000 Leuven, Belgium
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Wong MKF, Hei H, Lim SZ, Ng EYK. Applied machine learning for blood pressure estimation using a small, real-world electrocardiogram and photoplethysmogram dataset. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:975-997. [PMID: 36650798 DOI: 10.3934/mbe.2023045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Applying machine learning techniques to electrocardiography and photoplethysmography signals and their multivariate-derived waveforms is an ongoing effort to estimate non-occlusive blood pressure. Unfortunately, real ambulatory electrocardiography and photoplethysmography waveforms are inevitably affected by motion and noise artifacts, so established machine learning architectures perform poorly when trained on data of the Multiparameter Intelligent Monitoring in Intensive Care II type, a publicly available ICU database. Our study addresses this problem by applying four well-established machine learning methods, i.e., random forest regression, support vector regression, Adaboost regression and artificial neural networks, to a small, self-sampled electrocardiography-photoplethysmography dataset (n = 54) to improve the robustness of machine learning to real-world BP estimates. We evaluated the performance using a selection of optimal feature morphologies of waveforms by using pulse arrival time, morphological and frequency photoplethysmography parameters and heart rate variability as characterization data. On the basis of the root mean square error and mean absolute error, our study showed that support vector regression gave the best performance for blood pressure estimation from noisy data, achieving an mean absolute error of 6.97 mmHg, which meets the level C criteria set by the British Hypertension Society. We demonstrate that ambulatory electrocardiography- photoplethysmography signals acquired by mobile discrete devices can be used to estimate blood pressure.
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Affiliation(s)
- Mark Kei Fong Wong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
| | - Hao Hei
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
| | - Si Zhou Lim
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
| | - Eddie Yin-Kwee Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
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Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Khan Mamun MMR, Sherif A. Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010027. [PMID: 36671599 PMCID: PMC9854981 DOI: 10.3390/bioengineering10010027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022]
Abstract
Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.
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Affiliation(s)
| | - Ahmed Sherif
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA
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Mohammed H, Wang K, Wu H, Wang G. Subject-wise model generalization through pooling and patching for regression: Application on non-invasive systolic blood pressure estimation. Comput Biol Med 2022; 151:106299. [PMID: 36423530 DOI: 10.1016/j.compbiomed.2022.106299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/19/2022] [Accepted: 11/06/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND Subject-wise modeling using machine learning is useful in many applications requiring low error and complexity, such as wearable medical devices. However, regression accuracy depends highly on the data available to train the model and the model's generalization ability. Adversely, the prediction error may increase severely if unknown data patterns test the model; such a model is known to be overfitted. In medicine-related applications, such as Non-Invasive Blood Pressure (NIBP) estimation, the high error renders the estimation model useless and dangerous. METHODS This paper presents a novel algorithm to handle overfitting by editing the training data to achieve generalization for subject-wise models. The pooling and patching (PaP) algorithms use a relatively short record segment of a subject as a Key-Segment (KS) to search through a larger dataset for similar subjects. Then samples taken from the matched subjects' pool records are used to patch the original subject's KS. Due to the significance of systolic blood pressure (SBP) and the complexity of its variability, non-invasive estimation of SBP from electrocardiography (ECG) and photoplethysmography (PPG) is introduced as an application to assess the algorithm. The study was performed on 2051 subjects with a wide range of age, height, weight, length, and health status. The subjects' records were taken from a large public dataset, VitalDB, which is acquired from subjects undergoing different surgeries. Finally, all the results are obtained without using other model generalization techniques. RESULTS The generalization effect of the proposed algorithm, PaP, significantly outperformed cross-validation, which is widely used in regression model generalization. Moreover, the testing results show that a KS of 200 to 2000 samples is sufficient for providing high accuracy for much longer testing data of about 12000 to 24000 samples long, which is less than %10 of the record length on average. Furthermore, compared to other works based on the same dataset, PaP provides a significantly lower mean error of -0.75 ± 5.51 mmHg, with a small training data portion of 15% over 2051 subjects.
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Affiliation(s)
- Hazem Mohammed
- Department of Micro/Nano Electronics, School of Electrical, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China; Electrical Engineering Department, Faculty of Engineering, Assuit University, Asyut, Egypt.
| | - Kai Wang
- Zhiyuan College, Shanghai Jiao Tong University, Shanghai, China
| | - Hao Wu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, Guangdong, China.
| | - Guoxing Wang
- Department of Micro/Nano Electronics, School of Electrical, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China; Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, China.
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14
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Cuffless Blood Pressure Monitoring: Academic Insights and Perspectives Analysis. MICROMACHINES 2022; 13:mi13081225. [PMID: 36014147 PMCID: PMC9415520 DOI: 10.3390/mi13081225] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/22/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022]
Abstract
In recent decades, cuffless blood pressure monitoring technology has been a point of research in the field of health monitoring and public media. Based on the web of science database, this paper evaluated the publications in the field from 1990 to 2020 using bibliometric analysis, described the developments in recent years, and presented future research prospects in the field. Through the comparative analysis of keywords, citations, H-index, journals, research institutions, national authors and reviews, this paper identified research hotspots and future research trends in the field of cuffless blood pressure monitoring. From the results of the bibliometric analysis, innovative methods such as machine learning technologies related to pulse transmit time and pulse wave analysis have been widely applied in blood pressure monitoring. The 2091 articles related to cuffless blood pressure monitoring technology were published in 1131 journals. In the future, improving the accuracy of monitoring to meet the international medical blood pressure standards, and achieving portability and miniaturization will remain the development goals of cuffless blood pressure measurement technology. The application of flexible electronics and machine learning strategy in the field will be two major development directions to guide the practical applications of cuffless blood pressure monitoring technology.
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15
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Heimark S, Eitzen I, Vianello I, Bøtker-Rasmussen KG, Mamen A, Hoel Rindal OM, Waldum-Grevbo B, Sandbakk Ø, Seeberg TM. Blood Pressure Response and Pulse Arrival Time During Exercise Testing in Well-Trained Individuals. Front Physiol 2022; 13:863855. [PMID: 35899026 PMCID: PMC9309297 DOI: 10.3389/fphys.2022.863855] [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: 01/27/2022] [Accepted: 06/08/2022] [Indexed: 11/15/2022] Open
Abstract
Introduction: There is a lack of data describing the blood pressure response (BPR) in well-trained individuals. In addition, continuous bio-signal measurements are increasingly investigated to overcome the limitations of intermittent cuff-based BP measurements during exercise testing. Thus, the present study aimed to assess the BPR in well-trained individuals during a cycle ergometer test with a particular focus on the systolic BP (SBP) and to investigate pulse arrival time (PAT) as a continuous surrogate for SBP during exercise testing. Materials and Methods: Eighteen well-trained male cyclists were included (32.4 ± 9.4 years; maximal oxygen uptake 63 ± 10 ml/min/kg) and performed a stepwise lactate threshold test with 5-minute stages, followed by a continuous test to voluntary exhaustion with 1-min increments when cycling on an ergometer. BP was measured with a standard automated exercise BP cuff. PAT was measured continuously with a non-invasive physiological measurements device (IsenseU) and metabolic consumption was measured continuously during both tests. Results: At lactate threshold (281 ± 56 W) and maximal intensity test (403 ± 61 W), SBP increased from resting values of 136 ± 9 mmHg to maximal values of 219 ± 21 mmHg and 231 ± 18 mmHg, respectively. Linear within-participant regression lines between PAT and SBP showed a mean r2 of 0.81 ± 17. Conclusion: In the present study focusing on the BPR in well-trained individuals, we observed a more exaggerated systolic BPR than in comparable recent studies. Future research should follow up on these findings to clarify the clinical implications of the high BPR in well-trained individuals. In addition, PAT showed strong intra-individual associations, indicating potential use as a surrogate SBP measurement during exercise testing.
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Affiliation(s)
- Sondre Heimark
- Department of Nephrology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- *Correspondence: Sondre Heimark,
| | - Ingrid Eitzen
- Department of Smart Sensors and Microsystems, SINTEF Digital, Oslo, Norway
| | - Isabella Vianello
- Department of Smart Sensors and Microsystems, SINTEF Digital, Oslo, Norway
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | | | - Asgeir Mamen
- Kristiania University College, School of Health Sciences, Oslo, Norway
| | | | | | - Øyvind Sandbakk
- Centre for Elite Sports Research, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Trine M. Seeberg
- Department of Smart Sensors and Microsystems, SINTEF Digital, Oslo, Norway
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16
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A machine learning approach for hypertension detection based on photoplethysmography and clinical data. Comput Biol Med 2022; 145:105479. [DOI: 10.1016/j.compbiomed.2022.105479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/01/2022] [Accepted: 03/31/2022] [Indexed: 11/23/2022]
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17
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Methods for Continuous Blood Pressure Estimation Using Temporal Convolutional Neural Networks and Ensemble Empirical Mode Decomposition. ELECTRONICS 2022. [DOI: 10.3390/electronics11091378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Arterial blood pressure is not only an important index that must be measured in routine physical examination but also a key monitoring parameter of the cardiovascular system in cardiac surgery, drug testing, and intensive care. To improve the measurement accuracy of continuous blood pressure, this paper uses photoplethysmography (PPG) signals to estimate diastolic blood pressure and systolic blood pressure based on ensemble empirical mode decomposition (EEMD) and temporal convolutional network (TCN). In this method, the clean PPG signal is decomposed by EEMD to obtain n-order intrinsic mode functions (IMF), and then the IMF and the original PPG are input into the constructed TCN neural network model, and the results are output. The results show that TCN has better performance than CNN, CNN-LSTM, and CNN-GRU. Using the data added with IMF, the results of the above neural network model are better than those of the model with only PPG as input, in which the systolic blood pressure (SBP) and diastolic blood pressure (DBP) results of EEMD-TCN are −1.55 ± 9.92 mmHg and 0.41 ± 4.86 mmHg. According to the estimation results, DBP meets the requirements of the AAMI standard, BHS evaluates it as Grade A, SD of SBP is close to the standard AAMI, and BHS evaluates it as Grade B.
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18
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Abstract
Cuffless blood pressure (BP) measurement has become a popular field due to clinical need and technological opportunity. However, no method has been broadly accepted hitherto. The objective of this review is to accelerate progress in the development and application of cuffless BP measurement methods. We begin by describing the principles of conventional BP measurement, outstanding hypertension/hypotension problems that could be addressed with cuffless methods, and recent technological advances, including smartphone proliferation and wearable sensing, that are driving the field. We then present all major cuffless methods under investigation, including their current evidence. Our presentation includes calibrated methods (i.e., pulse transit time, pulse wave analysis, and facial video processing) and uncalibrated methods (i.e., cuffless oscillometry, ultrasound, and volume control). The calibrated methods can offer convenience advantages, whereas the uncalibrated methods do not require periodic cuff device usage or demographic inputs. We conclude by summarizing the field and highlighting potentially useful future research directions. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA;
| | - George S Stergiou
- Hypertension Center STRIDE-7, School of Medicine, Third Department of Medicine, National and Kapodistrian University of Athens, Sotiria Hospital, Athens, Greece; ,
| | - Alberto P Avolio
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia;
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19
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Heimark S, Rindal OMH, Seeberg TM, Stepanov A, Boysen ES, Bøtker-Rasmussen KG, Mobæk NK, Søraas CL, Stenehjem AE, Fadl Elmula FEM, Waldum-Grevbo B. Blood pressure altering method affects correlation with pulse arrival time. Blood Press Monit 2022; 27:139-146. [PMID: 34855653 PMCID: PMC8893131 DOI: 10.1097/mbp.0000000000000577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/07/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Pulse arrival time (PAT) is a potential main feature in cuff-less blood pressure (BP) monitoring. However, the precise relationship between BP parameters and PAT under varying conditions lacks a complete understanding. We hypothesize that simple test protocols fail to demonstrate the complex relationship between PAT and both SBP and DBP. Therefore, this study aimed to investigate the correlation between PAT and BP during two exercise modalities with differing BP responses using an unobtrusive wearable device. METHODS Seventy-five subjects, of which 43.7% had a prior diagnosis of hypertension, participated in an isometric and dynamic exercise test also including seated periods of rest prior to, in between and after. PAT was measured using a prototype wearable chest belt with a one-channel electrocardiogram and a photo-plethysmography sensor. Reference BP was measured auscultatory. RESULTS Mean individual correlation between PAT and SBP was -0.82 ± 0.14 in the full protocol, -0.79 ± 0.27 during isometric exercise and -0.77 ± 0.19 during dynamic exercise. Corresponding correlation between PAT and DBP was 0.25 ± 0.35, -0.74 ± 0.23 and 0.39 ± 0.41. CONCLUSION The results confirm PAT as a potential main feature to track changes in SBP. The relationship between DBP and PAT varied between exercise modalities, with the sign of the correlation changing from negative to positive between type of exercise modality. Thus, we hypothesize that simple test protocols fail to demonstrate the complex relationship between PAT and BP with emphasis on DBP.
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Affiliation(s)
- Sondre Heimark
- Department of Nephrology, Oslo University Hospital
- Section for Cardiovascular and Renal Research, Oslo University Hospital
- Institute of Clinical Medicine, University of Oslo
| | | | | | | | | | | | | | - Camilla L. Søraas
- Section for Cardiovascular and Renal Research, Oslo University Hospital
- Section for Environmental and Occupational Medicine, Oslo University Hospital
| | | | - Fadl Elmula M. Fadl Elmula
- Section for Cardiovascular and Renal Research, Oslo University Hospital
- Department of Acute Medicine, Oslo University Hospital, Oslo, Norway
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20
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Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2022. [DOI: 10.3390/jsan11010017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Artificial Intelligence (AI) has broadly connected the medical field at various levels of diagnosis based on the congruous data generated. Different types of bio-signal can be used to monitor a patient’s condition and in decision making. Medical equipment uses signals to communicate information to care staff. AI algorithms and approaches will help to predict health problems and check the health status of organs, while AI prediction, classification, and regression algorithms are helping the medical industry to protect from health hazards. The early prediction and detection of health conditions will guide people to stay healthy. This paper represents the scope of bio-signals using AI in the medical area. It will illustrate possible case studies relevant to bio-signals generated through IoT sensors. The bio-signals that retrospectively occur are discussed, and the new challenges of medical diagnosis using bio-signals are identified.
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21
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Jindal G, Deshmukh C, Bagal U, Nagare G. Pulse arrival time: Measurement and clinical applications. MGM JOURNAL OF MEDICAL SCIENCES 2022. [DOI: 10.4103/mgmj.mgmj_23_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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22
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Di Rienzo M, Avolio A, Rizzo G, Zeybek ZMI, Cucugliato L. Multi-site Pulse Transit Times, Beat-to-Beat Blood Pressure, and Isovolumic Contraction Time at Rest and Under Stressors. IEEE J Biomed Health Inform 2021; 26:561-571. [PMID: 34347613 DOI: 10.1109/jbhi.2021.3101976] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This study investigates the beat-to-beat relationships among Pulse Transit Times (PTTs) and Pulse Arrival Times (PATs) concomitantly measured from the heart to finger, ear and forehead vascular districts, and their correlations with continuous finger blood pressure. These aspects were explored in 22 young volunteers at rest and during cold pressure test (CPT, thermal stress), handgrip (HG, isometric exercise) and cyclo-ergometer pedalling (CYC, dynamic exercise). The starting point of the PTT measures was the opening of the aortic valve detected by the seismocardiogram. Results indicate that PTTs measured at the ear, forehead and finger districts are uncorrelated each other at rest, and during CPT and HG. The stressors produced district-dependent changes in the PTT variability. Only the dynamic exercise was able to induce significant changes with respect to rest in the PTTs mean values (-40%, -36% and -17%, respectively for PTTear, PTTfore, PTTfinger,), and synchronize their modulations. Similar trends were observed in the PATs. The isovolumic contraction time decreased during the stressors application with a minimum at CYC (-25%) reflecting an augmented heart contractility. The increase in blood pressure (BP) at CPT was greater than that at CYC (137 vs. 128 mmHg), but the correlations between beat-to-beat transit times and BP were maximal at CYC (PAT showed a higher correlation than PTT; correlations were greater for systolic than for diastolic BP). This suggests that pulse transit times do not always depend directly on the beat-to-beat BP values but, under specific conditions, on other factors and mechanisms that concomitantly also influence BP.
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23
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Martinez-Ríos E, Montesinos L, Alfaro-Ponce M, Pecchia L. A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102813] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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24
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Rong M, Li K. A multi-type features fusion neural network for blood pressure prediction based on photoplethysmography. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102772] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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25
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Zhang Y, Zhou C, Huang Z, Ye X. Study of cuffless blood pressure estimation method based on multiple physiological parameters. Physiol Meas 2021; 42. [PMID: 33857923 DOI: 10.1088/1361-6579/abf889] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/15/2021] [Indexed: 11/11/2022]
Abstract
Objective.Noninvasive blood pressure (BP) measurement technologies have been widely studied, but they still have the disadvantages of low accuracy, the requirement for frequent calibration and limited subjects. This work considers the regulation of vascular activity by the sympathetic nervous system and proposes a method for estimating BP using multiple physiological parameters.Approach.The parameters used in the model consist of heart rate variability (HRV), pulse transit time (PTT) and pulse wave morphology features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Through four classic machine learning algorithms, a hybrid data set of 3337 subjects from two databases is evaluated to verify the ability of cross-database migration. We also recommend an individual calibration procedure to further improve the accuracy of the method.Main results.The mean absolute error (MAE) and the root mean square error (RMSE) of the proposed algorithm is 10.03 and 14.55 mmHg for systolic BP (SBP), and 5.42 and 8.19 mmHg for diastolic BP (DBP). With individual calibration, the MAE and standard deviation (SD) is -0.16 ± 7.96 (SBP) and -0.13 ± 4.50 (DBP) mmHg, which satisfied the Advancement of Medical Instrumentation (AAMI) standard. In addition, the models are used to test single databases to evaluate their performance on different data sources. The overall performance of the Adaboost algorithm is better on the Multi-parameter Intelligent Monitoring in Intensive Care Unit (MIMIC) database; the MAE between its predicted value and true value reaches 6.6mmHg (SBP) and 3.12mmHg (DBP), respectively.Significance.The proposed method considers the regulation of blood vessels and the heart by the autonomic nervous system, and verifies its effectiveness and robustness across data sources, which is promising for improving the accuracy of continuous and cuffless BP estimation.
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Affiliation(s)
- Yiming Zhang
- Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou 310027, People's Republic of China
| | - Congcong Zhou
- Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou 310027, People's Republic of China
| | - Zhongyi Huang
- Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou 310027, People's Republic of China
| | - Xuesong Ye
- Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou 310027, People's Republic of China.,Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University, Hangzhou 310058, People's Republic of China
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26
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Yang S, Sohn J, Lee S, Lee J, Kim HC. Estimation and Validation of Arterial Blood Pressure Using Photoplethysmogram Morphology Features in Conjunction With Pulse Arrival Time in Large Open Databases. IEEE J Biomed Health Inform 2021; 25:1018-1030. [PMID: 32750963 DOI: 10.1109/jbhi.2020.3009658] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Although various predictors and methods for BP estimation have been proposed, differences in study designs have led to difficulties in determining the optimal method. This study presents analyses of BP estimation methods using 2.4 million cardiac cycles of two commonly used non-invasive biosignals, electrocardiogram (ECG) and photoplethysmogram (PPG), from 1376 surgical patients. Feature selection methods were used to determine the best subset of predictors from a total of 42 including PAT, heart rate (HR), and various PPG morphology features, and BP estimation models constructed using linear regression (LR), random forest (RF), artificial neural network (ANN), and recurrent neural network (RNN) were evaluated. 28 features out of 42 were determined as suitable for BP estimation, in particular two PPG morphology features outperformed PAT, which has been conventionally seen as the best non-invasive indicator of BP. By modelling the low frequency component of BP using ANN and the high frequency component using RNN with the selected predictors, mean errors of 0.05 ± 6.92 mmHg for systolic BP, and -0.05 ± 3.99 mmHg for diastolic BP were achieved. External validation of the model using another biosignal database consisting of 334 intensive care unit patients led to similar results, satisfying three standards for accuracy of BP monitors. The results indicate that the proposed method can contribute to the realization of ubiquitous non-invasive continuous BP monitoring.
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27
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Conventional pulse transit times as markers of blood pressure changes in humans. Sci Rep 2020; 10:16373. [PMID: 33009445 PMCID: PMC7532447 DOI: 10.1038/s41598-020-73143-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 09/09/2020] [Indexed: 11/08/2022] Open
Abstract
Pulse transit time (PTT) represents a potential approach for cuff-less blood pressure (BP) monitoring. Conventionally, PTT is determined by (1) measuring (a) ECG and ear, finger, or toe PPG waveforms or (b) two of these PPG waveforms and (2) detecting the time delay between the waveforms. The conventional PTTs (cPTTs) were compared in terms of correlation with BP in humans. Thirty-two volunteers [50% female; 52 (17) (mean (SD)) years; 25% hypertensive] were studied. The four waveforms and manual cuff BP were recorded before and after slow breathing, mental arithmetic, cold pressor, and sublingual nitroglycerin. Six cPTTs were detected as the time delays between the ECG R-wave and ear PPG foot, R-wave and finger PPG foot [finger pulse arrival time (PAT)], R-wave and toe PPG foot (toe PAT), ear and finger PPG feet, ear and toe PPG feet, and finger and toe PPG feet. These time delays were also detected via PPG peaks. The best correlation by a substantial extent was between toe PAT via the PPG foot and systolic BP [- 0.63 ± 0.05 (mean ± SE); p < 0.001 via one-way ANOVA]. Toe PAT is superior to other cPTTs including the popular finger PAT as a marker of changes in BP and systolic BP in particular.
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28
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Prognosis of Diabetic Peripheral Neuropathy via Decomposed Digital Volume Pulse from the Fingertip. ENTROPY 2020; 22:e22070754. [PMID: 33286526 PMCID: PMC7517300 DOI: 10.3390/e22070754] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/03/2020] [Accepted: 07/06/2020] [Indexed: 12/27/2022]
Abstract
Diabetic peripheral neuropathy (DPN) is a very common neurological disorder in diabetic patients. This study presents a new percussion-based index for predicting DPN by decomposing digital volume pulse (DVP) signals from the fingertip. In this study, 130 subjects (50 individuals 44 to 89 years of age without diabetes and 80 patients 37 to 86 years of age with type 2 diabetes) were enrolled. After baseline measurement and blood tests, 25 diabetic patients developed DPN within the following five years. After removing high-frequency noise in the original DVP signals, the decomposed DVP signals were used for percussion entropy index (PEIDVP) computation. Effects of risk factors on the incidence of DPN in diabetic patients within five years of follow-up were tested using binary logistic regression analysis, controlling for age, waist circumference, low-density lipoprotein cholesterol, and the new index. Multivariate analysis showed that patients who did not develop DPN in the five-year period had higher PEIDVP values than those with DPN, as determined by logistic regression model (PEIDVP: odds ratio 0.913, 95% CI 0.850 to 0.980). This study shows that PEIDVP can be a major protective factor in relation to the studied binary outcome (i.e., DPN or not in diabetic patients five years after baseline measurement).
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Wei HC, Ta N, Hu WR, Xiao MX, Tang XJ, Haryadi B, Liou JJ, Wu HT. Digital Volume Pulse Measured at the Fingertip as an Indicator of Diabetic Peripheral Neuropathy in the Aged and Diabetic. ENTROPY 2019; 21:1229. [PMCID: PMC7514575 DOI: 10.3390/e21121229] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
This study investigated the application of a modified percussion entropy index (PEIPPI) in assessing the complexity of baroreflex sensitivity (BRS) for diabetic peripheral neuropathy prognosis. The index was acquired by comparing the obedience of the fluctuation tendency in the change between the amplitudes of continuous digital volume pulse (DVP) and variations in the peak-to-peak interval (PPI) from a decomposed intrinsic mode function (i.e., IMF6) through ensemble empirical mode decomposition (EEMD). In total, 100 middle-aged subjects were split into 3 groups: healthy subjects (group 1, 48–89 years, n = 34), subjects with type 2 diabetes without peripheral neuropathy within 5 years (group 2, 42–86 years, n = 42, HbA1c ≥ 6.5%), and type 2 diabetic patients with peripheral neuropathy within 5 years (group 3, 37–75 years, n = 24). The results were also found to be very successful at discriminating between PEIPPI values among the three groups (p < 0.017), and indicated significant associations with the anthropometric (i.e., body weight and waist circumference) and serum biochemical (i.e., triglycerides, glycated hemoglobin, and fasting blood glucose) parameters in all subjects (p < 0.05). The present study, which utilized the DVP signals of aged, overweight subjects and diabetic patients, successfully determined the PPI intervals from IMF6 through EEMD. The PEIPPI can provide a prognosis of peripheral neuropathy from diabetic patients within 5 years after photoplethysmography (PPG) measurement.
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Affiliation(s)
- Hai-Cheng Wei
- School of Electrical and Information Engineering, North Minzu University, No. 204 North Wenchang Street, Yinchuan, Ningxia 750021, China; (H.-C.W.); (N.T.); (W.-R.H.); (M.-X.X.); (J.J.L.)
- Basic Experimental Teaching and Engineering Training Center, North Minzu University, No. 204 North Wenchang Street, Yinchuan, Ningxia 750021, China
| | - Na Ta
- School of Electrical and Information Engineering, North Minzu University, No. 204 North Wenchang Street, Yinchuan, Ningxia 750021, China; (H.-C.W.); (N.T.); (W.-R.H.); (M.-X.X.); (J.J.L.)
| | - Wen-Rui Hu
- School of Electrical and Information Engineering, North Minzu University, No. 204 North Wenchang Street, Yinchuan, Ningxia 750021, China; (H.-C.W.); (N.T.); (W.-R.H.); (M.-X.X.); (J.J.L.)
| | - Ming-Xia Xiao
- School of Electrical and Information Engineering, North Minzu University, No. 204 North Wenchang Street, Yinchuan, Ningxia 750021, China; (H.-C.W.); (N.T.); (W.-R.H.); (M.-X.X.); (J.J.L.)
| | - Xiao-Jing Tang
- School of Science, Ningxia Medical University, No. 1160 Shengli Street, Ningxia 750004, China;
| | - Bagus Haryadi
- Department of Physics, Universitas Ahmad Dahlan, Jendral A. Yani street, Kragilan, Tamanan, Kec. Banguntapan, Bantul, Daerah Istimewa, Yogyakarta 55191, Indonesia;
| | - Juin J. Liou
- School of Electrical and Information Engineering, North Minzu University, No. 204 North Wenchang Street, Yinchuan, Ningxia 750021, China; (H.-C.W.); (N.T.); (W.-R.H.); (M.-X.X.); (J.J.L.)
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China
| | - Hsien-Tsai Wu
- Department of Electrical Engineering, Dong Hwa University, No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 97401, Taiwan, China
- Correspondence:
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