1
|
Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas 2023; 44:111001. [PMID: 37494945 PMCID: PMC10686289 DOI: 10.1088/1361-6579/acead2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
Collapse
Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, 4878 Queensland, Australia
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055 Guandong, People’s Republic of China
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Harry J Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaorong Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708-0187, United States of America
- Duke Clinical Research Institute, Durham, NC 27705-3976, United States of America
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Munia Ferdoushi
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Daniel Franklin
- Institute of Biomedical Engineering, Translational Biology & Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, M5G 1M1, Canada
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Md Farhad Hassan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Jussi Hernesniemi
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Computer Sciences, College of Arts and Sciences, Emory University, Atlanta, GA 30322, United States of America
| | - Nan Ji
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
| | - Yasser Khan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Ilkka Korhonen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Chungkeun Lee
- Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, 28159, Republic of Korea
| | - Jeremy Levy
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
- Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, 3200003, Israel
| | - Yumin Li
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Chengyu Liu
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Jing Liu
- Analog Devices Inc, San Jose, CA 95124, United States of America
| | - Lei Lu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Vaidotas Marozas
- Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Meir Nitzan
- Department of Physics/Electro-Optic Engineering, Lev Academic Center, 91160 Jerusalem, Israel
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal
- Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
| | | | - Jessica C Ramella-Roman
- Department of Biomedical Engineering and Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33174, United States of America
| | - Harri Saarinen
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Md Mobashir Hasan Shandhi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Hangsik Shin
- Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gerard Stansby
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
- Northern Vascular Centre, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, 1698050, Japan
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- PulseOn Ltd, Espoo, 02150, Finland
| | - Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Yuan-Ting Zhang
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, People’s Republic of China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| |
Collapse
|
2
|
Li W, Zhang H, Xiao Y, Tao Y, Chen W, Wang D. Association between occupational noise exposure duration and heart rate variability among Chinese adults: The role of plasma miRNAs. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 323:121324. [PMID: 36813098 DOI: 10.1016/j.envpol.2023.121324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 01/31/2023] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
We aimed to explore the association between occupational noise exposure duration and heart rate variability (HRV) and the underlying mechanism. A total of 449 subjects in a manufacturing company in Wuhan, China were included in our study and six candidate miRNAs (miR-200a-3p, miR-200b-3p, miR-200c-3p, miR-1-3p, miR-92a-3p and miR-21-5p) were tested among 200 individuals. Information combining the work histories and the occupational noise monitoring records were used to calculate the exposure of occupational noise, HRV indices were measured by using 3-channel digital Holter monitors, including the standard deviation of all normal R-R intervals (SDNN), the root mean of the square of successive differences between adjacent normal NN intervals (r-MSSD), SDNN index, low-frequency power (LF), high-frequency power (HF) and TP (total power). We found a significant linear negative dose-response relationship between occupational noise exposure duration and HRV indices (P for overall <0.05, P for nonlinear >0.05), including SDNN, r-MSSD, SDNN index, LF and HF. In the continuous models, the β (95% CIs) for each 1-year occupational noise exposure were -0.002 (-0.004, -0.001) for SDNN, -0.002 (-0.004, -0.001) for r-MSSD, -0.002 (-0.004, -0.001) for SDNN index, and -0.006 (-0.012, -0.001) for HF. Meanwhile, we also found that occupational noise exposure duration was significantly associated with lower expression of five miRNAs, when adjusting for other covariates. The β (95% CIs) were -0.039 (-0.067, -0.011) for miRNA-200c-3p, -0.053 (-0.083, -0.022) for miRNA-200a-3p, -0.044 (-0.070, -0.019) for miRNA-200b-3p, -0.032 (-0.048, -0.017) for miRNA-92a-3p, and -0.063 (-0.089, -0.038) for miRNA-21-5p in the continuous models. In addition, we found a positive association between miRNA-1-3p and LF (β = 0.039, 95% CI = 0.002, 0.080). Our study suggests that occupational noise exposure duration is associated with cardiac autonomic dysfunction, and the role of miRNAs in noise induced HRV reduction needs to be confirmed in future studies.
Collapse
Affiliation(s)
- Wenzhen Li
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China; Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Haozhe Zhang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yang Xiao
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yueqing Tao
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Weihong Chen
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Dongming Wang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
| |
Collapse
|
3
|
Mejía-Mejía E, Kyriacou PA. Duration of photoplethysmographic signals for the extraction of Pulse Rate Variability Indices. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
4
|
Mejía-Mejía E, Kyriacou PA. Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
5
|
Mejía-Mejía E, Kyriacou PA. Spectral analysis for pulse rate variability assessment from simulated photoplethysmographic signals. Front Physiol 2022; 13:966130. [PMID: 36569750 PMCID: PMC9780434 DOI: 10.3389/fphys.2022.966130] [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: 06/10/2022] [Accepted: 11/29/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction: Pulse rate variability (PRV) refers to the changes in pulse rate through time and is extracted from pulsatile signals such as the photoplethysmogram (PPG). Although PRV has been used as a surrogate of heart rate variability (HRV), which is measured from the electrocardiogram (ECG), these variables have been shown to have differences, and it has been hypothesised that these differences may arise from technical aspects that may affect the reliable extraction of PRV from PPG signals. Moreover, there are no guidelines for the extraction of PRV information from pulsatile signals. Aim: In this study, the extraction of frequency-domain information from PRV was studied, in order to establish the best performing combination of parameters and algorithms to obtain the spectral representation of PRV. Methods: PPG signals with varying and known PRV content were simulated, and PRV information was extracted from these signals. Several spectral analysis techniques with different parameters were applied, and absolute, relative and centroid-related frequency-domain indices extracted from each combination. Indices from extracted and known PRV were compared using factorial analyses and Kruskal-Wallis tests to determine which spectral analysis technique gave the best performing results. Results: It was found that using fast Fourier transform and the multiple signal classification (PMUSIC) algorithms gave the best results, combined with cubic spline interpolation and a frequency resolution of 0.0078 Hz for the former; and a linear interpolation with a frequency resolution as low as 1.22 × 10-4, as well as applying a fifth order model, for the latter. Discussion: Considering the lower complexity of FFT over PMUSIC, FFT should be considered as the appropriate technique to extract frequency-domain information from PRV signals.
Collapse
|
6
|
Stuyck H, Dalla Costa L, Cleeremans A, Van den Bussche E. Validity of the Empatica E4 wristband to estimate resting-state heart rate variability in a lab-based context. Int J Psychophysiol 2022; 182:105-118. [DOI: 10.1016/j.ijpsycho.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
|
7
|
Chou L, Liu J, Gong S, Chou Y. A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree. Front Physiol 2022; 13:1008111. [PMID: 36311226 PMCID: PMC9614148 DOI: 10.3389/fphys.2022.1008111] [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: 07/31/2022] [Accepted: 09/23/2022] [Indexed: 01/11/2023] Open
Abstract
Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.
Collapse
Affiliation(s)
- Lijuan Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China,School of Computer and Information Technology, Northeast Petroleum University, Daqing, China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China
| | - Shengrong Gong
- School of Computer and Information Technology, Northeast Petroleum University, Daqing, China,School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China
| | - Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China,*Correspondence: Yongxin Chou,
| |
Collapse
|
8
|
Comparison of pulse rate variability and morphological features of photoplethysmograms in estimation of blood pressure. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
9
|
Photoplethysmography-Based Pulse Rate Variability and Haemodynamic Changes in the Absence of Heart Rate Variability: An In-Vitro Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Pulse rate variability (PRV), measured from pulsatile signals such as the photoplethysmogram (PPG), has been largely used in recent years as a surrogate of heart rate variability (HRV), which is measured from electrocardiograms (ECG). However, different studies have shown that PRV does not always replicate HRV as there are multiple factors that could affect their relationship, such as respiration and pulse transit time. In this study, an in-vitro model was developed for the simulation of the upper-circulatory system, and PPG signals were acquired from it when haemodynamic changes were induced. PRV was obtained from these signals and time-domain, frequency-domain and non-linear indices were extracted. Factorial analyses were performed to understand the effects of changing blood pressure and flow on PRV indices in the absence of HRV. Results showed that PRV indices are affected by these haemodynamic changes and that these may explain some of the differences between HRV and PRV. Future studies should aim to replicate these results in healthy volunteers and patients, as well as to include the HRV information in the in-vitro model for a more profound understanding of these differences.
Collapse
|
10
|
Zhang F. Heart Rate Estimation in Sports Based on Multi-Sensor Data for Sports Intensity Prediction. INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2022. [DOI: 10.4018/ijdst.307990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The heart rate (HR) is the most common measurement of the cardiovascular system. It reflects not only the cardiovascular function, but also the degree of recovery, and has high reliability. The heart rate monitoring can be used in athlete selection, sports training, medical supervision, and fitness to avoid the blindness of exercise intensity arrangement, provide an objective quantitative standard for scientific fitness, and improve the sports performance through monitoring sports intensity. In order to accurately predict the sports intensity, this paper adopts ECG signals and pulse wave to learn an ordinal regression model that can utilize the order relation between different sports intensity level. The experimental results have demonstrated the effectiveness of the proposed sports intensity method.
Collapse
Affiliation(s)
- Feng Zhang
- Jilin Engineering Vocational College, China
| |
Collapse
|
11
|
Mejia-Mejia E, Kyriacou PA. Outlier Management for Pulse Rate Variability Analysis from Photoplethysmographic Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:649-652. [PMID: 36086146 DOI: 10.1109/embc48229.2022.9871942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Pulse rate variability (PRV) has been proposed as a surrogate for the estimation of Heart Rate Variability (HRV), which is a non-invasive technique used to assess the cardiac autonomic activity. However, both physiological and technical factors may affect the relationship between HRV and PRV, and there are no standards for the analysis of PRV from photoplethysmographic (PPG) signals. The aim of this study was to determine the best outlier management strategies for PRV analysis. 117 PPG signals with randomly generated PRV information were simulated using Gaussian signals. From these, interbeat intervals were detected and different outlier detection and correction techniques were applied. Time and frequency-domain and non-linear PRV indices were extracted and compared with respect to the gold standard values obtained from the simulated PRV information. The results show that, in good quality PPG signals, there is no need to apply any outlier management technique for the extraction of PRV information. Clinical relevance- Establishing guidelines for PRV mea-surement can lead to more reliable and comparable results, as well as to the increase in the use of this variable for the diagnosis and monitoring of cardiovascular and autonomic conditions.
Collapse
|
12
|
Mejía-Mejía E, May JM, Kyriacou PA. Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106724. [PMID: 35255373 DOI: 10.1016/j.cmpb.2022.106724] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/28/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content. METHODS PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests. RESULTS From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz. CONCLUSION The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application. SIGNIFICANCE The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.
Collapse
Affiliation(s)
- Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, United Kingdom.
| | - James M May
- Research Centre for Biomedical Engineering, City, University of London, London, United Kingdom
| | - Panayiotis A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, United Kingdom
| |
Collapse
|
13
|
Considering blood scattering effect in noninvasive optical detection of blood components using dynamic spectrum along with time varying filter based empirical mode decomposition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103266] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
14
|
Chen YS, Lin YY, Shih CC, Kuo CD. Relationship Between Heart Rate Variability and Pulse Rate Variability Measures in Patients After Coronary Artery Bypass Graft Surgery. Front Cardiovasc Med 2021; 8:749297. [PMID: 34977176 PMCID: PMC8716438 DOI: 10.3389/fcvm.2021.749297] [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: 07/29/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Heart rate variability (HRV) and pulse rate variability (PRV) measures are two kinds of physiological indices that can be used to evaluate the autonomic nervous function of healthy subjects and patients with various kinds of illness. Purpose: In this study, we compared the agreement and linear relationship between electrocardiographic signals (ECG)-derived HRV and photoplethysmographic signals (PPG)-derived right hand PRV (R-PRV) and left hand PRV (L-PRV) measures in 14 patients over 1 year after coronary artery bypass graft (CABG) surgery. Method: The ECG and PPG signals of the patient were recorded simultaneously for 10 min in a supine position. The last 512 stationary RR intervals (RRI) and peak-to peak intervals (PPI) of pulse wave were derived for data analysis. Bland-Altman plot was used to assess the agreement among HRV and both hand PRV measures, while linear regression analysis was used to examine the relationship among corresponding measures of HRV, R-PRV, and L-PRV. Result: The results revealed significant differences in total power (TP), very low-frequency power (VLF), low-frequency power (LF), high-frequency power (HF), and normalized VLF (VLFnorm) among HRV, R-PRV, and L-PRV. Bland-Altman plot analysis showed good agreements in almost all measures between R-PRV and L-PRV, except insufficient agreement was found in LF/HF. Insufficient agreements were found in root mean square successive difference (RMSSD), normalized HF (HFnorm), and LF/HF indices between HRV and L-PRV, and in VLFnorm, HFnorm, and LF/HF indices between HRV and R-PRV. Linear regression analysis showed that the HRV, R-PRV, and L-PRV measures were all highly correlated with one another (r = 0.94 ~ 1; p < 0.001). Conclusion: Though PRV measures of either hand are not surrogates of HRV measures, they might still be used to evaluate the autonomic nervous functions of CABG patients due to the moderate to good agreements in most time-domain and frequency-domain HRV measures and the strong and positive correlations among HRV and both hands PRV measures in CABG patients.
Collapse
Affiliation(s)
- Yung-Sheng Chen
- Department of Exercise and Health Sciences, University of Taipei, Taipei, Taiwan
- Tanyu Research Laboratory, Taipei, Taiwan
| | - Yi-Ying Lin
- Institute of Emergency and Critical Care Medicine, National Yang-Ming-Chiao-Tung University, Taipei, Taiwan
| | - Chun-Che Shih
- Division of Cardiovascular Surgery, Department of Surgery, Taipei Municipal Wan Fang Hospital, Taipei, Taiwan
- Department of Surgery, Taipei Medical University School of Medicine, Taipei, Taiwan
| | - Cheng-Deng Kuo
- Tanyu Research Laboratory, Taipei, Taiwan
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
- Research and Development Department VI, Smart Healthcare Business Unit (BU), Leadtek Research Inc., Taipei, Taiwan
- Department of Medicine, Taian Hospital, Taipei, Taiwan
- *Correspondence: Cheng-Deng Kuo
| |
Collapse
|
15
|
Estimating Resting HRV during fMRI: A Comparison between Laboratory and Scanner Environment. SENSORS 2021; 21:s21227663. [PMID: 34833744 PMCID: PMC8619981 DOI: 10.3390/s21227663] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/02/2021] [Accepted: 11/15/2021] [Indexed: 12/31/2022]
Abstract
Heart rate variability (HRV) is regularly assessed in neuroimaging studies as an indicator of autonomic, emotional or cognitive processes. In this study, we investigated the influence of a loud and cramped environment during magnetic resonance imaging (MRI) on resting HRV measures. We compared recordings during functional MRI sessions with recordings in our autonomic laboratory (LAB) in 101 healthy subjects. In the LAB, we recorded an electrocardiogram (ECG) and a photoplethysmogram (PPG) over 15 min. During resting state functional MRI, we acquired a PPG for 15 min. We assessed anxiety levels before the scanning in each subject. In 27 participants, we performed follow-up sessions to investigate a possible effect of habituation. We found a high intra-class correlation ranging between 0.775 and 0.996, indicating high consistency across conditions. We observed no systematic influence of the MRI environment on any HRV index when PPG signals were analyzed. However, SDNN and RMSSD were significantly higher when extracted from the PPG compared to the ECG. Although we found a significant correlation of anxiety and the decrease in HRV from LAB to MRI, a familiarization session did not change the HRV outcome. Our results suggest that psychological factors are less influential on the HRV outcome during MRI than the methodological choice of the cardiac signal to analyze.
Collapse
|
16
|
Tang Q, Chen Z, Menon C, Ward R, Elgendi M. PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:4007. [PMID: 34200635 PMCID: PMC8229401 DOI: 10.3390/s21124007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/31/2021] [Accepted: 06/06/2021] [Indexed: 11/17/2022]
Abstract
An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.
Collapse
Affiliation(s)
- Qunfeng Tang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; (Q.T.); (Z.C.)
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
| | - Zhencheng Chen
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; (Q.T.); (Z.C.)
| | - Carlo Menon
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland;
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | - Rabab Ward
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
| | - Mohamed Elgendi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| |
Collapse
|