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Mehta S, Kwatra N, Jain M, McDuff D. Examining the challenges of blood pressure estimation via photoplethysmogram. Sci Rep 2024; 14:18318. [PMID: 39112533 PMCID: PMC11306225 DOI: 10.1038/s41598-024-68862-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis.
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Shokouhmand A, Jiang X, Ayazi F, Ebadi N. MEMS Fingertip Strain Plethysmography for Cuffless Estimation of Blood Pressure. IEEE J Biomed Health Inform 2024; 28:2699-2712. [PMID: 38442050 DOI: 10.1109/jbhi.2024.3372968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
OBJECTIVE To develop a cuffless method for estimating blood pressure (BP) from fingertip strain plethysmography (SPG) recordings. METHODS A custom-built micro-electromechanical systems (MEMS) strain sensor is employed to record heartbeat-induced vibrations at the fingertip. An XGboost regressor is then trained to relate SPG recordings to beat-to-beat systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) values. For this purpose, each SPG segment in this setup is represented by a feature vector consisting of cardiac time interval, amplitude features, statistical properties, and demographic information of the subjects. In addition, a novel concept, coined geometric features, are introduced and incorporated into the feature space to further encode the dynamics in SPG recordings. The performance of the regressor is assessed on 32 healthy subjects through 5-fold cross-validation (5-CV) and leave-subject-out cross validation (LSOCV). RESULTS Mean absolute errors (MAEs) of 3.88 mmHg and 5.45 mmHg were achieved for DBP and SBP estimations, respectively, in the 5-CV setting. LSOCV yielded MAEs of 8.16 mmHg for DBP and 16.81 mmHg for SBP. Through feature importance analysis, 3 geometric and 26 integral-related features introduced in this work were identified as primary contributors to BP estimation. The method exhibited robustness against variations in blood pressure level (normal to critical) and body mass index (underweight to obese), with MAE ranges of [1.28, 4.28] mmHg and [2.64, 7.52] mmHg, respectively. CONCLUSION The findings suggest high potential for SPG-based BP estimation at the fingertip. SIGNIFICANCE This study presents a fundamental step towards the augmentation of optical sensors that are susceptible to dark skin tones.
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Sarkar S, Ghosh A. Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative. Comput Biol Med 2023; 166:107558. [PMID: 37806054 DOI: 10.1016/j.compbiomed.2023.107558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/02/2023] [Accepted: 09/28/2023] [Indexed: 10/10/2023]
Abstract
The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals' spectral features, extracting clinically relevant PPG and its second derivative's (SDPPG) morphological features. The developed method was assessed using a virtual in-silico dataset with more than 4000 subjects and the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) dataset. Results showed that the method attained a mean absolute error (MAE) of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost algorithm. This approach met the Association for the Advancement of Medical Instrumentation's standard and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined clinically relevant database originating from MIMIC-III and the Queensland dataset. The proposed method's performance is further evaluated in a non-clinical setting with noisy and deformed PPG signals to validate the efficacy of the SCSA method. The noise stress tests further showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. The proposed cuff-less BP estimation technique has the potential to perform well in mobile healthcare devices due to its straightforward implementation approach.
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Affiliation(s)
- Sayan Sarkar
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Aayushman Ghosh
- Department of Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 11103, India; Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
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Joung J, Jung CW, Lee HC, Chae MJ, Kim HS, Park J, Shin WY, Kim C, Lee M, Choi C. Continuous cuffless blood pressure monitoring using photoplethysmography-based PPG2BP-net for high intrasubject blood pressure variations. Sci Rep 2023; 13:8605. [PMID: 37244974 DOI: 10.1038/s41598-023-35492-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/18/2023] [Indexed: 05/29/2023] Open
Abstract
Continuous, comfortable, convenient (C3), and accurate blood pressure (BP) measurement and monitoring are needed for early diagnosis of various cardiovascular diseases. To supplement the limited C3 BP measurement of existing cuff-based BP technologies, though they may achieve reliable accuracy, cuffless BP measurement technologies, such as pulse transit/arrival time, pulse wave analysis, and image processing, have been studied to obtain C3 BP measurement. One of the recent cuffless BP measurement technologies, innovative machine-learning and artificial intelligence-based technologies that can estimate BP by extracting BP-related features from photoplethysmography (PPG)-based waveforms have attracted interdisciplinary attention of the medical and computer scientists owing to their handiness and effectiveness for both C3 and accurate, i.e., C3A, BP measurement. However, C3A BP measurement remains still unattainable because the accuracy of the existing PPG-based BP methods was not sufficiently justified for subject-independent and highly varying BP, which is a typical case in practice. To circumvent this issue, a novel convolutional neural network(CNN)- and calibration-based model (PPG2BP-Net) was designed by using a comparative paired one-dimensional CNN structure to estimate highly varying intrasubject BP. To this end, approximately [Formula: see text], [Formula: see text], and [Formula: see text] of 4185 cleaned, independent subjects from 25,779 surgical cases were used for training, validating, and testing the proposed PPG2BP-Net, respectively and exclusively (i.e., subject-independent modelling). For quantifying the intrasubject BP variation from an initial calibration BP, a novel 'standard deviation of subject-calibration centring (SDS)' metric is proposed wherein high SDS represents high intrasubject BP variation from the calibration BP and vice versa. PPG2BP-Net achieved accurately estimated systolic and diastolic BP values despite high intrasubject variability. In 629-subject data acquired after 20 minutes following the A-line (arterial line) insertion, low error mean and standard deviation of [Formula: see text] and [Formula: see text] for highly varying A-line systolic and diastolic BP values, respectively, where their SDSs are 15.375 and 8.745. This study moves one step forward in developing the C3A cuffless BP estimation devices that enable the push and agile pull services.
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Affiliation(s)
- Jingon Joung
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, South Korea.
| | - Chul-Woo Jung
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Moon-Jung Chae
- Department of Industrial Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Hae-Sung Kim
- Department of Industrial Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Jonghun Park
- Department of Industrial Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Won-Yong Shin
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, 03722, South Korea
- Pohang University of Science and Technology (POSTECH) (Artificial Intelligence), Pohang, 37673, South Korea
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Zhao L, Liang C, Huang Y, Zhou G, Xiao Y, Ji N, Zhang YT, Zhao N. Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring. NPJ Digit Med 2023; 6:93. [PMID: 37217650 DOI: 10.1038/s41746-023-00835-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 05/05/2023] [Indexed: 05/24/2023] Open
Abstract
Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.
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Affiliation(s)
- Lei Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Cunman Liang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yan Huang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Guodong Zhou
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Yiqun Xiao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Nan Ji
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yuan-Ting Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China.
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González S, Hsieh WT, Chen TPC. A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram. Sci Data 2023; 10:149. [PMID: 36944668 PMCID: PMC10030661 DOI: 10.1038/s41597-023-02020-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 02/14/2023] [Indexed: 03/23/2023] Open
Abstract
Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.
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Fleischhauer V, Feldheiser A, Zaunseder S. Beat-to-Beat Blood Pressure Estimation by Photoplethysmography and Its Interpretation. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22187037. [PMID: 36146386 PMCID: PMC9506534 DOI: 10.3390/s22187037] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/29/2022] [Accepted: 09/13/2022] [Indexed: 05/28/2023]
Abstract
Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg, r: 0.874). Interpretation by means of SHAP revealed four features from PPG, its derivation and its decomposition to be most relevant. The presented approach depicts a general way to interpret multivariate prediction algorithms and reveals certain features to be valuable for absolute BP estimation. Our work underlines the considerable impact of data selection and of training/testing separation, which must be considered in detail when algorithms are to be compared. In order to make our work traceable, we have made all methods available to the public.
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Affiliation(s)
- Vincent Fleischhauer
- Faculty of Information Technology, University of Applied Sciences and Arts Dortmund, 44139 Dortmund, Germany
- TU Dresden, Institute for Biomedical Engineering, 01069 Dresden, Germany
| | - Aarne Feldheiser
- Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, Evang. Kliniken Essen-Mitte, Huyssens-Stiftung/Knappschaft, 45136 Essen, Germany
- Department of Anaesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany
| | - Sebastian Zaunseder
- Faculty of Information Technology, University of Applied Sciences and Arts Dortmund, 44139 Dortmund, Germany
- TU Dresden, Institute for Biomedical Engineering, 01069 Dresden, Germany
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8
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Yao P, Xue N, Yin S, You C, Guo Y, Shi Y, Liu T, Yao L, Zhou J, Sun J, Dong C, Liu C, Zhao M. Multi-Dimensional Feature Combination Method for Continuous Blood Pressure Measurement Based on Wrist PPG Sensor. IEEE J Biomed Health Inform 2022; 26:3708-3719. [PMID: 35417358 DOI: 10.1109/jbhi.2022.3167059] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The cuffless blood pressure monitoring method based on photoplethysmogram (PPG) makes it possible for long-term blood pressure monitoring to prevent and treat cardiovascular and cerebrovascular events. However, the traditional feature extraction is based on two separate sensors, which is inconvenient. In a single sensor measurement, the prediction model based on a single feature group usually does not perform well. This paper presents an artificial neural network (ANN) model for predicting blood pressure based on feature combinations. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm is used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features are extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm is used for regression. Thirty-three subjects distributed in three blood pressure groups were recruited. The proposed method passes the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of 0.03 4.27 mmHg for SBP and 0.01 3.38 for DBP. Moreover, the model can track blood pressure in a long-term range, which demonstrates the robustness of the algorithm. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.
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Learning and non-learning algorithms for cuffless blood pressure measurement: a review. Med Biol Eng Comput 2021; 59:1201-1222. [PMID: 34085135 DOI: 10.1007/s11517-021-02362-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 04/08/2021] [Indexed: 10/21/2022]
Abstract
The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.
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Singla M, Azeemuddin S, Sistla P. Learning-Based Model for Central Blood Pressure Estimation using Feature Extracted from ECG and PPG signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:855-858. [PMID: 33018119 DOI: 10.1109/embc44109.2020.9176593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Pre-detection of hypertension mostly considers the measurement of Brachial Artery Blood Pressure (BABP). Although being a standard vital, it is still considered a poor alternative for Central Blood Pressure (CBP). However, CBP is measured invasively during the process of cardiac catheterization (Cath). Though cuff-less techniques to estimate BABP are widely employed, CBP estimation has not been explored yet. Moreover, to best of our knowledge intermittent CBP estimation has not been proposed earlier. Therefore, we present a cuff-less and beat-by-beat CBP estimation technique using linear regression analysis on features extracted from continuous Electrocardiogram (ECG) and Photoplethysmograph (PPG) signals. Unlike for BABP estimation, 30 supplementary features to conventional pulse transit time such as ST-interval, Psystolic peak interval, etc., were extracted to enhance CBP accuracy. This extraction was done using Haar wavelet along with modulus maxima. Feature selection has been done using the wrapper technique and reduced using principal component analysis. Segregation of each beat was achieved with the help of constraints developed based on iteration and backtracing. This model estimates Systolic CBP with a validation error of 0.109±2.37 mmHg and Diastolic CBP with an error of 0.031±2.102 mmHg for 33 Cath lab patients.
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Hosanee M, Chan G, Welykholowa K, Cooper R, Kyriacou PA, Zheng D, Allen J, Abbott D, Menon C, Lovell NH, Howard N, Chan WS, Lim K, Fletcher R, Ward R, Elgendi M. Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring. J Clin Med 2020; 9:E723. [PMID: 32155976 PMCID: PMC7141397 DOI: 10.3390/jcm9030723] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/03/2020] [Accepted: 03/05/2020] [Indexed: 12/12/2022] Open
Abstract
One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure (BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010-2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.
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Affiliation(s)
- Manish Hosanee
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; (M.H.); (G.C.); (K.W.); (R.C.); (W.-S.C.); (K.L.)
| | - Gabriel Chan
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; (M.H.); (G.C.); (K.W.); (R.C.); (W.-S.C.); (K.L.)
| | - Kaylie Welykholowa
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; (M.H.); (G.C.); (K.W.); (R.C.); (W.-S.C.); (K.L.)
| | - Rachel Cooper
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; (M.H.); (G.C.); (K.W.); (R.C.); (W.-S.C.); (K.L.)
| | - Panayiotis A. Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London EC1V 0HB, UK;
| | - Dingchang Zheng
- Research Center of Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry CV1 5FB, UK;
| | - John Allen
- Northern Medical Physics and Clinical Engineering, Freeman Hospital, Newcastle upon Tyne NE7 7DN, UK;
| | - Derek Abbott
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA 5005, Australia;
- Centre for Biomedical Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Carlo Menon
- School of Mechatronic Systems Engineering, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
| | - Nigel H. Lovell
- Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia;
| | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK;
| | - Wee-Shian Chan
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; (M.H.); (G.C.); (K.W.); (R.C.); (W.-S.C.); (K.L.)
| | - Kenneth Lim
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; (M.H.); (G.C.); (K.W.); (R.C.); (W.-S.C.); (K.L.)
| | - Richard Fletcher
- D-Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
| | - Mohamed Elgendi
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; (M.H.); (G.C.); (K.W.); (R.C.); (W.-S.C.); (K.L.)
- School of Mechatronic Systems Engineering, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
- BC Children’s & Women’s Hospital, Vancouver, BC V6H 3N1, Canada
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