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Quigley KS, Gianaros PJ, Norman GJ, Jennings JR, Berntson GG, de Geus EJC. Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement. Psychophysiology 2024; 61:e14604. [PMID: 38873876 DOI: 10.1111/psyp.14604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 12/22/2023] [Accepted: 04/04/2024] [Indexed: 06/15/2024]
Abstract
This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.
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Affiliation(s)
- Karen S Quigley
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
| | - Peter J Gianaros
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Greg J Norman
- Department of Psychology, The University of Chicago, Chicago, Illinois, USA
| | - J Richard Jennings
- Department of Psychiatry & Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gary G Berntson
- Department of Psychology & Psychiatry, The Ohio State University, Columbus, Ohio, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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2
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Yao Y, Jia Y, Wu M, Wang S, Song H, Fang X, Liao X, Li D, Zhao Q. Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care. BMC PRIMARY CARE 2024; 25:267. [PMID: 39033295 PMCID: PMC11265054 DOI: 10.1186/s12875-024-02407-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/24/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND Atrial fibrillation (AF) is highly correlated with heart failure, stroke and death. Screening increases AF detection and facilitates the early adoption of comprehensive intervention. Long-term wearable devices have become increasingly popular for AF screening in primary care. However, interpreting data obtained by long-term wearable ECG devices is a problem in primary care. To diagnose the disease quickly and accurately, we aimed to build AF episode detection model based on a nonlinear Lorenz scattergram (LS) and deep learning. METHODS The MIT-BIH Normal Sinus Rhythm Database, MIT-BIH Arrhythmia Database and the Long-Term AF Database were extracted to construct the MIT-BIH Ambulatory Electrocardiograph (MIT-BIH AE) dataset. We converted the long-term ECG into a two-dimensional LSs. The LSs from MIT-BIH AE dataset was randomly divided into training and internal validation sets in a 9:1 ratio, which was used to develop and internally validated model. We built a MOBILE-SCREEN-AF (MS-AF) dataset from a single-lead wearable ECG device in primary care for external validation. Performance was quantified using a confusion matrix and standard classification metrics. RESULTS During the evaluation of model performance based on the LS, the sensitivity, specificity and accuracy of the model in diagnosing AF were 0.992, 0.973, and 0.983 in the internal validation set respectively. In the external validation set, these metrics were 0.989, 0.956, and 0.967, respectively. Furthermore, when evaluating the model's performance based on ECG records in the MS-AF dataset, the sensitivity, specificity and accuracy of model diagnosis paroxysmal AF were 1.000, 0.870 and 0.876 respectively, and 0.927, 1.000 and 0.973 for the persistent AF. CONCLUSIONS The model based on the nonlinear LS and deep learning has high accuracy, making it promising for AF screening in primary care. It has potential for generalization and practical application.
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Grants
- 2023YFS0027, 2023YFS0240, 2023YFS0074, 2023NSFSC1652, 2022YFS0279, 2021YFQ0062, 2022JDRC0148 Sichuan Province Science and Technology Support Program
- 2023YFS0027, 2023YFS0240, 2023YFS0074, 2023NSFSC1652, 2022YFS0279, 2021YFQ0062, 2022JDRC0148 Sichuan Province Science and Technology Support Program
- ZH2022-101 Sichuan Provincial Health Commission
- HXHL21016 Sichuan University West China Nursing Discipline Development Special Fund Project
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Affiliation(s)
- Yi Yao
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Jia
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Miaomiao Wu
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Songzhu Wang
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Haiqi Song
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Xiang Fang
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoyang Liao
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Dongze Li
- Department of Emergency Medicine and Laboratory of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, China.
| | - Qian Zhao
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China.
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Pustozerov E, Kulau U, Albrecht UV. Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms-A Feasibility Study. Bioengineering (Basel) 2024; 11:596. [PMID: 38927832 PMCID: PMC11200605 DOI: 10.3390/bioengineering11060596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/27/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity-reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved.
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Affiliation(s)
- Evgenii Pustozerov
- Department of Digital Medicine, Medical Faculty OWL, Bielefeld University, 33615 Bielefeld, Germany;
| | - Ulf Kulau
- Smart Sensors Group, Hamburg University of Technology (TUHH), 21073 Hamburg, Germany;
| | - Urs-Vito Albrecht
- Department of Digital Medicine, Medical Faculty OWL, Bielefeld University, 33615 Bielefeld, Germany;
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Terzi MB, Arikan O. Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram. BIOMED ENG-BIOMED TE 2024; 69:79-109. [PMID: 37823386 DOI: 10.1515/bmt-2022-0406] [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: 10/19/2022] [Accepted: 08/25/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVES Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs. METHODS In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs. RESULTS Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs. CONCLUSIONS The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians' success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).
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Affiliation(s)
- Merve Begum Terzi
- Faculty of Engineering, Electrical and Electronics Engineering Department, Bilkent University, Ankara, Türkiye
| | - Orhan Arikan
- Faculty of Engineering, Electrical and Electronics Engineering Department, Bilkent University, Ankara, Türkiye
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Jiang X, Zhang J, Mu W, Wang K, Li L, Zhang L. TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:9680. [PMID: 38139525 PMCID: PMC10747831 DOI: 10.3390/s23249680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/10/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research.
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Affiliation(s)
- Xikang Jiang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China; (X.J.)
| | - Jinhui Zhang
- Logistic Support Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Wenyao Mu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China; (X.J.)
| | - Kun Wang
- Logistic Support Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Lei Li
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China; (X.J.)
| | - Lin Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China; (X.J.)
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Go C, Kim S, Kim Y, Sunwoo Y, Eom SH, Yun J, Shin S, Choi YJ. A Real-World Data Driven Pharmacovigilance Investigation on Drug-Induced Arrhythmia Using KAERS DB, a Korean Nationwide Adverse Drug Reporting System. Pharmaceuticals (Basel) 2023; 16:1612. [PMID: 38004477 PMCID: PMC10674837 DOI: 10.3390/ph16111612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/08/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
This study aims to investigate the prevalence and seriousness of drug-induced arrhythmia and to identify predictors associated with the seriousness of arrhythmia. Drug-induced arrhythmia cases reported to the Korean Adverse Event Reporting System Database (KAERS DB) from January 2012 to December 2021 were investigated. A disproportionality test was performed to detect the association of the etiologic medication classes and types, along with patient demographic information, with the seriousness of drug-induced arrhythmia. Logistic regression was performed to investigate the predictors that increase the risk of serious arrhythmia. The most common etiologic agent for drug-induced arrhythmia was sevoflurane, whereas serious arrhythmia was most prevalent with narcotics. Antibiotics (reporting odds ratio (ROR) 4.125; 95% CI 1.438-11.835), chemotherapy (ROR 6.994; 95% CI 2.239-21.542), and iodinated contrast media (ROR 8.273; 95% CI 3.062-22.352) had a strong association with the seriousness of drug-induced arrhythmia. Among numerous etiologic agents, ioversol (ROR 16.490; 95% CI 3.589-75.772) and lidocaine (ROR 12.347; 95% CI 2.996-50.884) were more likely to be reported with serious arrhythmia. Aging and comorbidity, primarily cancer, are the most contributing predictors associated with serious arrhythmia. Further studies on the clinical significance of patient-specific predictors for the increased risk of serious drug-induced arrhythmia are warranted to promote drug safety.
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Affiliation(s)
- Chaerin Go
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Semi Kim
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yujin Kim
- Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yongjun Sunwoo
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Sae Hyun Eom
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Jiseong Yun
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Sooyoung Shin
- Department of Pharmacy, College of Pharmacy, Ajou University, Suwon 16499, Republic of Korea
- Research Institute of Pharmaceutical Science and Technology (RIPST), Ajou University, Suwon 16499, Republic of Korea
| | - Yeo Jin Choi
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
- Institute of Regulatory Innovation through Science (IRIS), Kyung Hee University, Seoul 02447, Republic of Korea
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Neha, Sardana HK, Dahiya N, Dogra N, Kanawade R, Sharma YP, Kumar S. Automated myocardial infarction and angina detection using second derivative of photoplethysmography. Phys Eng Sci Med 2023; 46:1259-1269. [PMID: 37395927 DOI: 10.1007/s13246-023-01293-w] [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: 01/24/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Abstract
Photoplethysmography (PPG) based healthcare devices have gained enormous interest in the detection of cardiac abnormalities. Limited research has been implemented for myocardial infarction (MI) detection. Moreover, PPG-based detection of angina is still a research gap. PPG signals are not always informative. Therefore, this research work presents the use of PPG signals and their second derivative to evaluate myocardial infarction and angina using a novel set of morphological features. The obtained morphological features are fed onto the feed-forward artificial neural network for the identification of the type of MI and unstable angina (UA). The initial experiments have been carried out on non-ambulatory (public) subjects for feature extraction and later evaluated on ambulatory (self-generated) databases. The intended method attains accuracy, sensitivity, and specificity of 98%, 97%, 98% on the public database and 94%, 94%, 94% on the self-generated database. The result shows that the proposed set of features can detect MI and UA with significant accuracy.
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Affiliation(s)
- Neha
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- Central Scientific Instruments Organisation, Chandigarh, India
| | - H K Sardana
- Indian Institute of Information Technology, Raichur, India.
| | - N Dahiya
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - N Dogra
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - R Kanawade
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- National Chemical Laboratory, Pune, India
| | - Y P Sharma
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - S Kumar
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- Central Scientific Instruments Organisation, Chandigarh, India
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Brandwood BM, Naik GR, Gunawardana U, Gargiulo GD. Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database. SENSORS (BASEL, SWITZERLAND) 2023; 23:7401. [PMID: 37687857 PMCID: PMC10490584 DOI: 10.3390/s23177401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023]
Abstract
This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device.
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Affiliation(s)
- Benjamin M. Brandwood
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia;
| | - Upul Gunawardana
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
- The MARCS Institute, Westmead, NSW 2145, Australia
- Translational Research Health Institute, Westmead, NSW 2145, Australia
- The Ingam Institute for Medical Research, Liverpool, NSW 2170, Australia
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Tiwari A, Gray G, Bondi P, Mahnam A, Falk TH. Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features. SENSORS (BASEL, SWITZERLAND) 2023; 23:5606. [PMID: 37420772 DOI: 10.3390/s23125606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/23/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks.
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Affiliation(s)
- Abhishek Tiwari
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada
- Myant Inc., Toronto, ON M9W 5Z9, Canada
| | | | | | | | - Tiago H Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada
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10
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Tang Q, Chen Z, Ward R, Menon C, Elgendi M. PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments. Bioengineering (Basel) 2023; 10:630. [PMID: 37370561 DOI: 10.3390/bioengineering10060630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/12/2023] [Accepted: 05/15/2023] [Indexed: 06/29/2023] Open
Abstract
Electrocardiograms (ECGs) provide crucial information for evaluating a patient's cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG reconstruction using PPG signals, but some require signal alignment during the training phase, which is not feasible in real-life settings where ECG signals are not collected at the same time as PPG signals. To address this challenge, we introduce PPG2ECGps, an end-to-end, patient-specific deep-learning neural network utilizing the W-Net architecture. This novel approach enables direct ECG signal reconstruction from PPG signals, eliminating the need for signal alignment. Our experiments show that the proposed model achieves mean values of 0.977 mV for Pearson's correlation coefficient, 0.037 mV for the root mean square error, and 0.010 mV for the normalized dynamic time-warped distance when comparing reconstructed ECGs to reference ECGs from a dataset of 500 records. As PPG signals are more accessible than ECG signals, our proposed model has significant potential to improve patient monitoring and diagnosis in healthcare settings via wearable devices.
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Affiliation(s)
- Qunfeng Tang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Zhencheng Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Rabab Ward
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Carlo Menon
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland
| | - Mohamed Elgendi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland
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11
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Photoplethysmograph based arrhythmia detection using morphological features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Al Fahoum AS, Abu Al-Haija AO, Alshraideh HA. Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process. Bioengineering (Basel) 2023; 10:bioengineering10020249. [PMID: 36829743 PMCID: PMC9952145 DOI: 10.3390/bioengineering10020249] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/19/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous.
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Affiliation(s)
- Amjed S. Al Fahoum
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
- Correspondence:
| | - Ansam Omar Abu Al-Haija
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
- Industrial Engineering Department, JUST, Irbid 22110, Jordan
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Yang M, Liu W, Zhang H. A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory. Front Physiol 2022; 13:982537. [PMID: 36545286 PMCID: PMC9760867 DOI: 10.3389/fphys.2022.982537] [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: 06/30/2022] [Accepted: 11/18/2022] [Indexed: 12/09/2022] Open
Abstract
Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-invasive approach for cardiologists to diagnose and classify the nature and severity of variant cardiac diseases including cardiac arrhythmia. However, the interpretation and analysis of ECG are highly working-load demanding, and the subjective may lead to false diagnoses and heartbeats classification. In recent years, many deep learning works showed an excellent role in accurate heartbeats classification. However, the imbalance of heartbeat classes is universal in most of the available ECG databases since abnormal heartbeats are always relatively rare in real life scenarios. In addition, many existing approaches achieved prominent results by removing noise and extracting features in data preprocessing, which relies heavily on powerful computers. It is a pressing need to develop efficient and automatic light weighted algorithms for accurate heartbeats classification that can be used in portable ECG sensors. Objective: This study aims at developing a robust and efficient deep learning method, which can be embedded into wearable or portable ECG monitors for classifying heartbeats. Methods: We proposed a novel and light weighted deep learning architecture with weight-based loss based on a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) that can automatically identify five types of ECG heartbeats according to the AAMI EC57 standard. It was also true that the raw ECG signals were simply segmented without noise removal and other feature extraction processing. Moreover, to tackle the challenge of classification bias due to imbalanced ECG datasets for different types of arrhythmias, we introduced a weight-based loss function to reduce the influence of over-weighted categories in the ECG dataset. For avoiding the influence of the division of validation dataset, k-fold method was adopted to improve the reliability of the model. Results: The proposed algorithm is trained and tested on MIT-BIH Arrhythmia Database, and achieves an average of 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F1 score.
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Affiliation(s)
- Mengting Yang
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases), Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China,School of Medical Information and Engineering, Southwest Medical University, Luzhou, China,School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Weichao Liu
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases), Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
| | - Henggui Zhang
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases), Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China,Department of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom,*Correspondence: Henggui Zhang,
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Feasibility of evaluation of Polar H10 chest-belt ECG in patients with a broad range of heart conditions. COR ET VASA 2022. [DOI: 10.33678/cor.2022.083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Guess M, Zavanelli N, Yeo WH. Recent Advances in Materials and Flexible Sensors for Arrhythmia Detection. MATERIALS 2022; 15:ma15030724. [PMID: 35160670 PMCID: PMC8836661 DOI: 10.3390/ma15030724] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/06/2022] [Accepted: 01/16/2022] [Indexed: 12/24/2022]
Abstract
Arrhythmias are one of the leading causes of death in the United States, and their early detection is essential for patient wellness. However, traditional arrhythmia diagnosis by expert evaluation from intermittent clinical examinations is time-consuming and often lacks quantitative data. Modern wearable sensors and machine learning algorithms have attempted to alleviate this problem by providing continuous monitoring and real-time arrhythmia detection. However, current devices are still largely limited by the fundamental mismatch between skin and sensor, giving way to motion artifacts. Additionally, the desirable qualities of flexibility, robustness, breathability, adhesiveness, stretchability, and durability cannot all be met at once. Flexible sensors have improved upon the current clinical arrhythmia detection methods by following the topography of skin and reducing the natural interface mismatch between cardiac monitoring sensors and human skin. Flexible bioelectric, optoelectronic, ultrasonic, and mechanoelectrical sensors have been demonstrated to provide essential information about heart-rate variability, which is crucial in detecting and classifying arrhythmias. In this review, we analyze the current trends in flexible wearable sensors for cardiac monitoring and the efficacy of these devices for arrhythmia detection.
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Affiliation(s)
- Matthew Guess
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Nathan Zavanelli
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence: ; Tel.: +1-404-385-5710
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