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Chang KM, Liu PT, Wei TS. Electromyography Parameter Variations with Electrocardiography Noise. SENSORS (BASEL, SWITZERLAND) 2022; 22:5948. [PMID: 36015715 PMCID: PMC9416316 DOI: 10.3390/s22165948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/30/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
Electromyograms (EMG signals) may be contaminated by electrocardiographic (ECG) signals that cannot be easily separated with traditional filters, because both signals have some overlapping spectral components. Therefore, the first challenge encountered in signal processing is to extract the ECG noise from the EMG signal. In this study, the EMG, mixed with different degrees of noise (ECG), is simulated to investigate the variations of the EMG features. Simulated data were derived from the MIT-BIH Noise Stress Test (NSTD) Database. Two EMG and four ECG data were composed with four EMG/ECG SNR to 32 simulated signals. Following Pan-Tompkins R-peak detection, four ECG removal methods were used to remove ECG with different compensation algorithms to obtain the denoised EMG signal. A total of 13 time-domain and four frequency-domain EMG features were calculated from the denoised EMG. In addition, the similarity of denoised EMG features compared to clean EMG was also evaluated. Our results showed that with the ratio EMG/ECG SNR = 10 and 20, the ECG can be almost ignored, and the similarity of EMG features is close to 1. When EMG/ECG SNR = 1 and 2, there is a large variation of EMG features. The results of our simulation study would be beneficial for understanding the variations of EMG features upon the different EMG/ECG SNR.
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Affiliation(s)
- Kang-Ming Chang
- Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
- Department of Digital Media Design, Asia University, Taichung 41354, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
| | - Peng-Ta Liu
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung 40227, Taiwan
- Fall Prevention Center and Department of Physical Medicine & Rehabilitation, Changhua Christian Hospital, Changhua 500209, Taiwan
| | - Ta-Sen Wei
- Fall Prevention Center and Department of Physical Medicine & Rehabilitation, Changhua Christian Hospital, Changhua 500209, Taiwan
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2
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ECG-based machine-learning algorithms for heartbeat classification. Sci Rep 2021; 11:18738. [PMID: 34548508 PMCID: PMC8455569 DOI: 10.1038/s41598-021-97118-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 08/19/2021] [Indexed: 11/16/2022] Open
Abstract
Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.
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3
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Elgendi M, Fletcher R, Liang Y, Howard N, Lovell NH, Abbott D, Lim K, Ward R. The use of photoplethysmography for assessing hypertension. NPJ Digit Med 2019; 2:60. [PMID: 31388564 PMCID: PMC6594942 DOI: 10.1038/s41746-019-0136-7] [Citation(s) in RCA: 207] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 06/05/2019] [Indexed: 12/13/2022] Open
Abstract
The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.
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Affiliation(s)
- Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, Canada
- BC Children’s & Women’s Hospital, Vancouver, Canada
| | - Richard Fletcher
- D-Lab, Massachusetts Institute of Technology, Cambridge, MA USA
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA USA
| | - Yongbo Liang
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
| | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Howard Brain Sciences Foundation, Providence, Rhode Island USA
| | - Nigel H. Lovell
- Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW Australia
| | - Derek Abbott
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA Australia
- Centre for Biomedical Engineering, The University of Adelaide, Adelaide, SA Australia
| | - Kenneth Lim
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, Canada
- BC Children’s & Women’s Hospital, Vancouver, Canada
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
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4
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Elgendi M, Menon C. Assessing Anxiety Disorders Using Wearable Devices: Challenges and Future Directions. Brain Sci 2019; 9:E50. [PMID: 30823690 PMCID: PMC6468793 DOI: 10.3390/brainsci9030050] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 02/14/2019] [Accepted: 02/26/2019] [Indexed: 02/07/2023] Open
Abstract
Wearable devices (WD) are starting to increasingly be used for interventions to promote well-being by reducing anxiety disorders (AD). Electrocardiogram (ECG) signal is one of the most commonly used biosignals for assessing the cardiovascular system as it significantly reflects the activity of the autonomic nervous system during emotional changes. Little is known about the accuracy of using ECG features for detecting ADs. Moreover, during our literature review, a limited number of studies were found that involve ECG collection using WD for promoting mental well-being. Thus, for the sake of validating the reliability of ECG features for detecting anxiety in WD, we screened 1040 articles, and only 22 were considered for our study; specifically 6 on panic, 4 on post-traumatic stress, 4 on generalized anxiety, 3 on social, 3 on mixed, and 2 on obsessive-compulsive anxiety disorder articles. Most experimental studies had controversial results. Upon reviewing each of these papers, it became apparent that the use of ECG features for detecting different types of anxiety is controversial, and the use of ECG-WD is an emerging area of research, with limited evidence suggesting its reliability. Due to the clinical nature of most studies, it is difficult to determine the specific impact of ECG features on detecting ADs, suggesting the need for more robust studies following our proposed recommendations.
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Affiliation(s)
- Mohamed Elgendi
- Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
- Faculty of Medicine, University of British Columbia, Vancouver, BC V1Y 1T3, Canada.
- BC Children's & Women's Hospital, Vancouver, BC V6H 3N1, Canada.
| | - Carlo Menon
- Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
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5
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Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach. J Clin Med 2018; 8:jcm8010012. [PMID: 30577637 PMCID: PMC6352119 DOI: 10.3390/jcm8010012] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 11/29/2018] [Accepted: 12/14/2018] [Indexed: 12/01/2022] Open
Abstract
Hypertension is a common chronic cardiovascular disease (CVD). Early screening and diagnosis of hypertension plays a major role in its prevention and in the control of CVDs. Our study discusses the early screening of hypertension while using the morphological features of photoplethysmography (PPG). Numerous morphological features of PPG and its derivative waves were defined and extracted. Six types of feature selection methods were chosen to screen and evaluate these PPG morphological features. The optimal features were comprehensively analyzed in relation to the physiological processes of the cardiovascular circulatory system. Particularly, the intrinsic relation and physiological significance between the formation process of systolic blood pressure (SBP) and PPG morphology features were analyzed in depth. A variety of linear and nonlinear classification models were established for the comparison trials. The F1 scores for the normotension versus prehypertension, normotension and prehypertension versus hypertension, and normotension versus hypertension trials were 72.97%, 81.82%, and 92.31%, respectively. In summary, this study established a PPG characteristic analysis model and established the intrinsic relationship between SBP and PPG characteristics. Finally, the risk stratification of hypertension at different stages was examined and compared based on the optimal feature subset.
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6
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Liang Y, Chen Z, Ward R, Elgendi M. Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. Diagnostics (Basel) 2018; 8:E65. [PMID: 30201887 PMCID: PMC6163274 DOI: 10.3390/diagnostics8030065] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 09/07/2018] [Accepted: 09/07/2018] [Indexed: 12/13/2022] Open
Abstract
Cardiovascular diseases (CVDs) have become the biggest threat to human health, and they are accelerated by hypertension. The best way to avoid the many complications of CVDs is to manage and prevent hypertension at an early stage. However, there are no symptoms at all for most types of hypertension, especially for prehypertension. The awareness and control rates of hypertension are extremely low. In this study, a novel hypertension management method based on arterial wave propagation theory and photoplethysmography (PPG) morphological theory was researched to explore the physiological changes in different blood pressure (BP) levels. Pulse Arrival Time (PAT) and photoplethysmogram (PPG) features were extracted from electrocardiogram (ECG) and PPG signals to represent the arterial wave propagation theory and PPG morphological theory, respectively. Three feature sets, one containing PAT only, one containing PPG features only, and one containing both PAT and PPG features, were used to classify the different BP categories, defined as normotension, prehypertension, and hypertension. PPG features were shown to classify BP categories more accurately than PAT. Furthermore, PAT and PPG combined features improved the BP classification performance. The F1 scores to classify normotension versus prehypertension reached 84.34%, the scores for normotension versus hypertension reached 94.84%, and the scores for normotension plus prehypertension versus hypertension reached 88.49%. This indicates that the simultaneous collection of ECG and PPG signals could detect hypertension.
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Affiliation(s)
- Yongbo Liang
- School of Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
| | - Zhencheng Chen
- School of Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| | - Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, BC V6Z 2K8, Canada.
- BC Children's & Women's Hospital, Vancouver, BC V6H 3N1, Canada.
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7
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Liang Y, Elgendi M, Chen Z, Ward R. An optimal filter for short photoplethysmogram signals. Sci Data 2018; 5:180076. [PMID: 29714722 PMCID: PMC5928853 DOI: 10.1038/sdata.2018.76] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 03/27/2018] [Indexed: 12/16/2022] Open
Abstract
A photoplethysmogram (PPG) contains a wealth of cardiovascular system information, and with the development of wearable technology, it has become the basic technique for evaluating cardiovascular health and detecting diseases. However, due to the varying environments in which wearable devices are used and, consequently, their varying susceptibility to noise interference, effective processing of PPG signals is challenging. Thus, the aim of this study was to determine the optimal filter and filter order to be used for PPG signal processing to make the systolic and diastolic waves more salient in the filtered PPG signal using the skewness quality index. Nine types of filters with 10 different orders were used to filter 219 (2.1s) short PPG signals. The signals were divided into three categories by PPG experts according to their noise levels: excellent, acceptable, or unfit. Results show that the Chebyshev II filter can improve the PPG signal quality more effectively than other types of filters and that the optimal order for the Chebyshev II filter is the 4th order.
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Affiliation(s)
- Yongbo Liang
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada.,School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, PR China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, PR China
| | - Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada.,Faculty of Medicine, University of British Columbia, Vancouver, V6T 1Z3, Canada.,BC Children's & Women's Hospital, Vancouver, V6H 3N1, Canada
| | - Zhencheng Chen
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, PR China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, PR China
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada
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8
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A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts. TECHNOLOGIES 2018. [DOI: 10.3390/technologies6010026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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Elgendi M, Al-Ali A, Mohamed A, Ward R. Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach. Diagnostics (Basel) 2018; 8:E10. [PMID: 29337892 PMCID: PMC5871993 DOI: 10.3390/diagnostics8010010] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 01/11/2018] [Accepted: 01/12/2018] [Indexed: 11/16/2022] Open
Abstract
Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.
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Affiliation(s)
- Mohamed Elgendi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
- Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC V6H 3N1, Canada.
| | - Abdulla Al-Ali
- Department of Computer Science & Engineering, University of Qatar, Doha 2713, Qatar.
| | - Amr Mohamed
- Department of Computer Science & Engineering, University of Qatar, Doha 2713, Qatar.
| | - Rabab Ward
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
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10
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Merging Digital Medicine and Economics: Two Moving Averages Unlock Biosignals for Better Health. Diseases 2018; 6:diseases6010006. [PMID: 29316626 PMCID: PMC5871952 DOI: 10.3390/diseases6010006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 01/05/2018] [Accepted: 01/06/2018] [Indexed: 11/17/2022] Open
Abstract
Algorithm development in digital medicine necessitates ongoing knowledge and skills updating to match the current demands and constant progression in the field. In today’s chaotic world there is an increasing trend to seek out simple solutions for complex problems that can increase efficiency, reduce resource consumption, and improve scalability. This desire has spilled over into the world of science and research where many disciplines have taken to investigating and applying more simplistic approaches. Interestingly, through a review of current literature and research efforts, it seems that the learning and teaching principles in digital medicine continue to push towards the development of sophisticated algorithms with a limited scope and has not fully embraced or encouraged a shift towards more simple solutions that yield equal or better results. This short note aims to demonstrate that within the world of digital medicine and engineering, simpler algorithms can offer effective and efficient solutions, where traditionally more complex algorithms have been used. Moreover, the note demonstrates that bridging different research disciplines is very beneficial and yields valuable insights and results.
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11
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Point-of-Care Diagnostics: Recent Advances and Trends. BIOSENSORS-BASEL 2017; 7:bios7040062. [PMID: 29258285 PMCID: PMC5746785 DOI: 10.3390/bios7040062] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 12/13/2017] [Accepted: 12/14/2017] [Indexed: 01/24/2023]
Abstract
Recent years have witnessed tremendous advances in point-of-care diagnostics (POCD), which are a result of continuous developments in biosensors, microfluidic, bioanalytical platforms, assay formats, lab-on-a-chip technologies, and complementary technologies. This special issue targets the critical advances in POCD and provides guided insights and directions for future research.
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Elgendi M, Mohamed A, Ward R. Efficient ECG Compression and QRS Detection for E-Health Applications. Sci Rep 2017; 7:459. [PMID: 28352071 PMCID: PMC5428727 DOI: 10.1038/s41598-017-00540-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 02/28/2017] [Indexed: 11/30/2022] Open
Abstract
Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.
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Affiliation(s)
- Mohamed Elgendi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
- Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Amr Mohamed
- Department of Computer Science & Engineering, University of Qatar, Doha, Qatar
| | - Rabab Ward
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
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Elgendi M, Meo M, Abbott D. A Proof-of-Concept Study: Simple and Effective Detection of P and T Waves in Arrhythmic ECG Signals. Bioengineering (Basel) 2016; 3:bioengineering3040026. [PMID: 28952588 PMCID: PMC5597269 DOI: 10.3390/bioengineering3040026] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 10/12/2016] [Accepted: 10/14/2016] [Indexed: 11/16/2022] Open
Abstract
A robust and numerically-efficient method based on two moving average filters, followed by a dynamic event-related threshold, has been developed to detect P and T waves in electrocardiogram (ECG) signals as a proof-of-concept. Detection of P and T waves is affected by the quality and abnormalities in ECG recordings; the proposed method can detect P and T waves simultaneously through a unique algorithm despite these challenges. The algorithm was tested on arrhythmic ECG signals extracted from the MIT-BIH arrhythmia database with 21,702 beats. These signals typically suffer from: (1) non-stationary effects; (2) low signal-to-noise ratio; (3) premature atrial complexes; (4) premature ventricular complexes; (5) left bundle branch blocks; and (6) right bundle branch blocks. Interestingly, our algorithm obtained a sensitivity of 98.05% and a positive predictivity of 97.11% for P waves, and a sensitivity of 99.86% and a positive predictivity of 99.65% for T waves. These results, combined with the simplicity of the method, demonstrate that an efficient and simple algorithm can suit portable, wearable, and battery-operated ECG devices.
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Affiliation(s)
- Mohamed Elgendi
- Department of Obstetrics & Gynecology, University of British Columbia and BC Children's & Women's Hospital, Vancouver, BC V6H 3N1, Canada.
| | - Marianna Meo
- Electrophysiology and Heart Modeling Institute, (IHU LIRYC), Bordeaux 33604, France.
| | - Derek Abbott
- School of Electrical and Electronics Engineering, University of Adelaide, Adelaide SA 5005, Australia.
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