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Niu Y, Wang H, Wang H, Zhang H, Jin Z, Guo Y. Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection. Digit Health 2023; 9:20552076231198682. [PMID: 37667685 PMCID: PMC10475230 DOI: 10.1177/20552076231198682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/12/2023] [Indexed: 09/06/2023] Open
Abstract
Objective To validate a single-lead electrocardiogram algorithm for identifying atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm. Methods A total of 656 subjects aged 19 to 94 years were enrolled. Participants were simultaneously tested with a wristwatch (Huawei Watch GT2 Pro, Huawei Technologies Co., Ltd, Shenzhen, China) and a 12-lead electrocardiogram for 3 minutes. A total of 1926 electrocardiogram signals from 628 subjects (282 men and 346 women) aged 19 to 94 years (median 64 years) were analyzed using an algorithm. Results The numbers of subjects with atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm were 129, 141, 107, and 251, respectively, and together they had a total of 1926 electrocardiogram signals. For the three-class classification system, the recall, precision, and F1 score were 97.6%, 96.5%, 97.0% for sinus rhythm; 96.7%, 96.9%, 96.8% for atrial fibrillation; and 92.8%, 94.2%, 93.5% for ectopic beats, respectively. The macro-F1 score of the three-class classification system was 95.8%. For the four-class classification system, the recall, precision, and F1 score were 97.6%, 96.5%, 97.0% for sinus rhythm; 96.7%, 96.9%, 96.8% for atrial fibrillation; 90.5%, 89.4%, 89.9% for atrial premature beats; and 86.1%, 89.6%, 87.8% for ventricular premature beats, respectively. The macro-F1 score of the four-class classification system was 92.9%. Conclusions The single-lead electrocardiogram algorithm embedded into smart wearables demonstrated good performance in detecting atrial fibrillation, atrial/ventricular premature beats, and sinus rhythm, and thus would facilitate atrial fibrillation screening and management.
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Affiliation(s)
- Yonghong Niu
- Department of Cardiology, The First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Hao Wang
- Department of Cardiology, Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Hong Wang
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
- Graduate School of PLA General Hospital, Beijing, China
| | - Hui Zhang
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhigeng Jin
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yutao Guo
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
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Bin Heyat MB, Akhtar F, Abbas SJ, Al-Sarem M, Alqarafi A, Stalin A, Abbasi R, Muaad AY, Lai D, Wu K. Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal. BIOSENSORS 2022; 12:427. [PMID: 35735574 PMCID: PMC9221208 DOI: 10.3390/bios12060427] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/06/2022] [Accepted: 06/14/2022] [Indexed: 05/02/2023]
Abstract
In the modern world, wearable smart devices are continuously used to monitor people's health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.
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Affiliation(s)
- Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China;
| | - Syed Jafar Abbas
- Faculty of Management, Vancouver Island University, Nanaimo, BC V9R5S5, Canada;
| | - Mohammed Al-Sarem
- College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia;
- Department of Computer Science, University of Sheba Province, Marib, Yemen
| | - Abdulrahman Alqarafi
- College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia;
| | - Antony Stalin
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China;
| | - Rashid Abbasi
- School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China;
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Mysore 570005, Karnataka, India;
- IT Department, Sana’a Community College, Sana’a 5695, Yemen
| | - Dakun Lai
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
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Qiu L, Cai W, Zhang M, Dong Y, Zhu W, Wang L. Supraventricular ectopic beats and ventricular ectopic beats detection based on improved U-net. Physiol Meas 2022; 43. [PMID: 35472766 DOI: 10.1088/1361-6579/ac6aa2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/26/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Supraventricular ectopic beats (SVEB) or ventricular ectopic beats (VEB) are common arrhythmia with uncertain occurrence and morphological diversity, so realizing their automatic localization is of great significance in clinical diagnosis. METHODS We propose a modified U-net network: USV-net, it can simultaneously realize the automatic positioning of VEB and SVEB. The improvement consists of three parts: Firstly, we reconstruct part of the convolutional layer in U-net using group convolution to reduce the expression of redundant features. Secondly, a plug-and-play multi-scale 2D deformable convolution (MSDC) module is designed to extract cross-channel features of different scales. Thirdly, in addition to conventional output of U-net, we also compress and output the bottom feature map of U-net, the dual-output is trained through Dice-loss to take into account the learning of high/low resolution features of the model. We used the MIT-BIH arrhythmia database for patient-specific training, and used Sensitivity, Positive prediction rate and F1-scores to evaluate the effectiveness of our method. MAIN RESULT The F1-scores of SVEB and VEB achieve the best results compared with other studies in different testing records. It is worth noting that the F1-scores of SVEB and VEB reached 81.3 and 95.4 in the 24 testing records. Moreover, our method is also at the forefront in Sensitivity and Positive prediction rate. SIGNIFICANCE The method proposed in this paper has great potential in the detection of SVEB and VEB. We anticipate efficiency and accuracy of clinical detection of ectopic beats would be improved.
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Affiliation(s)
- Lishen Qiu
- School of Biomedical Engineering (Suzhou),Division of Life Sciences and medicine, University of Science and Technology of China, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, suzhou, 230026, CHINA
| | - Wenqiang Cai
- Soochow University, No. 333 Ganjiang East Road, Gusu District, Suzhou City, Suzhou, 215000, CHINA
| | - Miao Zhang
- , SIBET, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Suzhou, 215000, CHINA
| | - Yanfang Dong
- School of Biomedical Engineering (suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Hefei, 215000, CHINA
| | - Wenliang Zhu
- , SIBET, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Suzhou, 215000, CHINA
| | - Lirong Wang
- Soochow University, No. 333 Ganjiang East Road, Gusu District, Suzhou City, Suzhou, 215006, CHINA
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Cai Z, Wang T, Shen Y, Xing Y, Yan R, Li J, Liu C. Robust PVC Identification by Fusing Expert System and Deep Learning. BIOSENSORS 2022; 12:185. [PMID: 35448245 PMCID: PMC9025768 DOI: 10.3390/bios12040185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 03/18/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.
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Affiliation(s)
- Zhipeng Cai
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Tiantian Wang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Yumin Shen
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Yantao Xing
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Ruqiang Yan
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 714009, China;
| | - Jianqing Li
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 714009, China;
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
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Yang J, Cai W, Wang M. Premature beats detection based on a novel convolutional neural network. Physiol Meas 2021; 42. [PMID: 34167103 DOI: 10.1088/1361-6579/ac0e82] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 06/24/2021] [Indexed: 11/11/2022]
Abstract
Objective.Automatic detection of premature beats on long electrocardiogram (ECG) recordings is of great significance for clinical diagnosis. In this paper, we propose a novel deep learning model, the ECGDet, to detect premature beats, including premature ventricular contractions (PVCs) and supraventricular premature beats (SPBs) on single-lead long-term ECGs.Approach.The ECGDet is proposed based on a convolutional neural network and squeeze-and-excitation network. It outputs the probabilities that the ECG samples belong to a premature contraction. Non-max suppression was used to select the most appropriate locations for the premature beats. The ECGDet was trained and tested on the MIT-BIH arrhythmia database (MITDB) using a five-fold cross-validation approach. A novel loss calculation method was introduced in the model training process. Then it was tuned and further tested on the China Physiological Signal Challenge (2020) database (CPSCDB).Main results.The results showed that the average F1 value of PVC detection was 92.6%, while that of SPB detection was 72.2% on MITDB. The ECGDet bagged the 2nd place for PVC detection and ranked 7th place of SPB detection in the China Physiological Signal Challenge (2020).Significance.The proposed ECGDet can automatically detect premature heartbeats without manually extracting the features. This technique can be used for long-term ECG signal analysis and has potential for clinical applications.
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Affiliation(s)
- Jingying Yang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Wenjie Cai
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Mingjie Wang
- Shanghai Key Laboratory of Bioactive Small Molecules, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, People's Republic of China
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Automatic diagnosis of ECG disease based on intelligent simulation modeling. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Behar JA, Liu C, Zigel Y, Laguna P, Clifford GD. Editorial on Remote Health Monitoring: from chronic diseases to pandemics. Physiol Meas 2021; 41:100401. [PMID: 33393486 DOI: 10.1088/1361-6579/abbb6d] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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