1
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Zhu H, Jiang N, Xia S, Tong J. Atrial Fibrillation Prediction Based on Recurrence Plot and ResNet. SENSORS (BASEL, SWITZERLAND) 2024; 24:4978. [PMID: 39124025 PMCID: PMC11314825 DOI: 10.3390/s24154978] [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: 06/28/2024] [Revised: 07/27/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024]
Abstract
Atrial fibrillation (AF) is the most prevalent form of arrhythmia, with a rising incidence and prevalence worldwide, posing significant implications for public health. In this paper, we introduce an approach that combines the Recurrence Plot (RP) technique and the ResNet architecture to predict AF. Our method involves three main steps: using wavelet filtering to remove noise interference; generating RPs through phase space reconstruction; and employing a multi-level chained residual network for AF prediction. To validate our approach, we established a comprehensive database consisting of electrocardiogram (ECG) recordings from 1008 AF patients and 48,292 Non-AF patients, with a total of 2067 and 93,129 ECGs, respectively. The experimental results demonstrated high levels of prediction precision (90.5%), recall (89.1%), F1 score (89.8%), accuracy (93.4%), and AUC (96%) on our dataset. Moreover, when tested on a publicly available AF dataset (AFPDB), our method achieved even higher prediction precision (94.8%), recall (99.4%), F1 score (97.0%), accuracy (97.0%), and AUC (99.7%). These findings suggest that our proposed method can effectively extract subtle information from ECG signals, leading to highly accurate AF predictions.
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Affiliation(s)
- Haihang Zhu
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.Z.); (N.J.)
| | - Nan Jiang
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.Z.); (N.J.)
| | - Shudong Xia
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Jinhua 321000, China;
| | - Jijun Tong
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.Z.); (N.J.)
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2
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Clancy CE, Santana LF. Advances in induced pluripotent stem cell-derived cardiac myocytes: technological breakthroughs, key discoveries and new applications. J Physiol 2024; 602:3871-3892. [PMID: 39032073 PMCID: PMC11326976 DOI: 10.1113/jp282562] [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: 02/21/2024] [Accepted: 07/02/2024] [Indexed: 07/22/2024] Open
Abstract
A transformation is underway in precision and patient-specific medicine. Rapid progress has been enabled by multiple new technologies including induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). Here, we delve into these advancements and their future promise, focusing on the efficiency of reprogramming techniques, the fidelity of differentiation into the cardiac lineage, the functional characterization of the resulting cardiac myocytes, and the many applications of in silico models to understand general and patient-specific mechanisms controlling excitation-contraction coupling in health and disease. Furthermore, we explore the current and potential applications of iPSC-CMs in both research and clinical settings, underscoring the far-reaching implications of this rapidly evolving field.
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Affiliation(s)
- Colleen E Clancy
- Department of Physiology & Membrane Biology, School of Medicine, University of California Davis, Davis, CA, USA
- Center for Precision Medicine and Data Sciences, University of California Davis, School of Medicine, Sacramento, CA, USA
| | - L Fernando Santana
- Department of Physiology & Membrane Biology, School of Medicine, University of California Davis, Davis, CA, USA
- Center for Precision Medicine and Data Sciences, University of California Davis, School of Medicine, Sacramento, CA, USA
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3
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Liang H, Zhang H, Wang J, Shao X, Wu S, Lyu S, Xu W, Wang L, Tan J, Wang J, Yang Y. The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment. Rev Cardiovasc Med 2024; 25:257. [PMID: 39139434 PMCID: PMC11317345 DOI: 10.31083/j.rcm2507257] [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: 10/17/2023] [Revised: 01/16/2024] [Accepted: 01/26/2024] [Indexed: 08/15/2024] Open
Abstract
Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for AF have been updated in recent years, its gradual onset and associated risk of stroke pose challenges for both patients and cardiologists in real-world practice. Artificial intelligence (AI) is a powerful tool in image analysis, data processing, and for establishing models. It has been widely applied in various medical fields, including AF. In this review, we focus on the progress and knowledge gap regarding the use of AI in AF patients and highlight its potential throughout the entire cycle of AF management, from detection to drug treatment. More evidence is needed to demonstrate its ability to improve prognosis through high-quality randomized controlled trials.
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Affiliation(s)
- Hanyang Liang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Han Zhang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Juan Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Xinghui Shao
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Shuang Wu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Siqi Lyu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Wei Xu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Lulu Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Jiangshan Tan
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Jingyang Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Yanmin Yang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
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4
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Glaser K, Marino L, Stubnya JD, Bilotta F. Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review. J Anesth 2024; 38:301-308. [PMID: 38594589 PMCID: PMC11096200 DOI: 10.1007/s00540-024-03316-6] [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: 11/07/2023] [Accepted: 02/04/2024] [Indexed: 04/11/2024]
Abstract
Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for prediction and detection poses a challenge. This study aims to provide a comprehensive literature review on the application of machine learning (ML) algorithms for predicting and detecting new-onset atrial fibrillation (NOAF) in ICU-treated patients. Following the PRISMA recommendations, this systematic review outlines ML models employed in the prediction and detection of NOAF in ICU patients and compares the ML-based approach with clinical-based methods. Inclusion criteria comprised randomized controlled trials (RCTs), observational studies, cohort studies, and case-control studies. A total of five articles published between November 2020 and April 2023 were identified and reviewed to extract the algorithms and performance metrics. Reviewed studies sourced 108,724 ICU admission records form databases, e.g., MIMIC. Eight prediction and detection methods were examined. Notably, CatBoost exhibited superior performance in NOAF prediction, while the support vector machine excelled in NOAF detection. Machine learning algorithms emerge as promising tools for predicting and detecting NOAF in ICU patients. The incorporation of these algorithms in clinical practice has the potential to enhance decision-making and the overall management of NOAF in ICU settings.
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Affiliation(s)
- Krzysztof Glaser
- Department of Anaesthesiology, Critical Care and Pain Medicine, Policlinico Umberto I,, Sapienza University of Rome, 00185, Rome, Italy.
| | - Luca Marino
- Department of Mechanical and Aerospace Engineering, Policlinico Umberto I, Sapienza University of Rome, 00185, Rome, Italy
| | | | - Federico Bilotta
- Department of Anaesthesiology, Critical Care and Pain Medicine, Policlinico Umberto I,, Sapienza University of Rome, 00185, Rome, Italy
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5
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Halvaei H, Hygrell T, Svennberg E, Corino VD, Sörnmo L, Stridh M. Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:480-487. [PMID: 38899146 PMCID: PMC11186645 DOI: 10.1109/jtehm.2024.3397739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/20/2024] [Accepted: 04/30/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVE Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT. METHODS The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable. RESULTS The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. CONCLUSION The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector.Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.
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Affiliation(s)
- Hesam Halvaei
- Department of Biomedical EngineeringLund University221 00LundSweden
| | - Tove Hygrell
- Department of MedicineKarolinska Institutet171 77StockholmSweden
| | - Emma Svennberg
- Department of MedicineKarolinska Institutet171 77StockholmSweden
| | - Valentina D.A. Corino
- Department of ElectronicsInformation and Bioengineering (DEIB)Politecnico di MilanoMilan20133Italy
- CardioTech LaboratoryIRCCS Centro Cardiologico MonzinoMilan20138Italy
| | - Leif Sörnmo
- Department of Biomedical EngineeringLund University221 00LundSweden
| | - Martin Stridh
- Department of Biomedical EngineeringLund University221 00LundSweden
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6
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Weidlich S, Mannhart D, Kennedy A, Doggart P, Serban T, Knecht S, Du Fay de Lavallaz J, Kühne M, Sticherling C, Badertscher P. Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:29-35. [PMID: 38390580 PMCID: PMC10879015 DOI: 10.1016/j.cvdhj.2023.12.003] [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] [Indexed: 02/24/2024] Open
Abstract
Background Multiple smart devices capable of automatically detecting atrial fibrillation (AF) based on single-lead electrocardiograms (SL-ECG) are presently available. The rate of inconclusive tracings by manufacturers' algorithms is currently too high to be clinically useful. Method This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart device artificial intelligence (AI)-based algorithm for detecting AF from 4 commercially available smart devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12-lead ECG and 4 smart device SL-ECGs. The novel AI algorithm (PulseAI, Belfast, United Kingdom) was compared with each manufacturer's algorithm. Results We enrolled 206 patients (31% female, median age 64 years). AF was present in 60 patients (29%). Sensitivity and specificity for the detection of AF by the novel AI algorithm vs manufacturer algorithm were 88% vs 81% (P = .34) and 97% vs 77% (P < .001) for the AliveCor KardiaMobile, 86% vs 81% (P = .45) and 95% vs 83% (P < .001) for the Apple Watch 6, 91% vs 67% (P < .01) and 94% vs 82% (P < .001) for the Fitbit Sense, and 86% vs 82% (P = .63) and 94% vs 80% (P < .001) for the Samsung Galaxy Watch3, respectively. In addition, the proportion of SL-ECGs with an inconclusive diagnosis (1.2%) was significantly lower for all smart devices using the AI-based algorithm compared to manufacturer's algorithms (14%-17%), P < .001. Conclusion A novel AI algorithm reduced the rate of inconclusive SL-ECG diagnosis massively while maintaining sensitivity and improving the specificity compared to the manufacturers' algorithms.
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Affiliation(s)
- Simon Weidlich
- University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel (CRIB), Basel, Switzerland
| | - Diego Mannhart
- University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel (CRIB), Basel, Switzerland
| | | | | | - Teodor Serban
- University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel (CRIB), Basel, Switzerland
| | - Sven Knecht
- University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel (CRIB), Basel, Switzerland
| | - Jeanne Du Fay de Lavallaz
- University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel (CRIB), Basel, Switzerland
| | - Michael Kühne
- University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel (CRIB), Basel, Switzerland
| | - Christian Sticherling
- University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel (CRIB), Basel, Switzerland
| | - Patrick Badertscher
- University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel (CRIB), Basel, Switzerland
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7
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Zhao X, Zhou R, Ning L, Guo Q, Liang Y, Yang J. Atrial Fibrillation Detection with Single-Lead Electrocardiogram Based on Temporal Convolutional Network-ResNet. SENSORS (BASEL, SWITZERLAND) 2024; 24:398. [PMID: 38257491 PMCID: PMC10820095 DOI: 10.3390/s24020398] [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: 11/15/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Abstract
Atrial fibrillation, one of the most common persistent cardiac arrhythmias globally, is known for its rapid and irregular atrial rhythms. This study integrates the temporal convolutional network (TCN) and residual network (ResNet) frameworks to effectively classify atrial fibrillation in single-lead ECGs, thereby enhancing the application of neural networks in this field. Our model demonstrated significant success in detecting atrial fibrillation, with experimental results showing an accuracy rate of 97% and an F1 score of 87%. These figures indicate the model's exceptional performance in identifying both majority and minority classes, reflecting its balanced and accurate classification capability. This research offers new perspectives and tools for diagnosis and treatment in cardiology, grounded in advanced neural network technology.
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Affiliation(s)
- Xiangyu Zhao
- ShenSi Lab, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu 518110, China; (X.Z.); (R.Z.); (L.N.); (Q.G.)
| | - Rong Zhou
- ShenSi Lab, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu 518110, China; (X.Z.); (R.Z.); (L.N.); (Q.G.)
- National Supercomputing Center in Shenzhen, Shenzhen 518005, China
| | - Li Ning
- ShenSi Lab, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu 518110, China; (X.Z.); (R.Z.); (L.N.); (Q.G.)
| | - Qiuquan Guo
- ShenSi Lab, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu 518110, China; (X.Z.); (R.Z.); (L.N.); (Q.G.)
| | - Yan Liang
- ShenSi Lab, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu 518110, China; (X.Z.); (R.Z.); (L.N.); (Q.G.)
- School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jun Yang
- ShenSi Lab, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu 518110, China; (X.Z.); (R.Z.); (L.N.); (Q.G.)
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8
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Li F, Wang P, Wang Li X. Deep learning-based regional ECG diagnosis platform. Pacing Clin Electrophysiol 2024; 47:139-148. [PMID: 38029363 DOI: 10.1111/pace.14891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 11/06/2023] [Accepted: 11/15/2023] [Indexed: 12/01/2023]
Abstract
OBJECTIVE To enable the intelligent diagnosis of a variety of common Electrocardiogram (ECG), we investigate the deep learning-based ECG diagnosis system. METHODS From January 2015 to December 2019, four consecutive years of 100,120 conventional 12-lead ECG data were collected in our hospital. Utilizing this dataset, we constructed a deep learning model designed to intelligently diagnose prevalent ECG anomalies by employing a multi-task learning framework. The system performance was evaluated using various metrics, including sensitivity, specificity, negative predictive value, positive predictive value, and so forth. Additionally, we employed an ECG intelligent diagnostic platform for clinical application to undertake real-time online analysis of 2500 conventional 12-lead ECG samples in June 2020, aiming to validate our model. At this stage, we compared the performance of our model against the traditional manual identification method. RESULTS The efficacy of the ECG intelligent diagnostic model was notably high for common and straightforward ECG patterns, such as sinus rhythm (F1 = 98.01%), sinus tachycardia (F1 = 96.26%), sinus bradycardia (F1 = 94.88%), and a normal electrocardiogram (F1 = 91.71%), as well as for Premature Ventricular Contractions (F1 = 91.62%). Nevertheless, when diagnosing rarer and more intricate ECG anomalies, the system requires an increased number of samples to refine the deep learning models. During the validation stage, our model exhibited better efficiency in terms of accuracy, labor time and labor cost when compared to the manual identification approach. CONCLUSIONS Our deep learning-driven intelligent ECG diagnostic model clearly demonstrates significant clinical utility. The integrated artificial intelligence diagnosis system not only has the potential to augment physicians in their diagnostic processes but also offers a viable avenue to reduce associated labor costs.
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Affiliation(s)
- Fang Li
- Department of Cardiology, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang Province, China
| | - Ping Wang
- Department of Cardiology, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang Province, China
| | - Xiao Wang Li
- Department of Cardiology, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang Province, China
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9
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Neri L, Oberdier MT, van Abeelen KCJ, Menghini L, Tumarkin E, Tripathi H, Jaipalli S, Orro A, Paolocci N, Gallelli I, Dall’Olio M, Beker A, Carrick RT, Borghi C, Halperin HR. Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4805. [PMID: 37430719 PMCID: PMC10223364 DOI: 10.3390/s23104805] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.
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Affiliation(s)
- Luca Neri
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Matt T. Oberdier
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Kirsten C. J. van Abeelen
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Internal Medicine, Radboud University Medical Center, 6525 AJ Nijmegen, The Netherlands
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Ethan Tumarkin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Hemantkumar Tripathi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Sujai Jaipalli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alessandro Orro
- Institute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy
| | - Nazareno Paolocci
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Ilaria Gallelli
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Massimo Dall’Olio
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Amir Beker
- AccYouRate Group S.p.A., 67100 L’Aquila, Italy
| | - Richard T. Carrick
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Henry R. Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA
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10
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Budaraju D, Neelapu BC, Pal K, Jayaraman S. Stacked machine learning models to classify atrial disorders based on clinical ECG features: a method to predict early atrial fibrillation. BIOMED ENG-BIOMED TE 2023:bmt-2022-0430. [PMID: 36963433 DOI: 10.1515/bmt-2022-0430] [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: 09/10/2022] [Accepted: 02/20/2023] [Indexed: 03/26/2023]
Abstract
OBJECTIVES Atrial Tachycardia (AT) and Left Atrial Enlargement (LAE) are atrial diseases that are significant precursors to Atrial Fibrillation (AF). There are ML models for ECG classification; clinical features-based classification is required. The suggested work aims to create stacked ML models that categorize Sinus Rhythm (SR), Sinus Tachycardia (ST), AT, and LAE signals based on clinical parameters for AF prognosis. METHODS The classification was based on thirteen clinical parameters, such as amplitude, time domain ECG aspects, and P-Wave Indices (PWI), such as the ratio of P-wave length and amplitude ((P (ms)/P (µV)), P-wave area (µV*ms), and P-wave terminal force (PTFV1(µV*ms). Apart from classifying the ECG signals, the stacked ML models prioritized the clinical features using a pie formula-based technique. RESULTS The Stack 1 model achieves 99% accuracy, sensitivity, precision, and F1 score, while the Stack 2 model achieves 91%, 91%, 94%, and 92% for identifying SR, ST, LAE, and AT, respectively. Both stack models obtained a computational time of 0.06 seconds. PTFV1 (µV*ms), P (ms)/P (µV)), and P-wave area (µV*ms) were ranked as crucial clinical features. CONCLUSION Clinical feature-based stacking ML models may help doctors obtain insight into important clinical ECG aspects for early AF prediction.
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Affiliation(s)
- Dhananjay Budaraju
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India
| | - Bala Chakravarthy Neelapu
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India
| | - Kunal Pal
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India
| | - Sivaraman Jayaraman
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India
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11
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Zhang Z, Zhang Z, Zou C, Pei Z, Yang Z, Wu J, Sun S, Gu F. ECGNet: An Efficient Network for Detecting Premature Ventricular Complexes Based on ECG Images. IEEE Trans Biomed Eng 2023; 70:446-458. [PMID: 35881595 DOI: 10.1109/tbme.2022.3193906] [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: 11/10/2022]
Abstract
BACKGROUND Preoperative prediction of the origin site of premature ventricular complexes (PVCs) is critical for the success of operations. However, current methods are not efficient or accurate enough. In addition, among the proposed strategies, there are few good prediction methods for electrocardiogram (ECG) images combined with deep learning aspects. METHODS We propose ECGNet, a new neural network for the classification of 12-lead ECG images. In ECGNet, 609 ECG images from 310 patients who had undergone successful surgery in the Division of Cardiology, the First Affiliated Hospital of Soochow University, are utilized to construct the dataset. We adopt dense blocks, special convolution kernels and divergent paths to improve the performance of ECGNet. In addition, a new loss function is designed to address the sample imbalance situation, whose cause is the uneven distribution of cases themselves, which often occurs in the medical field. We also conduct extensive experiments in terms of network prediction accuracy to compare ECGNet with other networks, such as ResNet and DarkNet. RESULTS Our ECGNet achieves extremely high prediction accuracy (91.74%) and efficiency with very small datasets. Our newly proposed loss function can solve the problem of sample imbalance during the training process. CONCLUSION The proposed ECGNet can quickly and accurately realize the multiclassification of PVCs after training with little data. Our network has the potential to be helpful to doctors with a preoperative diagnosis of PVCs. We will continue to collect similar cases and perfect our network structure to further improve the accuracy of our network's prediction.
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Hajeb-Mohammadalipour S, Hossain MB, Chon KH. Improving the Accuracy of R-Peak Detection in a Wearable Armband Device for Daily Life Electrocardiogram Monitoring Using a Deep Convolutional Denoising Encoder-Decoder Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4291-4294. [PMID: 36085851 DOI: 10.1109/embc48229.2022.9871609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Continuous long-term heart rate (HR) monitoring using wearable devices is desirable to aid in the diagnosis of many health-related conditions. Recently, we have developed an armband device that does not use obstructive leads, has dry electrodes which are convenient for long-term electrocardiogram (ECG) recording, and has been shown to be an effective alternate approach for continuous ECG monitoring. However, motion artifacts (MA) due to electromyogram (EMG) contractions are acknowledged as the major challenge of an armband. In this study, we used a deep convolutional neural network denoising encoder-decoder (CNNDED) to enhance the accuracy of R-peak detection in MA-corrupted ECG recordings obtained by an armband device. We collected simultaneous 24-hour ECG recordings using both the armband device and a Holter monitor on 10 subjects. Each 10-sec ECG segment was converted to a time-frequency representation and subsequently used as the input to CNNDED. During the training process, the model learned to accentuate the location of R peaks by amplifying their values in each ECG beat and suppressing the remaining waveforms. For the training output, the model used the R-peak location information from the simultaneously collected Holter ECG data, which were considered as the reference. The performance of CNNDED was evaluated on an independent test data set using the standard performance metrics. The mean relative error of the estimated HR with respect to the Holter data was 17.5 and 7.3 beats/min, pre- and post-CNNDED, respectively. The mean relative difference of the root mean square of successive difference values were 0.23 and 0.06 before and after applying CNNDED, respectively. Although further study is needed, the current preliminary results suggest that CNNDED can improve detection of R peaks even when they are completely buried in the presence of EMG artifacts.
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Flanders WH, Moïse NS, Pariaut R, Sargent J. The next heartbeat: creating dynamic and histographic Poincaré plots for the assessment of cardiac rhythms. J Vet Cardiol 2022; 42:1-13. [DOI: 10.1016/j.jvc.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 04/19/2022] [Accepted: 04/28/2022] [Indexed: 10/18/2022]
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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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Huang YH, Lyle JV, Razak ASA, Nandi M, Marr CM, Huang CLH, Aston PJ, Jeevaratnam K. Detecting Paroxysmal Atrial Fibrillation from Normal Sinus Rhythm in Equine Athletes using Symmetric Projection Attractor Reconstruction and Machine Learning. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:96-106. [PMID: 35493267 PMCID: PMC9043370 DOI: 10.1016/j.cvdhj.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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16
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Naseri Jahfari A, Tax D, Reinders M, van der Bilt I. Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View. JMIR Med Inform 2022; 10:e29434. [PMID: 35044316 PMCID: PMC8811688 DOI: 10.2196/29434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/22/2021] [Accepted: 12/04/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. OBJECTIVE This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. METHODS We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as "wearables," "machine learning," and "cardiovascular disease." Methodologies were categorized and analyzed according to machine learning-based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). RESULTS After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. CONCLUSIONS Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.
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Affiliation(s)
- Arman Naseri Jahfari
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
- Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
| | - David Tax
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
| | - Marcel Reinders
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
| | - Ivo van der Bilt
- Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
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Guo CY, Wang KJ, Hsieh TL. Piezoelectric Sensor for the Monitoring of Arterial Pulse Wave: Detection of Arrhythmia Occurring in PAC/PVC Patients. SENSORS 2021; 21:s21206915. [PMID: 34696128 PMCID: PMC8540434 DOI: 10.3390/s21206915] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/09/2021] [Accepted: 10/15/2021] [Indexed: 01/23/2023]
Abstract
Previous studies have found that the non-invasive blood pressure measurement method based on the oscillometric method is inaccurate when an arrhythmia occurs. Therefore, we propose a high-sensitivity pulse sensor that can measure the hemodynamic characteristics of the pulse wave and then estimate the blood pressure. When an arrhythmia occurs, the hemodynamics of the pulse wave are abnormal and change the morphology of the pulse wave. Our proposed sensor can measure the occurrence of ectopic beats from the radial artery, and the detection algorithm can reduce the error of blood pressure estimation caused by the distortion of ectopic beats that occurs when the pulse wave is measured. In this study, we tested patients with premature atrial contraction (PAC) or premature ventricular contraction (PVC) and analyzed the morphology of the pulse waves when the sensor detected the ectopic beats. We discuss the advantages of using the Moens–Korteweg equation to estimate the blood pressure of patients with arrhythmia, which is different from the oscillometric method. Our research provides a possible arrhythmia detection method for wearable devices and can accurately estimate blood pressure in a non-invasive way during an arrhythmia.
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Affiliation(s)
- Cheng-Yan Guo
- College of Medicine, National Taiwan University, Taipei 10617, Taiwan;
| | - Kuan-Jen Wang
- Accurate Meditech Inc., New Taipei City 24159, Taiwan;
| | - Tung-Li Hsieh
- General Education Center, Ursuline College of Liberal Arts Education, Wenzao Ursuline University of Languages, Kaohsiung 80793, Taiwan
- Correspondence: ; Tel.: +886-7-342-6031 (ext. 7226)
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Li H, An Z, Zuo S, Zhu W, Zhang Z, Zhang S, Zhang C, Song W, Mao Q, Mu Y, Li E, García JDP. Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG. SENSORS 2021; 21:s21186043. [PMID: 34577248 PMCID: PMC8472929 DOI: 10.3390/s21186043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/07/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022]
Abstract
Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%.
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Affiliation(s)
- Hongqiang Li
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China; (Z.A.); (S.Z.); (C.Z.); (W.S.); (Q.M.); (Y.M.)
- Correspondence:
| | - Zhixuan An
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China; (Z.A.); (S.Z.); (C.Z.); (W.S.); (Q.M.); (Y.M.)
| | - Shasha Zuo
- Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin 300192, China; (S.Z.); (W.Z.)
| | - Wei Zhu
- Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin 300192, China; (S.Z.); (W.Z.)
| | - Zhen Zhang
- School of Computer Science and Technology, Tiangong University, Tianjin 300387, China;
| | - Shanshan Zhang
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China; (Z.A.); (S.Z.); (C.Z.); (W.S.); (Q.M.); (Y.M.)
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Institute of Modern Optics, Nankai University, Tianjin 300071, China
| | - Cheng Zhang
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China; (Z.A.); (S.Z.); (C.Z.); (W.S.); (Q.M.); (Y.M.)
| | - Wenchao Song
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China; (Z.A.); (S.Z.); (C.Z.); (W.S.); (Q.M.); (Y.M.)
| | - Quanhua Mao
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China; (Z.A.); (S.Z.); (C.Z.); (W.S.); (Q.M.); (Y.M.)
| | - Yuxin Mu
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China; (Z.A.); (S.Z.); (C.Z.); (W.S.); (Q.M.); (Y.M.)
| | - Enbang Li
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia;
| | - Juan Daniel Prades García
- Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona (UB), E-08028 Barcelona, Spain;
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Feasibility of atrial fibrillation detection from a novel wearable armband device. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 2:179-191. [PMID: 35265907 PMCID: PMC8890073 DOI: 10.1016/j.cvdhj.2021.05.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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20
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Rouhi R, Clausel M, Oster J, Lauer F. An Interpretable Hand-Crafted Feature-Based Model for Atrial Fibrillation Detection. Front Physiol 2021; 12:657304. [PMID: 34054575 PMCID: PMC8155476 DOI: 10.3389/fphys.2021.657304] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/07/2021] [Indexed: 11/21/2022] Open
Abstract
Atrial Fibrillation (AF) is the most common type of cardiac arrhythmia. Early diagnosis of AF helps to improve therapy and prognosis. Machine Learning (ML) has been successfully applied to improve the effectiveness of Computer-Aided Diagnosis (CADx) systems for AF detection. Presenting an explanation for the decision made by an ML model is considerable from the cardiologists' point of view, which decreases the complexity of the ML model and can provide tangible information in their diagnosis. In this paper, a range of explanation techniques is applied to hand-crafted features based ML models for heart rhythm classification. We validate the impact of the techniques by applying feature selection and classification to the 2017 CinC/PhysioNet challenge dataset. The results show the effectiveness and efficiency of SHapley Additive exPlanations (SHAP) technique along with Random Forest (RF) for the classification of the Electrocardiogram (ECG) signals for AF detection with a mean F-score of 0.746 compared to 0.706 for a technique based on the same features based on a cascaded SVM approach. The study also highlights how this interpretable hand-crafted feature-based model can provide cardiologists with a more compact set of features and tangible information in their diagnosis.
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Affiliation(s)
| | | | - Julien Oster
- IADI U1254, Inserm and Université de Lorraine, Nancy, France
| | - Fabien Lauer
- Université de Lorraine, CNRS, LORIA, Nancy, France
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21
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Parsi A, Glavin M, Jones E, Byrne D. Prediction of paroxysmal atrial fibrillation using new heart rate variability features. Comput Biol Med 2021; 133:104367. [PMID: 33866252 DOI: 10.1016/j.compbiomed.2021.104367] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/09/2021] [Accepted: 03/29/2021] [Indexed: 02/01/2023]
Abstract
Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia that can eventually lead to heart failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any further complications and avoid fatalities. An implantable defibrillator device could be used to both detect and treat the condition though such devices have limited computational capability. With this constraint in mind, this paper presents a novel set of features to accurately predict the presence of PAF. The method is evaluated using ECG signals from the widely used atrial fibrillation prediction database (AFPDB) from PhysioNet. We analysed 106 signals from 53 pairs of ECG recordings. Each pair of signals contains one 5-min ECG segment that ends just before the onset of a PAF event and another 5-min ECG segment at least 45 min distant from the PAF event, to represent a non-PAF event. Seven novel features are extracted through the Poincaré representation of R-R interval signals, and are prioritised through feature ranking schemes. The features are used with four standard classification techniques for PAF prediction and compared to the existing state of the art from the literature. Using only the seven proposed features, classification performance outperforms those of the classical state-of-the-art feature set, registering sensitivity and specificity measurements of over 96%. The results further improve when the features are combined with several of the classical features, with an accuracy increasing to 98% using a linear kernel SVM. The results show that the proposed features provide a useful representation of the PAF condition and achieve good prediction with off-the-shelf classification techniques that would be suitable for ICU deployment.
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Affiliation(s)
- Ashkan Parsi
- National University of Ireland (NUI) Galway, Galway, H91 TK33, Ireland.
| | - Martin Glavin
- National University of Ireland (NUI) Galway, Galway, H91 TK33, Ireland.
| | - Edward Jones
- National University of Ireland (NUI) Galway, Galway, H91 TK33, Ireland.
| | - Dallan Byrne
- National University of Ireland (NUI) Galway, Galway, H91 TK33, Ireland.
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