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Cai J, Song J, Peng B. Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function. Physiol Meas 2024; 45:075002. [PMID: 38936397 DOI: 10.1088/1361-6579/ad5cc0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 06/27/2024] [Indexed: 06/29/2024]
Abstract
Objective.This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data.Approach.We propose a feature fusion neural network enhanced by a dynamic minority-biased batch weighting loss function. This network comprises three specialized branches: the complete ECG data branch for a comprehensive view of ECG signals, the local QRS wave branch for detailed features of the QRS complex, and theRwave information branch to analyzeRwave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network's learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network's ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification.Main results.The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value for Supraventricular ectopic beat were 99.63%, 93.62%, 99.81%, and 92.98%, respectively, and for Fusion beat were 99.76%, 85.56%, 99.87%, and 84.16%, respectively. Under the inter-patient paradigm, these metrics were 96.56%, 89.16%, 96.84%, and 51.99%for Supraventricular ectopic beat, and 96.10%, 77.06%, 96.25%, and 13.92%for Fusion beat, respectively.Significance.This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.
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Affiliation(s)
- Jiajun Cai
- School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, Sichuan, People's Republic of China
| | - Junmei Song
- Department of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, People's Republic of China
- Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, People's Republic of China
| | - Bo Peng
- School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, Sichuan, People's Republic of China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 641400, Sichuan, People's Republic of China
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Zhu W, Zheng L, Cheng A, Qiu L, Chen Y, Wang L. Two-stage detection method of supraventricular and ventricular ectopic beats based on sequential artificial features and heartbeats. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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Scorza R, Jonsson M, Corander JM, Rosenqvist M, Frykman V. Prognostic impact of morphology and duration of premature ventricular contractions in a population without structural heart disease. Ann Noninvasive Electrocardiol 2023:e13067. [PMID: 37326286 DOI: 10.1111/anec.13067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 05/03/2023] [Accepted: 06/02/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND Premature ventricular contractions (PVCs) are a common form of arrhythmia associated with an unfavorable prognosis in patients with structural heart disease. It is unclear whether PVCs site of origin and QRS-width has a prognostic significance in patients without structural heart disease. The aim of this study was to assess the prognostic importance of PVCs morphology and duration in this patient group. METHODS We included 511 consecutive patients without a history of previous heart disease. They were examined with echocardiography and exercise test with normal findings. We categorized the PVCs from a 12 lead ECG according to morphology and width of the QRS-complex and analyzed the outcome in terms of a composite endpoint of total mortality and cardiovascular morbidity. RESULTS During a median follow-up time of 5.3 years, 19 patients (3.5%) died and 61 (11.3%) met the composite outcome. Patients with PVCs originating from the outflow tracts had a significantly lower risk for the composite outcome compared to patients with non-OT-PVCs. Similarly, patients with PVC originating from the right ventricle had a better outcome than patients with left ventricular PCVs. No difference in outcome depending on QRS-width during PVCs was noticed. CONCLUSION In our cohort of consecutively included PVC patients without structural heart disease PVCs from the outflow tracts were associated with a better prognostic outcome than non-OT PVCs; the same was true for right ventricular PVCs when compared to left ventricular ones. The classification of the origin of the PVCs was based on 12-lead ECG morphology. QRS-width during PVC did not seem to have prognostic significance.
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Affiliation(s)
- Raffaele Scorza
- Cardiovascular Unit, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
| | - Martin Jonsson
- Department for Clinical Science and Education, Södersjukhuset, Center for Resuscitation Science, Karolinska Institutet, Stockholm, Sweden
| | - John-Martin Corander
- Cardiovascular Unit, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
| | - Mårten Rosenqvist
- Cardiovascular Unit, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
| | - Viveka Frykman
- Cardiovascular Unit, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
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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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Huang Y, Li H, Yu X. A novel time representation input based on deep learning for ECG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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Tao R, Yu X, Lu J, Wang Y, Lu W, Zhang Z, Li H, Zhou J. A deep learning nomogram of continuous glucose monitoring data for the risk prediction of diabetic retinopathy in type 2 diabetes. Phys Eng Sci Med 2023; 46:813-825. [PMID: 37041318 DOI: 10.1007/s13246-023-01254-3] [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: 09/13/2022] [Accepted: 03/27/2023] [Indexed: 04/13/2023]
Abstract
Continuous glucose monitoring (CGM) data analysis will provide a new perspective to analyze factors related to diabetic retinopathy (DR). However, the problem of visualizing CGM data and automatically predicting the incidence of DR from CGM is still controversial. Here, we explored the feasibility of using CGM profiles to predict DR in type 2 diabetes (T2D) by deep learning approach. This study fused deep learning with a regularized nomogram to construct a novel deep learning nomogram from CGM profiles to identify patients at high risk of DR. Specifically, a deep learning network was employed to mine the nonlinear relationship between CGM profiles and DR. Moreover, a novel nomogram combining CGM deep factors with basic information was established to score the patients' DR risk. This dataset consists of 788 patients belonging to two cohorts: 494 in the training cohort and 294 in the testing cohort. The area under the curve (AUC) values of our deep learning nomogram were 0.82 and 0.80 in the training cohort and testing cohort, respectively. By incorporating basic clinical factors, the deep learning nomogram achieved an AUC of 0.86 in the training cohort and 0.85 in the testing cohort. The calibration plot and decision curve showed that the deep learning nomogram had the potential for clinical application. This analysis method of CGM profiles can be extended to other diabetic complications by further investigation.
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Affiliation(s)
- Rui Tao
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Xia Yu
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China
| | - Yaxin Wang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China
| | - Zhanhu Zhang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Hongru Li
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China.
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Huang Y, Zhang R, Li H, Xia Y, Yu X, Liu S, Yang Y. A multi-label learning prediction model for heart failure in patients with atrial fibrillation based on expert knowledge of disease duration. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04487-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Lee S, Chu Y, Ryu J, Park YJ, Yang S, Koh SB. Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis. Yonsei Med J 2022; 63:S93-S107. [PMID: 35040610 PMCID: PMC8790582 DOI: 10.3349/ymj.2022.63.s93] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 11/27/2022] Open
Abstract
PURPOSE Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. MATERIALS AND METHODS The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. RESULTS A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). CONCLUSION This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.
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Affiliation(s)
- Solam Lee
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Yuseong Chu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Jiseung Ryu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Young Jun Park
- Division of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sejung Yang
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea.
| | - Sang Baek Koh
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
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Murat F, Yildirim O, Talo M, Demir Y, Tan RS, Ciaccio EJ, Acharya UR. Exploring deep features and ECG attributes to detect cardiac rhythm classes. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107473] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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