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Gupta U, Paluru N, Nankani D, Kulkarni K, Awasthi N. A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms. Heliyon 2024; 10:e26787. [PMID: 38562492 PMCID: PMC10982903 DOI: 10.1016/j.heliyon.2024.e26787] [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: 01/22/2024] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.
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Affiliation(s)
- Utkarsh Gupta
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Naveen Paluru
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Deepankar Nankani
- Department of Computer Science and Engineering, Indian Institute of Technology, Guwahati, Assam, 781039, India
| | - Kanchan Kulkarni
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, Pessac, Bordeaux, F-33000, France
- University of Bordeaux, INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, F-33000, France
| | - Navchetan Awasthi
- Faculty of Science, Mathematics and Computer Science, Informatics Institute, University of Amsterdam, Amsterdam, 1090 GH, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, 1081 HV, the Netherlands
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Han S, Jeon W, Gong W, Kwak IY. MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning. BIOLOGY 2023; 12:1291. [PMID: 37887001 PMCID: PMC10604338 DOI: 10.3390/biology12101291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/22/2023] [Accepted: 09/26/2023] [Indexed: 10/28/2023]
Abstract
In this study, we constructed a model to predict abnormal cardiac sounds using a diverse set of auscultation data collected from various auscultation positions. Abnormal heart sounds were identified by extracting features such as peak intervals and noise characteristics during systole and diastole. Instead of using raw signal data, we transformed them into log-mel 2D spectrograms, which were employed as input variables for the CNN model. The advancement of our model involves integrating a deep learning architecture with feature extraction techniques based on existing knowledge of cardiac data. Specifically, we propose a multi-channel-based heart signal processing (MCHeart) scheme, which incorporates our proposed features into the deep learning model. Additionally, we introduce the ReLCNN model by applying residual blocks and MHA mechanisms to the LCNN architecture. By adding murmur features with a smoothing function and training the ReLCNN model, the weighted accuracy of the model increased from 79.6% to 83.6%, showing a performance improvement of approximately 4% point compared to the LCNN baseline model.
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Affiliation(s)
- Soyul Han
- Department of Applied Statistics, Chung-Ang University, Seoul 06974, Republic of Korea;
| | - Woongsun Jeon
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea;
| | - Wuming Gong
- Lillehei Heart Institute, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Il-Youp Kwak
- Department of Applied Statistics, Chung-Ang University, Seoul 06974, Republic of Korea;
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Reyna MA, Sadr N, Perez Alday EA, Gu A, Shah AJ, Robichaux C, Bahrami Rad A, Elola A, Seyedi S, Ansari S, Ghanbari H, Li Q, Sharma A, Clifford GD. Issues in the automated classification of multilead ecgs using heterogeneous labels and populations. Physiol Meas 2022; 43:10.1088/1361-6579/ac79fd. [PMID: 35815673 PMCID: PMC9469795 DOI: 10.1088/1361-6579/ac79fd] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 06/17/2022] [Indexed: 11/12/2022]
Abstract
Objective.The standard twelve-lead electrocardiogram (ECG) is a widely used tool for monitoring cardiac function and diagnosing cardiac disorders. The development of smaller, lower-cost, and easier-to-use ECG devices may improve access to cardiac care in lower-resource environments, but the diagnostic potential of these devices is unclear. This work explores these issues through a public competition: the 2021 PhysioNet Challenge. In addition, we explore the potential for performance boosting through a meta-learning approach.Approach.We sourced 131,149 twelve-lead ECG recordings from ten international sources. We posted 88,253 annotated recordings as public training data and withheld the remaining recordings as hidden validation and test data. We challenged teams to submit containerized, open-source algorithms for diagnosing cardiac abnormalities using various ECG lead combinations, including the code for training their algorithms. We designed and scored the algorithms using an evaluation metric that captures the risks of different misdiagnoses for 30 conditions. After the Challenge, we implemented a semi-consensus voting model on all working algorithms.Main results.A total of 68 teams submitted 1,056 algorithms during the Challenge, providing a variety of automated approaches from both academia and industry. The performance differences across the different lead combinations were smaller than the performance differences across the different test databases, showing that generalizability posed a larger challenge to the algorithms than the choice of ECG leads. A voting model improved performance by 3.5%.Significance.The use of different ECG lead combinations allowed us to assess the diagnostic potential of reduced-lead ECG recordings, and the use of different data sources allowed us to assess the generalizability of the algorithms to diverse institutions and populations. The submission of working, open-source code for both training and testing and the use of a novel evaluation metric improved the reproducibility, generalizability, and applicability of the research conducted during the Challenge.
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Affiliation(s)
- Matthew A Reyna
- Department of Biomedical Informatics, Emory University, United States of America
| | - Nadi Sadr
- Department of Biomedical Informatics, Emory University, United States of America
| | - Erick A Perez Alday
- Department of Biomedical Informatics, Emory University, United States of America
| | - Annie Gu
- Department of Biomedical Informatics, Emory University, United States of America
| | - Amit J Shah
- Department of Epidemiology, Emory University, United States of America
- Department of Medicine, Division of Cardiology, Emory University, United States of America
| | - Chad Robichaux
- Department of Biomedical Informatics, Emory University, United States of America
| | - Ali Bahrami Rad
- Department of Biomedical Informatics, Emory University, United States of America
| | - Andoni Elola
- Department of Biomedical Informatics, Emory University, United States of America
- Department of Electronics Technology, University of the Basque Country, Spain
| | - Salman Seyedi
- Department of Biomedical Informatics, Emory University, United States of America
| | - Sardar Ansari
- Department of Emergency Medicine, University of Michigan, United States of America
| | - Hamid Ghanbari
- Division of Cardiovascular Medicine, University of Michigan, United States of America
| | - Qiao Li
- Department of Biomedical Informatics, Emory University, United States of America
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, United States of America
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology, United States of America
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