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Islam MS, Kalmady SV, Hindle A, Sandhu R, Sun W, Sepehrvand N, Greiner R, Kaul P. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications. Can J Cardiol 2024:S0828-282X(24)00523-3. [PMID: 38992812 DOI: 10.1016/j.cjca.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
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Affiliation(s)
- Md Saiful Islam
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sunil Vasu Kalmady
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Padma Kaul
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
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Que W, Bian Y, Chen S, Zhao X, Ji Z, Hu P, Han C, Shi L. Efficient electrocardiogram generation based on cardiac electric vector simulation model. Comput Biol Med 2024; 177:108629. [PMID: 38820778 DOI: 10.1016/j.compbiomed.2024.108629] [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: 01/22/2024] [Revised: 04/27/2024] [Accepted: 05/18/2024] [Indexed: 06/02/2024]
Abstract
This study introduces a novel Cardiac Electric Vector Simulation Model (CEVSM) to address the computational inefficiencies and low fidelity of traditional electrophysiological models in generating electrocardiograms (ECGs). Our approach leverages CEVSM to efficiently produce reliable ECG samples, facilitating data augmentation essential for the computer-aided diagnosis of myocardial infarction (MI). Significantly, experimental results show that our model dramatically reduces computation time compared to conventional models, with the self-adapting regression transformation matrix method (SRTM) providing clear advantages. SRTM not only achieves high fidelity in ECG simulations but also ensures exceptional consistency with the gold standard method, greatly enhancing MI localization accuracy by data augmentation. These advancements highlight the potential of our model to generate dependable ECG training samples, making it highly suitable for data augmentation and significantly advancing the development and validation of intelligent MI diagnostic systems. Furthermore, this study demonstrates the feasibility of applying life system simulations in the training of medical big models.
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Affiliation(s)
- Wenge Que
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yingnan Bian
- School of Logistics, Henan College of Transportation, Zhengzhou, 450000, China.
| | - Shengjie Chen
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Xiliang Zhao
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
| | - Zehua Ji
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Pingge Hu
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Chuang Han
- School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, 100084, China; Beijing National Research Center for Information Science and Technology, Beijing, 100084, China.
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Gong Z, Tang Z, Qin Z, Su X, Choi C. Electrocardiogram identification based on data generative network and non-fiducial data processing. Comput Biol Med 2024; 173:108333. [PMID: 38522250 DOI: 10.1016/j.compbiomed.2024.108333] [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: 01/15/2024] [Revised: 03/01/2024] [Accepted: 03/17/2024] [Indexed: 03/26/2024]
Abstract
Nowadays, the use of biological signals as a criterion for identity recognition has gained increasing attention from various organizations and companies. Therefore, it has become crucial to have a biometric identity recognition method that is fast and accurate. In this paper, we propose a linear electrocardiogram (ECG) data preprocessing algorithm based on Kalman filters for rapid noise data filtering (wavelet transform filtering algorithm). Additionally, we introduce a generative network model called Data Generation Strategy Network (DRCN) based on generative networks. The DRCN is employed to augment training samples for convolutional classification networks, ultimately improving the classification performance of the model. Through the final experiments, our method successfully reduced the average misidentification rate of ECG-based identity recognition to 2.5%, and achieved an average recognition rate of 98.7% for each category, significantly surpassing previous achievements. In the future, this method is expected to be widely applied in the field of ECG-based identity recognition.
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Affiliation(s)
- Ziyang Gong
- Department of Computer Engineering, Gachon University, Seongnam-si, 13120, Republic of Korea.
| | - Zhenyu Tang
- College of Information Science and Engineering, Hohai University, Changzhou, 213200, China.
| | - Zijian Qin
- College of Information Science and Engineering, Hohai University, Changzhou, 213200, China.
| | - Xin Su
- College of Information Science and Engineering, Hohai University, Changzhou, 213200, China.
| | - Chang Choi
- Department of Computer Engineering, Gachon University, Seongnam-si, 13120, Republic of Korea.
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4
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Xing X, Li X, Wei C, Zhang Z, Liu O, Xie S, Chen H, Quan S, Wang C, Yang X, Jiang X, Shuai J. DP-GAN+B: A lightweight generative adversarial network based on depthwise separable convolutions for generating CT volumes. Comput Biol Med 2024; 174:108393. [PMID: 38582001 DOI: 10.1016/j.compbiomed.2024.108393] [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: 02/06/2024] [Revised: 03/17/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
X-rays, commonly used in clinical settings, offer advantages such as low radiation and cost-efficiency. However, their limitation lies in the inability to distinctly visualize overlapping organs. In contrast, Computed Tomography (CT) scans provide a three-dimensional view, overcoming this drawback but at the expense of higher radiation doses and increased costs. Hence, from both the patient's and hospital's standpoints, there is substantial medical and practical value in attempting the reconstruction from two-dimensional X-ray images to three-dimensional CT images. In this paper, we introduce DP-GAN+B as a pioneering approach for transforming two-dimensional frontal and lateral lung X-rays into three-dimensional lung CT volumes. Our method innovatively employs depthwise separable convolutions instead of traditional convolutions and introduces vector and fusion loss for superior performance. Compared to prior models, DP-GAN+B significantly reduces the generator network parameters by 21.104 M and the discriminator network parameters by 10.82 M, resulting in a total reduction of 31.924 M (44.17%). Experimental results demonstrate that our network can effectively generate clinically relevant, high-quality CT images from X-ray data, presenting a promising solution for enhancing diagnostic imaging while mitigating cost and radiation concerns.
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Affiliation(s)
- Xinlong Xing
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325000, China
| | - Xiaosen Li
- School of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, China
| | - Chaoyi Wei
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325000, China
| | - Zhantian Zhang
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325000, China
| | - Ou Liu
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325000, China.
| | - Senmiao Xie
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Haoman Chen
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325000, China
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, China
| | - Cong Wang
- Department of Mathematics and Statistics, Carleton College, 300 N College St, Northfield, MN, 55057, USA
| | - Xin Yang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Xiaoming Jiang
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325000, China
| | - Jianwei Shuai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325000, China.
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Xie J, Stavrakis S, Yao B. Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet. Front Physiol 2024; 15:1362185. [PMID: 38655032 PMCID: PMC11035782 DOI: 10.3389/fphys.2024.1362185] [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: 12/27/2023] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes. Moreover, human error is inevitable, as even experienced medical professionals can overlook or misinterpret subtle signs of AF. As such, it is of critical importance to develop an advanced analytical model that can automatically interpret ECG signals and provide decision support for AF diagnostics. Methods: In this paper, we propose an innovative deep-learning method for automated AF identification using single-lead ECGs. We first extract time-frequency features from ECG signals using continuous wavelet transform (CWT). Second, the convolutional neural networks enhanced with residual learning (ReNet) are employed as the functional approximator to interpret the time-frequency features extracted by CWT. Third, we propose to incorporate a multi-branching structure into the ResNet to address the issue of class imbalance, where normal ECGs significantly outnumber instances of AF in ECG datasets. Results and Discussion: We evaluate the proposed Multi-branching Resnet with CWT (CWT-MB-Resnet) with two ECG datasets, i.e., PhysioNet/CinC challenge 2017 and ECGs obtained from the University of Oklahoma Health Sciences Center (OUHSC). The proposed CWT-MB-Resnet demonstrates robust prediction performance, achieving an F1 score of 0.8865 for the PhysioNet dataset and 0.7369 for the OUHSC dataset. The experimental results signify the model's superior capability in balancing precision and recall, which is a desired attribute for ensuring reliable medical diagnoses.
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Affiliation(s)
- Jianxin Xie
- School of Data Science, University of Virginia, Charlottesville, VA, United States
| | - Stavros Stavrakis
- Health Sciences Center, University of Oklahoma, Oklahoma City, OK, United States
| | - Bing Yao
- Department of Industrial and Systems Engineering, University of Tennessee at Knoxville, Knoxville, TN, United States
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Choi S, Choi K, Yun HK, Kim SH, Choi HH, Park YS, Joo S. Diagnosis of atrial fibrillation based on AI-detected anomalies of ECG segments. Heliyon 2024; 10:e23597. [PMID: 38187293 PMCID: PMC10770559 DOI: 10.1016/j.heliyon.2023.e23597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Early detection of atrial fibrillation (AF) is crucial for its effective management and prevention. Various methods for detecting AF using deep learning (DL) based on supervised learning with a large labeled dataset have a remarkable performance. However, supervised learning has several problems, as it is time-consuming for labeling and has a data dependency problem. Moreover, most of the DL methods do not provide any clinical evidence to physicians regarding the analysis of electrocardiography (ECG) for classification or detection of AF. To address these limitations, in this study, we proposed a novel AF diagnosis system using unsupervised learning for anomaly detection with three segments, PreQ, QRS, and PostS, based on the normal ECG. Two independent datasets, PTB-XL and China, were used in three experiments. We used a long short-term memory (LSTM)-based autoencoder to train the segments of the normal ECG. Based on the threshold of anomaly scores using mean squared error (MSE), it distinguished between normal and AF segments. In Experiment A, the best score was that of PreQ, which detected AF with an AUROC score of 0.96. In Experiment B and C for cross validation of each dataset, the best scores were also of PreQ, with AUROC scores of 0.9 and 0.95, respectively. To verify the significance of the anomaly score in distinguishing between AF and normal segments, we utilized an XG-Boosted model after generating anomaly scores in the three segments. The XG-Boosted model achieved an AUROC score of 0.98 and an F1 score of 0.94. AF detection using DL has been controversial among many physicians. However, our study differentiates itself from previous studies in that we can demonstrate evidence that distinguishes AF from normal segments based on the anomaly score.
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Affiliation(s)
- Sanghoon Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Kyungmin Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Hong Kyun Yun
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Su Hyeon Kim
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Hyeon-Hwa Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Yi-Seul Park
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Segyeong Joo
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
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