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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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Vasconcelos L, Martinez BP, Kent M, Ansari S, Ghanbari H, Nenadic I. Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning. J Electrocardiol 2023; 81:201-206. [PMID: 37778217 DOI: 10.1016/j.jelectrocard.2023.09.010] [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: 07/07/2023] [Revised: 09/05/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023]
Abstract
There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching >85% accuracy for various cardiac pathologies. Despite the high accuracy at individual institutions, challenges remain when it comes to multi-center deployment. Transfer learning (TL) is a technique in which a model trained for a specific task is repurposed for another related task, in this case ECG ML model trained at one institution is fine-tuned to be utilized to classify ECGs at another institution. Models trained at one institution, however, might not be generalizable for accurate classification when deployed broadly due to differences in type, time, and sampling rate of traditional ECG acquisition. In this study, we evaluate the performance of time domain (TD) and frequency domain (FD) convolutional neural network (CNN) classification models in an inter-institutional scenario leveraging three different publicly available datasets. The larger PTB-XL ECG dataset was used to initially train TD and FD CNN models for atrial fibrillation (AFIB) classification. The models were then tested on two different data sets, Lobachevsky University Electrocardiography Database (LUDB) and Korea University Medical Center database (KURIAS). The FD model was able to retain most of its performance (>0.81 F1-score), whereas TD was highly affected (<0.53 F1-score) by the dataset variations, even with TL applied. The FD CNN showed superior robustness to cross-institutional variability and has potential for widespread application with no compromise to ECG classification performance.
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Affiliation(s)
| | | | - Madeline Kent
- Division of Cardiology, Henry Ford Hospital, Detroit, MI, USA
| | - Sardar Ansari
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hamid Ghanbari
- Cardiovascular Disease, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ivan Nenadic
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Duke Cardiology, Duke University Medical Center, Durham, NC, USA
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