https://scholars.lib.ntu.edu.tw/handle/123456789/636759
標題: | Few-shot transfer learning for personalized atrial fibrillation detection using patient-based siamese network with single-lead ECG records | 作者: | Ng, Yiuwai MIN-TSUN LIAO Chen, Ting Li CHIH-KUO LEE CHENG-YING CHOU WEICHUNG WANG |
關鍵字: | Arrhythmia detection | Atrial fibrillation | Deep learning | Electrocardiogram | 公開日期: | 1-十月-2023 | 卷: | 144 | 來源出版物: | Artificial Intelligence in Medicine | 摘要: | The proliferation of wearable devices has allowed the collection of electrocardiogram (ECG) recordings daily to monitor heart rhythm and rate. For example, 24-hour Holter monitors, cardiac patches, and smartwatches are widely used for ECG gathering and application. An automatic atrial fibrillation (AF) detector is required for timely ECG interpretation. Deep learning models can accurately identify AFs if large amounts of annotated data are available for model training. However, it is impractical to request sufficient labels for ECG recordings for an individual patient to train a personalized model. We propose a Siamese-network-based approach for transfer learning to address this issue. A pre-trained Siamese convolutional neural network is created by comparing two labeled ECG segments from the same patient. We sampled 30-second ECG segments with a 50% overlapping window from the ECG recordings of patients in the MIT-BIH Atrial Fibrillation Database. Subsequently, we independently detected the occurrence of AF in each patient in the Long-Term AF Database. By fine-tuning the model with the 1, 3, 5, 7, 9, or 11 ECG segments ranging from 30 to 180 s, our method achieved macro-F1 scores of 96.84%, 96.91%, 96.97%, 97.02%, 97.05%, and 97.07%, respectively. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/636759 | ISSN: | 09333657 | DOI: | 10.1016/j.artmed.2023.102644 |
顯示於: | 生物機電工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。