期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 22, 期 6, 页码 1744-1753出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2858789
关键词
Deep convolutional neural network; ECG; atrial fibrillation; deep learning; classification; biomedical monitoring
类别
资金
- Major Special Project of Guangdong Province [2017B030308007]
- Shenzhen Basic Research Projects [JCYJ20170818163445670, JCYJ20170413161515911, JCYJ20170412110753954]
- Guangdong Applied Special Project for Research and Development [2015B010129012]
- Special Fund Project for Overseas High-level Talents Innovation and Entrepreneurship [KQJSCX20170731165939298]
Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly population, associated with a high mortality and morbidity in stroke, heart failure, coronary artery disease, systemic thromboembolism, etc. The early detection of AF is necessary for averting the possibility of disability or mortality. However, AF detection remains problematic due to its episodic pattern. In this paper, a multiscaled fusion of deep convolutional neural network (MS-CNN) is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The MS-CNN employs the architecture of two-stream convolutional networks with different filter sizes to capture features of different scales. The experimental results show that the proposed MS-CNN achieves 96.99% of classification accuracy on ECG recordings cropped/padded to 5 s. Especially, the best classification accuracy, 98.13%, is obtained on ECG recordings of 20 s. Compared with artificial neural network, shallow single-stream CNN, and Visual-Geometry group network, the MS-CNN can achieve the better classification performance. Meanwhile, visualization of the learned features from the MS-CNN demonstrates its superiority in extracting linear separable ECG features without hand-craft feature engineering. The excellent AF screening performance of the MS-CNN can satisfy the most elders for daily monitoring with wearable devices.
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