出版社
IEEE
DOI: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00131
关键词
convolutional neural network; recurrent neural network; arrhythmia detection; varied-length signal; real-time processing
类别
资金
- Shenzhen Basic Research fund [JCYJ20150630114942270]
- Major Special Project of Guangdong Province [2017B030308007]
- Basic Research discipline Planning in Shenzhen [JCYJ20170413161515911]
- National Natural Science Fund [61771465]
- Shenzhen Engineering Laboratory for Analysis and Application of Health big data
- Central authorities guide local special funds for scientific and technological development
Automatic arrhythmia detection plays an important role in early prevention and diagnosis of cardiovascular diseases. Convolutional neural network (CNN) introduced a simple, end-to-end solution to multi-class arrhythmia classification, but the restriction that it could only accept fixed-length input resulted in noises or key information losses in training. Meanwhile, CNN's high memory consumption and computation cost also limited its application. To address these issues, we proposed a time-incremental convolutional neural network (TI-CNN), which utilized recurrent cells to introduce flexibility in input length for CNN models, and featured halved parameter amount as well as more than 90% computation reduction in real-time processing. The experiment results showed that, TI-CNN reached an overall classification accuracy of 77.3%. In comparison with a classical 16-layer CNN named VGGNet, TI-CNN achieved accuracy increases of more than 6% in average and up to 22% in detecting paroxysmal arrhythmias. Combining all these excellent features, TI-CNN offered an exemplification for all kinds of varied-length signal processing problems.
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