Gated temporal convolutional neural network and expert features for diagnosing and explaining physiological time series: A case study on heart rates.

Journal: Computer methods and programs in biomedicine
Published Date: Nov 23, 2020

Abstract

BACKGROUND AND OBJECTIVE: Physiological time series are common data sources in many health applications. Mining data from physiological time series is crucial for promoting healthy living and reducing governmental medical expenditure. Recently, research and applications of deep learning methods on physiological time series have developed rapidly because such data can be continuously recorded by smart wristbands or smartwatches. However, existing deep learning methods suffer from excessive model complexity and a lack of explanation. This paper aims to handle these issues.

Authors

  • Shenda Hong
    National Institute of Health Data Science at Peking University, Peking University, 100871 Beijing, China.
  • Can Wang
    School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Zhaoji Fu
    HeartVoice Medical Technology, Hefei, 230027, China; University of Science and Technology of China, Hefei, 230026, China.

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