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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Shenda Hong
  • Can Wang
  • Zhaoji Fu

Detail(s)

Original languageEnglish
Article number105847
Journal / PublicationComputer Methods and Programs in Biomedicine
Volume200
Online published23 Nov 2020
Publication statusPublished - Mar 2021

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. Methods: We propose TEG-net, which is a novel deep learning method for accurately diagnosing and explaining physiological time series. TEG-net constructs T-net (a multi-scale bi-directional temporal convolutional neural network) to model physiological time series directly, E-net (personalized linear model) to model expert features extracted from physiological time series, and G-net (gating neural network) to combine T-net and E-net for diagnosis. The combination of T-net and E-net through G-net improves diagnosis accuracy and E-net can be utilized for explanation. Results: Experimental results demonstrate that TEG-net outperforms the second-best baseline by 13.68% in terms of area under the receiver operating characteristic curve and 11.49% in terms of area under the precision-recall curve. Additionally, intuitive justifications can be provided to explain model predictions. Conclusions: This paper develops an ensemble method to combine expert features and deep learning method for modeling physiological time series. Improvements in diagnostic accuracy and explanation make TEG-net applicable to many real-world health applications.

Research Area(s)

  • Deep neural network, Ensemble method, Expert features, Physiological time series

Citation Format(s)

Gated temporal convolutional neural network and expert features for diagnosing and explaining physiological time series : A case study on heart rates. / Hong, Shenda; Wang, Can; Fu, Zhaoji.

In: Computer Methods and Programs in Biomedicine, Vol. 200, 105847, 03.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review