HITS : Binarizing physiological time series with deep hashing neural network

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

View graph of relations

Author(s)

  • Zhaoji Fu
  • Guodong Wei
  • Wenrui Zhang
  • Shaofu Du
  • Shenda Hong

Detail(s)

Original languageEnglish
Pages (from-to)23-28
Journal / PublicationPattern Recognition Letters
Volume156
Online published8 Mar 2022
Publication statusPublished - Apr 2022

Abstract

In this paper, we aim to transform numerical physiological time series into binary hash codes, that can be further used for indexing large-scale dataset, or accelerating downstream tasks such as instance-based classification. We propose HITS to learn binary Hash codes from physIological Time Series. HITS first builds a very deep one-dimensional convolutional neural network to learn lower-dimensional representations from raw physiological time series. Then, HITS jointly learns high utility, similarity preserving, and temporal related compact binary codes by corresponding objectives with imposed. Finally, given a new physiological time series, HITS can encode it to a binary hash code. Experiments are performed on two real-world Electrocardiogram and Electroencephalogram datasets. The accuracy of a k-nearest classifier is used to evaluate the quality of codes. HITS outperforms the second-best baseline 7.42% on average of = {1, 2, 4, 8, 16, 32} with code length = 48, while reducing 45.51% searching time than the same length numerical vectors on average of = {16, 24, 32, 48, 64}. HITS consistently achieve higher classification accuracy than compared methods using k-nearest classifier varying different k and code length. Thus, HITS learns better binary hash codes than compared methods.

Research Area(s)

  • Deep hashing, Deep neural network, Physiological time series

Citation Format(s)

HITS : Binarizing physiological time series with deep hashing neural network. / Fu, Zhaoji; Wang, Can; Wei, Guodong; Zhang, Wenrui; Du, Shaofu; Hong, Shenda.

In: Pattern Recognition Letters, Vol. 156, 04.2022, p. 23-28.

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