JRM Vol.14 No.6 pp. 625-632
doi: 10.20965/jrm.2002.p0625


Modeling Heart Rate Variability with a HMM-based Neural Network

Osamu Fukuda*, Yoshihiko Nagata*, Keiko Homma* and Toshio Tsuji**

*National Institute of Advanced Industrial Science and Technology, AIST Tsukuba East, 1-2-1, Namiki, Tsukuba, Ibaraki, 305-8564 Japan

**Department of Artificial Systems Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527 Japan

February 27, 2002
August 20, 2002
December 20, 2002
heart rate variability, mental stress, wavelet transform, recurrent neural network, hidden Markov model

This paper proposes a method of modeling heart rate variability combining wavelet transform with a neural network based on a hidden Markov model. The proposed method has the following features: 1. The wavelet transform is used for feature extraction to extract the local change of heart rate variability in the timefrequency domain. 2. A new recurrent neural network incorporating a hidden Markov model is used to model the different patterns of heart rate variability caused by individual variations, physical conditions and so on. In experiments, five subjects were subjected to a mental workload, and the proposed method was used map subjective rating scores of their mental stress and the pattern of heart rate variability. Experiments confirmed that the proposed method achieved highly accurate modeling.

Cite this article as:
Osamu Fukuda, Yoshihiko Nagata, Keiko Homma, and Toshio Tsuji, “Modeling Heart Rate Variability with a HMM-based Neural Network,” J. Robot. Mechatron., Vol.14, No.6, pp. 625-632, 2002.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Feb. 25, 2021