Evaluation of heart rate variability by using wavelet transform and a recurrent neural network

The purpose of this paper is to evaluate the physical and mental stress based on the physiological index, and a new evaluation method of heart rate variability is proposed. This method combines the wavelet transform with a recurrent neural network. The features of the proposed method are as follows: 1. The wavelet transform is utilized for the feature extraction so that the local change of heart rate variability in the time-frequency domain can be extracted. 2. In order to learn and evaluate the different patterns of heart rate variability caused by individual variations, body conditions, circadian rhythms and so on, a new recurrent neural network which incorporates a hidden Markov model is used. In the experiments, a mental workload was given to five subjects, and the subjective rating scores of their mental stress were evaluated using heart rate variability. It was confirmed from the experiments that the proposed method could achieve high learning/evaluating performances.