Abstract-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 w^ith 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 netw^ork v^^hich incorporates a hidden Markov Model is used. In the experiments, a mental w^ork-load was given to five subjects, and the subjective rating scores of their mental stress were evaluated using heart rate variability. It w^as confirmed from the experiments that the proposed method could achieve high learning/evaluating performances. Keywords-Heart rate variability, Mental stress. Wavelet transform. Recurrent neural network. Hidden Markov model. I INTRODUCTIONAn electrocardiogram (ECG) is available for a basic physiological index which evaluates the change in patient's body condition and monitors physical and mental stress in his/her daily activities. The heart rate is complicated since it is affected by various factors such as a sinus node for a pacemaker, autononiic nerves, the endocrine system and so on. It is expected that the changes of these factors can be evaluated based on the analysis of Heart Rate Variability (HRV). In this paper, we propose a new evaluation method of HRV.HRV includes many frequency components, and various information can be obtained from them through the frequency domain analysis [1], [2]. In the report of Sayers, which is the pioneer research in this area, three peaks exist on the power spectrum of HRV. The lower frequency component (0.02 -0.06 [Hz]) is influenced by thermoregulation, and the middle frequency component (0.07 -0.14[Hz]) is influenced by blood pressure regulation, and the high frequency component (0.15 -0.5() [Hz]) is influenced by respiration respectively. However, if the power spectrum of HRV is calculated using fast Fourier transform, it expresses rough information in a fixed period of the time series signal, and the dynamic changes of the autononiic nerve activity cannot be expressed. It is difficult to analyze the nonstationary pattern of HRV using this method during the exercise. To overcome this difficulty. The wavelet transform (WT), which extracts local features of HRV in the time-frequency domain, is proposed [3].The changes in the spectrum pattern from physical and mental stress are different among individuals. The subjective feeling of the subjects is also different. These are the problems of the spectrum analysis of HRV. Most previous studies defined specified frequency ranges such as the low frequency component (LF) and the high frequency component (HF) on the power spectrum of HRV, and extracted a...
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.
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