Pattern recognition of mental stress levels from differential RRI time series using LSTM networks

This paper reports some experimental results of a preliminary study on development of a mental stress recognition method using a deep learning-based model. This model acquires time-varying characteristics in heart rate variability (HRV) data with a long short-term memory (LSTM) deep network. The method proposed in this paper attempts to directly input differential R-R intervals (RRI) time series into the LSTM network. Training and classification performance have been discussed with stress level recognition experiments, in which HRV data were obtained with mental stress levels induced in a virtual reality environment. Test configurations have examined with different number of classes, segmentation lengths, and datasets. Comparing with the experimental results of the authors' previous studies, it is found that using differential RRI can largely ease the convergence difficulty in LSTM training process.