Research based on medical signals has received significant attention in recent years. If the patients' states can be accurately monitored based on medical signals, it greatly benefits both doctors and patients. This paper proposes a method to extract signal features from heart rate variability signals and classify patients' states using the long short-term memory network and enable effective monitoring of noxious stimulation. For data processing, the heart rate variability signal is decomposed and recombined by the empirical mode decomposition method, and the signal features of the noxious stimulation are extracted by the sliding time window method. Compared with the average accuracy of direct classifications, the classification accuracy based on the proposed method is proved more accurate. The model based on the extracted features proposed can realize the classification of consciousness and general anaesthesia with an accuracy rate of more than 90% and accurately estimate the occurrence of tracheal intubation stimulation. Furthermore, this study shows that combining the deep learning neural network with the extracted more effective signal features under different states and stresses can classify the states with high accuracy. Therefore, it is promising to apply the deep learning method in researching the autonomic nervous system.
Keywords: Autonomic nervous system; Deep learning; Feature extraction; State classification.
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