Pain detecting and positioning method and system based on brain waves and neural network

Abstract

The invention discloses a pain detecting and positioning method and system based on brain waves and neural network, and the method comprises the steps: removing related noise of original electroencephalogram signals through independent component analysis, carrying out the pain grading of the electroencephalogram signals, carrying out the processing through a signal time division window, integrating the pain level data and then establishing a pain level data set; respectively generating spectral topographic maps of time windows of Theta, Alpha and Beta frequency bands related to pain through Fourier transform, azimuth isometric projection and a CloughTocher interpolation algorithm, and combining the spectral topographic maps into a multi-channel electroencephalogram sequence; obtaining a time-space feature vector of a brain wave sequence related to the pain degree and the pain position through a CNN-LSTM-AM neural network; and inputting the brain wave pain features learned by the CNN-LSTM-AM neural network into the pain classifier model to evaluate the pain level and the pain position. According to the invention, the pain degree change and position change characteristics of the brain waves can be accurately and efficiently extracted and processed, and the pain level and the pain position can be automatically identified.

Description

technical field

[0001] The invention relates to the technical field of pain detection, in particular to a pain detection and positioning method and system based on brain waves and neural networks. Background technique

[0002] Studies in recent years have shown that pain is accompanied by obvious physiological changes, which are mainly reflected in the response parameters of the autonomic nervous system, such as brain waves, heart rate, and skin electrical levels. Using the electrophysiological signals that accompany pain to measure pain level is an effective way to achieve objective pain assessment. Among pain-related electrophysiological signals, electroencephalogram (EEG) is an ideal physiological index for objectively evaluating pain. It has high time resolution of millisecond level and relatively low data acquisition cost, and directly reflects A large amount of information reflecting human physiology, psychology and diseases can be obtained through feature extraction ...

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