The Study Of Electrophysiological Signal Analysis System Towards Real-world Application

Electrophysiological signal analysis is one of the effective tools for clinical detection and brain function research of neuropsychiatric diseases.With the development of computer and microelectronics technology,integrated,intelligent and portable electrophysiological signal analysis systems are widely used in different fields of medicine and neuroscience,playing a crucial role in disease monitoring and diagnosis.Especially in the study of neuropsychiatric diseases such as epilepsy,Alzheimer's and Parkinson's syndrome,it is possible to extract reliable and effective biomarkers from the signals more objectively and efficiently,so as to realize the diagnosis and prediction for neurological diseases.However,the current clinical application system pays more attention to the collection of EEG signals,and lacks comprehensive and effective analysis functions.First,this paper proposes an architecture of electrophysiological signal analysis system based on EEG signals,which realizes multi-channel EEG signal acquisition,signal preprocessing,feature extraction and model prediction.Firstly,the acquisition,filtering,amplification and analog-to-digital conversion of EEG signals are designed with TGMA module.Preprocess was performed on the original EEG signals to achieve de-spying and frequency division.The feature extraction module extracts corresponding EEG features from single channel,pair-wised channel and multi-channel EEG signals by using time,frequency domain analysis algorithm and nonlinear analysis algorithm.The results of the analysis were used to assist in the diagnosis of neuropsychiatric disorders and the study of nonlinear dynamics in the brain.Moreover,using analysis software to extract power spectral density,phase lag index and cross-frequency coupling from the EEG data of patients with temporal lobe epilepsy,healthy people and acupuncture subjects,respectively.The results of epilepsy analysis showed that the average energy and synchrony of EEG signals of epilepsy were higher than those of healthy control,and epilepsy showed stronger cross-frequency coupling in the ?-? and ?-? cross bands.Then the brain function network was constructed based on cross-frequency coupling,it is found that the small world efficiency of the brain function network of epilepsy is enhanced between ? and low frequency,but weakened between ? and high frequency bands,indicating that the ?-band plays a crucial role in the information transmission among the brain.Acupuncture analysis showed that the acupuncture increased the power spectral density of the ? and ? bands.And the index in the ? and ? bands during acupuncture is significantly higher than pre-acupuncture.In addition,a significant post-effect was observed in the?band because the phase synchronization index remained high after acupuncture.A brain function network is constructed based on synchronous connections.As the acupuncture progresses,the functional connections between the left and right hemispheres are enhanced to promote brain information transmission.These signal characteristics are important reference for the diagnosis of epilepsy,and also provide new ideas for understanding the mechanism of acupuncture.The proposed electrophysiological signal analysis system can realize data acquisition,extract signal characteristics,and provide a novel tool for disease diagnosis and clinical research.