Study On Biological Impedance Detection Method Of Brain Nerve Activity

The basic physiological activities and the cognitive function of the human brain need an accurate cerebral blood flow regulation mechanism.Studying the cerebral nerve regulation mechanism is valuable for brain function research and early screening and diagnosis of diseases.Biological impedance detection technology can be used to dynamically monitor changes in electrical properties in biological tissue and quickly obtain their physiologic information.Therefore,this thesis proposes a bioimpedance detection system for brain neural activity based on the principle of bioimpedance detection.The system records cerebral blood flow impedance signals under different neural activity states.The research proves that bioimpedance signals can effectively represent the degree of brain nerve activity,and establishes a foundation for further study of brain function.Firstly,the correlation between biological tissue electrical properties and different excitation signals is clarified.Aiming at the composition of neurovascular units and the regulation process of cerebral nerve blood flow,this thesis proposes a method to detect cerebral nerve activity based on the basic principle of bioimpedance detection.The biological detection system of cerebral nerve activity includes an excitation source,signal conditioning circuit,data transmission module and upper computer software.The controllable AC constant current source is used to deliver a steady and reliable AC current to the brain.The signal conditioning circuit is designed to match with requirements of physiological signal monitoring system and to improve the signal-to-noise ratio of the signal.Then,combining the impedance detection circuit with above systems,this experiment receives the cerebral blood flow impedance signal.Finally,the data transmission is completed through the acquisition card.The software interface realizes the real-time signal transmission,presentation and writing between the upper computer and the hardware system.It is verified that the impedance modulus measurement accuracy of the bioimpedance system in brain neural activity was 1.09%.Secondly,according to the basic principles of the functional activities of the nervous system and the division of the functional areas of the brain,this experiment designs the detection scheme of cerebral blood flow impedance signal.System is used to collect the left occipital lobe cerebral blood flow impedance signals before and after visual stimulation.Extracting the effective information of cerebral blood flow impedance signal and cerebral blood flow impedance differential signal by wavelet decomposition and reconstruction method and event detection.Through analysis of its blood flow dynamics parameters,evaluation of variation trend of cerebral blood flow impedance signal and differential signal under different neural activity states can be achieved.Finally,this thesis proposes a brain nerve activity biological impedance differential signal pattern identification network based on the attention mechanism CNN-LSTM(Convolutional Neural Network Long Short-Term Memory).In order to avoid subjective judgment affecting the selection of signal characteristic parameters,the convolutional neural network was established to extract the depth features of the impedance differential signals of cerebral blood flow.To identify the bioimpedance differential signals of cerebral blood flow under different neural activity states.The network proposed in this thesis has good classification performance for impedance differential signals of different brain neural activity states,and the average classification accuracy is more than 89%.The results show that the bioimpedance detection system can meet the requirements of signal detection.It is verified that cerebral blood flow impedance signal and cerebral blood flow impedance differential signal can effectively represent the changes in brain neural activity.It is a reliable research providing basis for further exploring brain functional zoning and clinical diagnosis.