Research On Neural Control System Based On Closed-Loop Control Strategy Of Multi-Sensors

Parkinson’s disease,as a motor neurological disorder,is characterized by pathological tremor,stiffness and slow movement of the limbs due to the reduction of dopamine,which brings inconvenience to patients’ daily life.Clinical researchers have been focusing on this disease,but research progress is limited,the exact principle is still unclear,and drug therapy will eventually bring side effects to patients.At present,most of the studies focus on pathogenesis and drug treatment,while the research on non-pharmacological inhibitory measures is relatively less.Therefore,this thesis attempts to explore a new way from engineering to suppress Parkinson’s disease.The purpose of this thesis is not to study the mechanism of Parkinson’s disease,but to inhibit the occurrence of Parkinson’s disease.From the engineering point of view,combined with instrumental science and technology,starting from the detection of multi-state signals of Parkinson’s disease,such as brain nerve signals,limb surface electromyogram signals and motor physiological signals,the design of the research device for neuroregulation of Parkinson’s disease and the collection of multi-sensor pathogenic signals are discussed.Intelligent closed-loop suppression strategy is studied in order to provide new ideas for the control of Parkinson’s disease,ultimately improve the quality of life of patients,and bring health to patients.The main research work is as follows:1.Study on the network structure model of brain motor neurons.Based on the classical model,the processing and transmission of electrical signals in brain neurons were modeled by mathematical and electrical methods.The current formula of input target neurons was given,and the mechanism of deep brain electrical stimulation acting on the basal thalamic nucleus to control the onset of Parkinson’s disease was explained.The characteristics of neurons in neural networks are modeled by using higher order differential equations,and the corresponding membrane potential equations are given.Quantitative neuron model lays a foundation for the study of multi-sensor closed-loop neuroregulatory system in this thesis.2.Research on the scheme of multi-sensor neuromodulation instrument.A high common mode rejection ratio analog front-end circuit is designed to effectively detect weak deep brain field potential and limb surface electromyography.According to the characteristics of brain tissue,a multi-mode electrical pulse output circuit is designed to satisfy the impedance driving ability of deep brain,and to achieve effective electrical stimulation output in a safe range.The wearable idea is adopted to design a motion state acquisition device to realize the recognition and quantitative evaluation of the body motion state.Using wireless networking technology,the real-time synchronization and interaction schemes of multi-node data are studied.The real-time upload,data analysis and closed-loop adjustable electrical stimulation output of deep brain field potential signal,limb surface electromyography signal and body motion signal are realized.The design and implementation of multi-sensor neuro-control instrument provide the instrument basis for the proposed and verified closed-loop control strategy of multi-sensor in this thesis.3.Research on closed-loop control strategy for multi-sensor neuroregulation process.By detecting multi-node motion signals,obtaining walking and static data,analyzing motion parameters,studying the quantitative evaluation method of symptom grade,inheriting the classical content of motor function evaluation,improving the method of doctor’s subjective score in the scale method to meet the needs of clinical long-term continuous monitoring and objective evaluation.The research combines multi-state physiological signal detection,quantitative evaluation results,and automatic control technology and nerve regulation process to realize closed-loop accurate identification and control process of multi-signal feedback.It avoids the problem of using single deep brain field potential signal as feedback variable to control patients with tremor symptoms but without detecting signal characteristics and output electrical stimulation.The proposed multi-sensor closed-loop control strategy aims to explore a new research approach for clinical treatment of Parkinson’s disease.4.Comprehensive experiment of multi-sensor closed-loop nerve control system.By simulating the human tissue environment and setting up the experimental platform of nerve regulation,the experiment verifies that the system in this thesis can accurately collect the deep brain field potential signal and control the output of stimulation by closed loop.The function and performance of the system are tested from the aspects of weak signal acquisition,motion state perception and electrical stimulation output.The test results meet the requirements.Based on the clinical controlled experiment,the data of body movement were obtained,the characteristics of body movement were analyzed,and the results of comprehensive quantitative evaluation of movement characteristics were given.The feasibility of the closed-loop multi-sensor control strategy proposed in this thesis is proved by simulation and clinical comprehensive experimental results.