Intelligent brain deep electrical stimulation parameter optimization method for Parkinson patients

By eliminating artifacts during deep brain stimulation, identifying neural residual signals, and optimizing the stimulation pulse amplitude using a dynamic programming algorithm, the problem of pathological signal fragmentation was solved, thus improving the treatment stability for Parkinson's patients.

CN122006111APending Publication Date: 2026-05-12THE AFFILIATED HOSPITAL OF GUIZHOU MEDICAL UNIV
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Patent Information

Application Number
CN202610366858.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-24
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

In the process of deep brain stimulation, existing technologies suffer from fragmentation of pathological signals due to the masking and truncation of weak neural signals by stimulation artifacts. This makes it impossible to accurately optimize the amplitude of stimulation pulses, resulting in unstable symptoms in Parkinson's patients.

Method used

By collecting mixed field potential signals during deep brain stimulation, artifacts are eliminated, neural residual signals are identified, and the instantaneous energy of the residuals is analyzed. A dynamic programming algorithm is used to obtain pathological signals to estimate their intensity and optimize the amplitude of the stimulation pulse.

Benefits of technology

It achieves accurate optimization of the stimulation pulse amplitude during deep brain electrical stimulation, avoids pathological signal fragmentation, and improves the stability and effectiveness of treatment for Parkinson's patients.

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Abstract

The invention relates to the technical field of deep brain stimulation, in particular to a deep brain electrical stimulation parameter intelligent optimization method for Parkinson patients. According to the method, feature measurement is carried out on neural residual signals in the deep brain stimulation process, waveform slope features and observation credibility are analyzed through the neural residual signals, and therefore an artifact form penalty term and path accumulation cost are determined; based on the path accumulation cost, a dynamic programming algorithm is adopted to obtain pathological signal estimation intensity, the pathological signal estimation intensity is utilized to perform characteristic measurement on the pathological outbreak density, and the stimulation pulse amplitude in the deep brain stimulation process is optimized based on the pathological outbreak density. According to the method, the intensity is estimated by measuring the pathological signals, the problem of signal fragmentation of the pathological signals is avoided, and therefore the stimulation pulse amplitude in the deep brain stimulation process is accurately optimized.
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