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CN-122006111-A - Intelligent optimization method for deep brain electrical stimulation parameters of parkinsonism patient

CN122006111ACN 122006111 ACN122006111 ACN 122006111ACN-122006111-A

Abstract

The invention relates to the technical field of deep brain stimulation, in particular to an intelligent optimization method for deep brain stimulation parameters of parkinsonian patients. The method comprises the steps of carrying out characteristic measurement on nerve residual signals in the deep brain electric stimulation process, analyzing waveform slope characteristics and observing credibility by utilizing the nerve residual signals, determining artifact morphology punishment items and path accumulation cost, acquiring pathological signal estimated intensity by adopting a dynamic programming algorithm based on the path accumulation cost, carrying out characteristic measurement on pathological burst density by utilizing the pathological signal estimated intensity, and optimizing stimulation pulse amplitude in the deep brain electric stimulation process based on the pathological burst density. According to the invention, the intensity is estimated by measuring the pathological signal, so that the problem of signal fragmentation of the pathological signal is avoided, and the amplitude of the stimulation pulse in the deep brain electrical stimulation process is accurately optimized.

Inventors

  • YAN JIAN
  • JIANG JIANNAN
  • XIAO JIE

Assignees

  • 贵州医科大学附属医院

Dates

Publication Date
20260512
Application Date
20260324

Claims (10)

  1. 1. An intelligent optimization method for deep brain electrical stimulation parameters of a parkinsonism patient is characterized by comprising the following steps: collecting mixed field potential signals in the deep brain electrical stimulation process, eliminating artifacts in the mixed field potential signals according to the signal saturation state at each sampling moment, and determining nerve residual signals at each sampling moment; Comparing the residual instantaneous energy with a preset maximum signal amplitude value to determine a state observation value of each sampling moment, presetting each preset state index and a corresponding preset state signal value of each sampling moment, judging whether a preset capacitor discharge condition is met according to the waveform slope factor and the preset state signal value, and acquiring an artifact form penalty item of the preset state index according to the judgment condition; Comparing the preset state indexes of the previous sampling moment with each preset state index of the current sampling moment, analyzing the first deviation between the state observation value and the preset state signal value, and acquiring path accumulation cost by combining the artifact morphology penalty term; based on the path accumulated cost, acquiring an optimal state index of each historical sampling moment by adopting a dynamic programming algorithm, and acquiring pathological signal estimated intensity of each historical sampling moment by utilizing the optimal state index; And acquiring the pathological burst density at each sampling moment according to the distribution of the estimated intensity of the pathological signal on the time sequence, and optimizing the stimulation pulse amplitude in the deep brain electrical stimulation process based on the pathological burst density.
  2. 2. The intelligent optimization method for deep brain stimulation parameters of parkinsonism patients according to claim 1, wherein the judgment method for the signal saturation state comprises the following steps: Calculating a difference value between the maximum value of a preset range interval of the ADC and a preset saturation judgment margin as a first difference value; Calculating the sum of the minimum value of the preset measuring range interval and the preset saturation judgment margin of the ADC as a first sum value; And determining a saturation mask of each sampling time according to the comparison result of the mixed field potential signal and the first difference value and the first sum value, wherein the saturation mask is 0 and indicates that the current sampling time is in a signal unsaturated state, and the saturation mask is 1 and indicates that the current sampling time is in a signal saturated state.
  3. 3. The intelligent optimization method for deep brain stimulation parameters of parkinsonian patients according to claim 1, wherein the method for obtaining the neural residual signals comprises the following steps: in an initial preset first parameter stimulation period after the deep brain electric stimulation is started, the length of the stimulation period is the reciprocal of a preset stimulation frequency, and an artifact waveform template vector with the same length as the stimulation period is constructed; under each sampling time of an unsaturated state, carrying out phase superposition averaging according to mixed field potential signals in a preset first parameter stimulation period, and filling template initial values on an artifact waveform template vector according to a phase superposition averaging result to obtain the artifact waveform vector; And eliminating the artifacts of the mixed field potential signals according to the artifact waveform vector, and determining the nerve residual signals at each sampling moment.
  4. 4. The intelligent optimization method for deep brain stimulation parameters of parkinsonian patients according to claim 1, wherein the method for obtaining the waveform slope factor comprises the following steps: calculating the absolute value of the neural residual signal as residual instantaneous energy at each sampling moment; and according to the difference of the neural residual signals at adjacent sampling moments and combining the residual instantaneous energy, obtaining the waveform slope factor at each sampling moment.
  5. 5. The intelligent optimization method for deep brain stimulation parameters of parkinsonian patients according to claim 1, wherein the method for acquiring the state observation value comprises the following steps: calculating the ratio of the residual instantaneous energy to the preset maximum signal amplitude as a first ratio; and mapping the first ratio by using the preset state resolution, rounding the mapping result, and comparing the mapping result with the preset state resolution to obtain the state observation value of each sampling moment.
  6. 6. The method for intelligently optimizing deep brain electrical stimulation parameters of a parkinsonism patient according to claim 1, wherein the method for acquiring the artifact morphology penalty term comprises the following steps: acquiring pulse trigger time of the stimulation pulse to which each sampling time belongs, and calculating a difference value between each sampling time and the pulse trigger time of the stimulation pulse to which each sampling time belongs as a stimulation phase offset of each sampling time; for each sampling moment, if the stimulus phase offset belongs to an element in a sensitive time window of preset capacitor discharge, the waveform slope factor is smaller than a preset waveform smoothing threshold value, and a preset state signal value corresponding to each preset state index is larger than a preset safety noise floor threshold value, a trigger condition of preset capacitor discharge is met, and an artifact morphology penalty term of each preset state index at each sampling moment is determined according to a preset strong suppression coefficient and the preset state signal value; If the triggering condition of the preset capacitor discharge is not met, enabling the artifact morphology penalty term of each preset state index at each sampling moment to be 0.
  7. 7. The intelligent optimization method for deep brain stimulation parameters of parkinsonian patients according to claim 1, wherein the method for acquiring the path cumulative cost comprises the following steps: for the first sampling moment, setting the path accumulation cost of each preset state index of the first sampling moment to be 0; For each sampling time except the first sampling time, analyzing the observation credibility of each sampling time, and constructing an observation fitting term by taking the observation credibility as the weight of the first deviation; taking an artifact morphology penalty term of each preset state index at each sampling moment as a morphology penalty term; Comparing the state index to be analyzed at the previous sampling moment with the corresponding preset state index at the current sampling moment, and combining the path accumulated cost under the state index to be analyzed at the previous sampling moment to construct a state transfer item; and obtaining the path accumulated cost of each preset state index at each sampling moment according to the observation fitting term, the morphology punishment term and the state transfer term.
  8. 8. The intelligent optimization method for deep brain stimulation parameters of parkinsonism patients according to claim 7, wherein the method for obtaining the observation credibility comprises the following steps: for each sampling moment, if the sampling moment is in a signal saturation state, the observation reliability of the sampling moment is set to be 0; if the sampling time is in a signal saturation state, comparing the nerve residual signal and the mixed field potential signal in a preset short-time window, and calculating the observation credibility of the sampling time.
  9. 9. The intelligent optimization method for deep brain stimulation parameters of parkinsonian patients according to claim 1, wherein the method for obtaining the estimated intensity of the pathological signal comprises the following steps: Calculating a second ratio by using the optimal state index and the preset state resolution; And calculating the product of the second ratio and the preset maximum signal amplitude as the pathological signal estimated intensity at each historical sampling moment.
  10. 10. The intelligent optimization method for deep brain stimulation parameters of parkinsonian patients according to claim 1, wherein the method for acquiring pathological burst density comprises the following steps: Presetting a preset long-time sliding window and a preset short-time statistical window of each sampling moment, and acquiring a preset pathology judgment threshold value of each sampling moment by using the preset long-time sliding window; Recording the number of sampling points with the estimated pathological signal intensities of all the historical sampling moments in the preset short-time statistical window being greater than the preset pathological judgment threshold as the target number of sampling points; And recording the ratio of the number of target sampling points to the total number of sampling points in the preset short-time statistical window as the pathological burst density at each sampling moment.

Description

Intelligent optimization method for deep brain electrical stimulation parameters of parkinsonism patient Technical Field The invention relates to the technical field of deep brain stimulation, in particular to an intelligent optimization method for deep brain stimulation parameters of parkinsonian patients. Background Deep brain electrical stimulation (DeepBrainStimulation) of DBS is an effective means for treating neurodegenerative diseases such as Parkinson's disease by delivering high-frequency electrical pulses to specific nuclei of the brain through implanted electrodes. In order to improve the treatment precision and reduce the side effects, in the process of conducting deep brain electrical stimulation on a parkinsonism patient, the stimulation parameters are required to be dynamically adjusted according to the real-time pathological state of the patient, but because stimulation artifacts (StimulationArtifact) are generated in the deep brain electrical stimulation process, weak nerve signals are covered and cut off. Meanwhile, the amplitude of the stimulation pulse in the deep brain electrical stimulation process is usually in the level of volts, and the effective LFP nerve signal (LocalFieldPotential) is only in the level of microvolts, which are different from each other by aboutMultiple times. Although the front-end amplifier has a certain common mode rejection capability, the double layer capacitance effect of the electrode-tissue interface generates exponential decay artifacts, and the exponential decay artifacts cover a wide frequency band in the frequency domain and overlap with LFP nerve signals. More seriously, at the moment of the stimulus pulse and in a short time thereafter, the front-end amplifier tends to enter a saturated or cut-off state due to the excessive signal, resulting in the physical signal being completely lost or truncated by the plateau. At present, a Blanking (Blanking) strategy is generally adopted in the prior art, namely, pathological signal data during a pulse are directly discarded, but continuous pathological signals are cut into discontinuous fragments in a time domain, namely, the problem of signal fragmentation of the pathological signals occurs, so that the amplitude of a stimulation pulse in a deep brain electrical stimulation process cannot be accurately optimized, and the symptom control of a parkinsonism patient is unstable. Disclosure of Invention In order to solve the technical problem that the amplitude of stimulation pulses in the deep brain electrical stimulation process cannot be accurately optimized due to the fact that a Blanking (Blanking) strategy is adopted in the prior art, a continuous pathological signal is cut into discontinuous fragments in a time domain, the invention aims to provide an intelligent optimization method for deep brain electrical stimulation parameters of a parkinsonism patient, and the adopted technical scheme is as follows: The invention provides an intelligent optimization method for deep brain electrical stimulation parameters of a parkinsonism patient, which comprises the following steps: collecting mixed field potential signals in the deep brain electrical stimulation process, eliminating artifacts in the mixed field potential signals according to the signal saturation state at each sampling moment, and determining nerve residual signals at each sampling moment; Comparing the residual instantaneous energy with a preset maximum signal amplitude value to determine a state observation value of each sampling moment, presetting each preset state index and a corresponding preset state signal value of each sampling moment, judging whether a preset capacitor discharge condition is met according to the waveform slope factor and the preset state signal value, and acquiring an artifact form penalty item of the preset state index according to the judgment condition; Comparing the preset state indexes of the previous sampling moment with each preset state index of the current sampling moment, analyzing the first deviation between the state observation value and the preset state signal value, and acquiring path accumulation cost by combining the artifact morphology penalty term; based on the path accumulated cost, acquiring an optimal state index of each historical sampling moment by adopting a dynamic programming algorithm, and acquiring pathological signal estimated intensity of each historical sampling moment by utilizing the optimal state index; And acquiring the pathological burst density at each sampling moment according to the distribution of the estimated intensity of the pathological signal on the time sequence, and optimizing the stimulation pulse amplitude in the deep brain electrical stimulation process based on the pathological burst density. Preferably, the method for judging the signal saturation state includes: Calculating a difference value between the maximum value of a preset range interval of the ADC and a preset sat