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Time domain data classification method based on zero crossing point coefficient and implantable stimulation system

A technology of time-domain data and classification method, applied in implanted stimulators, electrotherapy, medical science, etc., can solve problems such as hidden safety hazards, high accuracy, overheating damage or explosion, etc., to reduce secondary damage, Improve accuracy and ensure battery life

Active Publication Date: 2022-02-08
NEURACLE TECH CHANGZHOU CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Generally speaking, the accuracy of complex algorithms is high but the power consumption is high, the battery working time is short and easy to heat up, and even extreme situations such as overheating, damage or explosion may occur, which has potential safety hazards and is not suitable for implantable stimulation systems.

Method used

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  • Time domain data classification method based on zero crossing point coefficient and implantable stimulation system
  • Time domain data classification method based on zero crossing point coefficient and implantable stimulation system
  • Time domain data classification method based on zero crossing point coefficient and implantable stimulation system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0076] Select 3 patients as test targets, respectively recorded as ID01-ID03, each patient has two epileptic seizures, and collect the EEG raw data of any channel from before the seizure to the seizure of each patient It is processed as the training data of the classifier (6 sets of data in total) and various classification standards are obtained, and the zero-crossing coefficient is selected as the signal feature of the classification standard to classify the real-time EEG data of the patient, and the classification results are counted. Accuracy.

Embodiment 2

[0078] Based on the classification standard obtained in Example 1, the zero-crossing coefficient, the amplitude mean value and the line length are selected and combined with the signal characteristics of the classification standard to classify the real-time EEG data of the patient, and the accuracy of the classification is counted.

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PUM

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Abstract

The invention discloses a time domain data classification method based on a zero crossing point coefficient and an implantable stimulation system. The time domain data classification method based on the zero crossing point coefficient comprises the steps of obtaining a classification standard of time domain data; classifying the time domain data according to the classification standard; and selecting a stimulation strategy according to a classification result, so that the power consumption of the implantable nerve stimulator can be reduced, the endurance time of the implantable nerve stimulator can be prolonged, and the accuracy of closed-loop stimulation can be improved.

Description

technical field [0001] The invention relates to the technical field of time-domain data classification, in particular to a time-domain data classification method based on zero-crossing coefficients and an implantable stimulation system. Background technique [0002] Time domain (Time domain) is to describe the relationship of mathematical functions or physical signals to time. For example, the time-domain waveform of a signal can express the change of the signal over time. Using the time-domain analysis method to extract the waveform characteristics of the EEG data makes it possible for the neurostimulation system to be applied in the medical field. In this system, the implantable neurostimulator is implanted into the patient's body, which can monitor the EEG signal of the target part of the patient and perform analysis and calculation, and then transmit the analysis result to the external device, and the external device is then based on the implanted neurostimulator. The ...

Claims

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Application Information

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IPC IPC(8): A61B5/372A61B5/00A61N1/36
CPCA61B5/372A61B5/7225A61B5/7267A61N1/3605A61N1/36139
Inventor 黄肖山刘晓玲胥红来章希睿李含磊任思瑾宫长辉
Owner NEURACLE TECH CHANGZHOU CO LTD
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