Power monitoring method and power monitoring system of deep brain stimulator

A brain pacemaker and power monitoring technology, applied in the field of medical devices, can solve problems such as unfavorable treatment effect, inability to apply clinically, and errors

Active Publication Date: 2016-12-21
BEIJING PINS MEDICAL +1
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AI Technical Summary

Problems solved by technology

Inaccurate battery life prediction will lead to premature DBS replacement surgery, resulting in waste of battery and property loss of patients; or late replacement surgery resulting in unplanned downtime, which is detrimental to the treatment effect
For patients with DBS indications, the adjustment of stimulation parameters (amplitude, frequency, pulse width) after surgery is the most important means to ensure the curative effect. Almost every DBS implanter will go through the process of parameter adjustment. It is suitable for typical (commonly used) stimulation parameter combina

Method used

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  • Power monitoring method and power monitoring system of deep brain stimulator
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  • Power monitoring method and power monitoring system of deep brain stimulator

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Embodiment Construction

[0034] The invention provides a method for monitoring the electricity of a brain pacemaker and a system for monitoring the electricity of a brain pacemaker using the method. The method can predict the charge of non-rechargeable batteries and the charge consumption of rechargeable batteries. Especially important for non-rechargeable batteries.

[0035] The present invention first introduces a support vector machine (SVM) prediction model.

[0036] Statistic Learning Theory (SLT) is a basic theory and mathematical framework that specializes in the study of machine learning laws in the case of small samples. The learning machine support vector machine (Support Vector Machine, SVM) based on structural risk minimization proposed by Vapnik is a very potential regression classification technology and a pattern recognition method based on statistical learning theory. Function regression estimation is a common machine learning problem. On this problem, SVM obtains a good generalizati...

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Abstract

The invention relates to a power monitoring method of a deep brain stimulator and a power monitoring system employing the method. The method comprises the steps of acquiring the stimulation parameters of a deep brain stimulator, calculating the battery-related parameters of the deep brain stimulator through a support vector machine prediction model, and displaying the calculated battery parameters to a user. According to the power monitoring method and the power monitoring system of a deep brain stimulator provided by the invention, the power of a deep brain stimulator can be predicted with high precision. Through the prediction scheme, DBS power monitoring and service life assessment under each stimulation parameter combination and patient's individual condition based on clinical specificity can be realized. The method and the system are of certain reliability, and can be applied to clinical practice eventually.

Description

technical field [0001] The present invention relates to the related technical field of medical devices, in particular, to an implantable medical device (Implantable Medical Device, IMD). Background technique [0002] Brain pacemaker, also known as deep brain stimulation (Deep Brain Stimulation, DBS), is currently an effective technology for the treatment of advanced and drug-refractory movement disorders and mental disorders. Especially in the treatment of Parkinson's disease, the curative effect is remarkable. Recent research results show that brain pacemakers can also be used to treat Alzheimer's disease, obsessive-compulsive disorder, depression and other mental diseases. see figure 1 The implementation device of deep brain stimulation is brain pacemaker, which is a set of implantable microelectronic devices, including pulse generator (IPG), electrodes and extension wires. After the brain pacemaker is stimulated, the pulse generator will generate continuous electrical ...

Claims

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367G01R31/382
Inventor 曲薇胡春华马伯志黄俊陈浩郝红伟薛林李路明
Owner BEIJING PINS MEDICAL
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