Multiscale Entropy Analysis for VNS Patient Screening
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Solution Overview
Problem
Current methods for screening patients suitable for Vagus Nerve Stimulation (VNS) surgery are costly, complex, and yield inconsistent results due to high individual variability and lack of definitive preoperative evaluation techniques.
Innovation Solution
A modeling method using 24-hour dynamic ECG signals and multiscale entropy (MSE) analysis to extract heart rate complexity parameters, which indirectly reflect autonomic nervous system balance, allowing for accurate and efficient patient selection for VNS surgery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG and MRI methods are used for patient screening, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive, complex EEG and MRI equipment with a simple, low-cost portable dynamic ECG recorder. The ECG device is inexpensive, portable, and requires minimal operational expertise, while still providing sufficient screening capability through HRV analysis. This embodies the principle of using cheap, simple devices instead of expensive, complex ones.
Solution Approach 2:
The patent substitutes complex neurological imaging systems (EEG, MRI) with a simpler cardiovascular monitoring system (ECG). Instead of directly measuring brain electrical activity or structural images, the invention uses indirect cardiovascular markers (heart rate variability) that are easier to measure and analyze, replacing complex mechanical and electromagnetic imaging systems with a simpler electrical monitoring approach.
2Measurement precision
If EEG and MRI methods are used for patient screening, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent employs a low-cost portable ECG recorder instead of expensive EEG or MRI equipment. The ECG device costs a fraction of the other modalities and can be deployed widely without significant financial burden, making the screening process economically viable while maintaining adequate measurement precision through HRV parameters.
3Reliability
If comprehensive preoperative evaluation is performed, then reliability is improved, but time consumption increases
Solution Approach 1:
The patent extracts the essential screening information from the comprehensive preoperative evaluation by focusing specifically on heart rate variability parameters from ECG data. Instead of analyzing all possible clinical parameters, imaging data, and test results, the invention isolates and utilizes only the HRV metrics that are most predictive of VNS response, thereby maintaining reliability while dramatically reducing evaluation time.
Solution Approach 2:
The patent creates a simplified model of patient response to VNS therapy by using HRV parameters as a proxy for overall neurological status and autonomic function. Rather than performing the complete, time-consuming comprehensive evaluation, the invention uses this copied surrogate marker (HRV) that correlates with treatment outcome, achieving similar reliability with much less time investment.
Data Source
AI summary
A modeling method for screening surgical patients, used in analysis modeling for heart rate variability (HRV). Low-cost, portable and wearable signal acquisition equipment is utilized to acquire an electrocardiography (ECG) signal of an epileptic 24 hours before surgery; a multiscale entropy (MSE) of the ECG is calculated by means of a programmed HRV analysis method, wherein characteristic parameters representing heart rate complexity are extracted on the basis of an MSE curve, and a medical refractory epileptic suitable for vagus nerve stimulation (VNS) surgery is accurately and efficiently screened, thus avoiding unnecessary expenditures and avoiding delaying an optimal opportunity for treatment. Meanwhile, the curative effects of the VNS treatment may be wholly improved by means of clearly selecting VNS surgical indication patients according to the characteristic parameters of the MSE complexity of the ECG.