Spinal Cord Evoked Potential Detection for Fast EMG Mapping
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Solution Overview
Problem
The manual detection of evoked potentials in spinal cord epidural stimulation is laborious, time-consuming, and prone to human errors, limiting scalability and accuracy in data analysis.
Innovation Solution
A novel method using unsupervised and online algorithms to automatically detect, de-noise, extract features, and visualize evoked potentials through Generalized Gaussian Markov Random Field (GGMRF) techniques, maximum likelihood estimation, and log-likelihood ratios, converting EMG signals into 2-D and 3-D images for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection is used to detect evoked potentials, then detection accuracy is improved, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual visual inspection (mechanical human observation) with an automated computer-based detection system that uses signal processing algorithms. The system automatically identifies evoked potentials in EMG signals through computational methods, eliminating the need for manual analysis while maintaining detection accuracy and significantly reducing processing time.
Solution Approach 2:
The automated detection system enables the data analysis process to be self-service, where the computer algorithm independently processes and analyzes EMG signals without requiring human intervention. The system automatically detects evoked potentials, quantifies their attributes, and generates results, making the entire process autonomous and scalable.
2Measurement precision
If manual detection methods are used, then detection accuracy is maintained, but scalability to large numbers of patients is limited
Solution Approach 1:
The patent replaces manual detection with an automated computational system that can process multiple patients' data simultaneously. This substitution enables scalable processing of large datasets from numerous patients while maintaining consistent detection accuracy, as the algorithm applies the same rigorous analysis criteria uniformly across all subjects.
Solution Approach 2:
The automated detection system provides universal applicability across multiple patients and experimental conditions. The same algorithm can analyze EMG signals from different patients, muscles, and stimulation configurations, making the system highly scalable and adaptable to various research scenarios without requiring additional manual effort.
3Productivity
If automated detection algorithms are implemented, then processing speed and productivity are improved, but risk of human error elimination is achieved
Solution Approach 1:
The patent replaces manual detection with an automated algorithm that eliminates human errors such as fatigue, inattention, and subjective bias. The computational system consistently applies detection criteria without variation, ensuring reliable and reproducible results while dramatically increasing processing speed and productivity.
Solution Approach 2:
The automated detection system incorporates feedback mechanisms where the algorithm continuously refines its detection based on signal characteristics and previously identified patterns. This feedback loop ensures high reliability by adjusting detection parameters dynamically and maintaining consistent accuracy across different signal conditions and patient variations.
Data Source
AI summary
Embodiments of the present invention relate to a novel approach to automatically detect the occurrence of evoked potentials, quantify the attributes of the signal and visualize the effect across a high number of spinal cord epidural stimulation parameters. This new method is designed to automate the current process for performing this task that has been accomplished manually by data analysts through observation of the raw EMG signals, which is laborious and time-consuming as well as being prone to human errors. The proposed method provides fast and accurate framework for activation detection and visualization of the results within five main algorithms.