Epidural Stimulation Parameter Selection Using EMG Spectral Analysis
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Solution Overview
Problem
Current methods lack algorithms and procedures for determining the exact set of parameters for facilitating standing using tonic spinal cord epidural stimulation (scES) in individuals with motor complete spinal cord injury (SCI), and the characteristics of muscle activation patterns leading to independent standing remain poorly understood.
Innovation Solution
A novel framework that implements spectral analysis and machine learning methods to characterize electromyography (EMG) activity, identifying effective frequency-domain features for independent standing and ranking the effectiveness of muscle activation patterns generated by different scES parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral analysis and machine learning methods are implemented to characterize EMG activity, then measurement precision of muscle activation patterns is improved, but device complexity increases
Solution Approach 1:
The patent introduces spectral analysis and machine learning algorithms as intermediary computational tools that process EMG signals to extract meaningful features. These intermediaries transform raw EMG data into classified muscle activation patterns, enabling precise measurement without directly modifying the physiological system being measured.
Solution Approach 2:
The patent replaces traditional mechanical or manual analysis methods with computational signal processing techniques. Spectral analysis and machine learning models substitute for conventional EMG interpretation methods, providing automated classification of muscle activation patterns associated with independent versus assisted standing.
2Adaptability or versatility
If multiple interleaving stimulation programs are used to access different spinal circuitry locations, then adaptability of stimulation parameters is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic stimulation programs that can be adjusted and interleaved to target different spinal circuitry locations. The stimulation parameters (electrode configurations, intensities, frequencies) are made variable and adaptable, allowing the system to dynamically switch between multiple programs to access different motor pools and spinal segments.
Solution Approach 2:
The patent divides the stimulation approach into multiple discrete interleaving programs, each targeting specific spinal circuitry locations or motor pools. By segmenting the overall stimulation task into separate programmable units, the system achieves adaptability while maintaining organized control over complex parameter sets.
3Measurement precision
If EMG frequency-domain features are extracted using spectral analysis, then measurement precision of muscle activation characteristics is improved, but loss of time in signal processing increases
Solution Approach 1:
The patent performs preliminary spectral analysis and feature extraction during the signal acquisition phase. By pre-processing EMG signals to extract frequency-domain features before classification, the system prepares data in advance, reducing computational burden during real-time decision-making and minimizing processing delays.
Solution Approach 2:
The patent implements continuous spectral analysis and real-time feature extraction from EMG signals. The system maintains continuous monitoring and processing of muscle activation patterns, ensuring that frequency-domain features are continuously updated without interrupting the stimulation or measurement process, thereby minimizing time loss.
Data Source
AI summary
Computer-implemented systems and methods for determining epidural spinal stimulation parameters that promote muscle activation use spectral analysis and machine learning techniques to characterize electromyography data.