Decomposing Compound Muscle Action Potentials Into Individual Motor Units
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Solution Overview
Problem
Current methods fail to accurately decompose compound muscle action potentials (CMAPs) into individual motor unit contributions due to overlapping motor unit responses and time scattering, limiting detailed analysis of motor unit activity and excitability in clinical and neurophysiological studies.
Innovation Solution
A computer- or circuit-implemented method that receives multichannel electromyograms during voluntary and stimulated muscle contractions, identifies firing moments, calculates filters for individual or common motor units, and applies these filters to decompose CMAPs into individual motor unit contributions, accounting for noise and incomplete cancellations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CMAP analysis is performed using conventional methods, then the overall muscle response can be measured, but the individual motor unit contributions cannot be accurately separated due to overlapping MUAPs
Solution Approach 1:
The patent segments the compound muscle action potential into individual motor unit action potentials by identifying and separating overlapping signals. The decomposition process divides the complex CMAP waveform into discrete MUAP components, each representing a specific motor unit's contribution to the overall response.
Solution Approach 2:
The patent introduces a temporal dimension to the decomposition process by using time-scattered MUAPs from multiple stimulations. By analyzing MUAPs across different time points and stimulation events, the system resolves overlaps that cannot be separated in a single time window, effectively adding a temporal dimension to the signal separation problem.
2Quantity of substance
If multiple motor units are stimulated simultaneously, then the CMAP amplitude increases, but the individual MUAPs overlap and cannot be distinguished
Solution Approach 1:
The patent performs preliminary identification and characterization of MUAP waveforms before attempting decomposition. By pre-processing the signals to identify distinct MUAP patterns and their temporal characteristics, the system prepares the data structure needed for successful separation of simultaneously occurring motor unit responses.
Solution Approach 2:
The decomposition algorithm uses feedback mechanisms where the separated MUAP components are reconstructed and compared against the original CMAP. This iterative feedback process refines the decomposition by adjusting the timing, amplitude, and waveform parameters of individual MUAPs to minimize the difference between reconstructed and actual signals.
3Object-affected harmful factors
If submaximal stimulation is used, then muscle cross-talk and incomplete stimulation occur, causing high CMAP variability
Solution Approach 1:
The patent employs dynamic thresholding and adaptive filtering that adjusts parameters based on the instantaneous signal characteristics. The decomposition algorithm dynamically adapts to varying signal conditions, allowing it to reliably separate MUAPs even when stimulation levels fluctuate or when cross-talk from neighboring muscles is present.
Solution Approach 2:
The system changes multiple parameters including filtering frequencies, decomposition thresholds, and temporal windows based on the specific characteristics of each CMAP recording. By adapting these parameters to match the actual signal quality and stimulation conditions, the system maintains reliable decomposition across varying stimulation intensities and muscle states.
Data Source
Figure 1~2
AI summary
The invention relates to a method and device for decomposition of compound muscle action potentials, CMAPs, into contributions of individual motor units. The technical problem solved by the invention is how to assess the time moments in which the motor unit action potentials, i.e. MUAPs, appear as the responses of motor units to external stimulation. The problem is solved by the process for decomposition of CMAPs into contributions of individual motor units, which comprises the following steps: receiving (100) multichannel electromyograms, i.e. EMGs captured during voluntary muscle contractions, as input signals, identifying (300) firing moments of motor units from EMG input signals captured during voluntary muscle contractions, calculating (400) filters of individual motor units or a common filter of several motor units, receiving (200) multichannel EMGs captured during stimulated muscle contractions, as input signals, and applying (500) calculated filters of individual motor units or a common filter of several motor units to input EMG signals captured during stimulated muscle contractions to decompose CMAPs into contributions of individual motor units.