Two-Time-Series Signal Analysis for Small Fiber Conduction Detection
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Solution Overview
Problem
Current clinical nerve conduction studies are unable to effectively differentiate the small electrical signals generated by Aδ- and C-fibers from background noise, as these fibers have smaller diameters and varying conduction velocities, making them difficult to examine with commercially available EMG machines.
Innovation Solution
A method involving the analysis of two time series using an algorithm (Alg) to identify and differentiate stimulus-evoked events from noise by comparing identical stimuli applied to nerve fibers, allowing for the detection of Aδ- and C-fiber conduction velocities using commercially available EMG equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EMG machines are used to record nerve signals, then Aβ-fiber potentials can be easily recorded with sufficient amplitude, but Aδ- and C-fiber potentials cannot be differentiated from background noise due to their small amplitude
Solution Approach 1:
The patent combines multiple individual fiber potentials into a composite signal by summing potentials from multiple Aδ- and C-fibers. This merging approach increases the overall signal amplitude to a level that can be differentiated from background noise, while preserving the characteristic conduction velocity information of small fiber populations.
Solution Approach 2:
The patent introduces conduction velocity as an additional dimension for signal differentiation. Instead of relying solely on amplitude, the system analyzes the temporal dispersion of potentials to calculate conduction velocities, creating a velocity-based dimension that enables identification of small fiber potentials that would otherwise be indistinguishable from noise.
2Reliability
If supramaximal stimulation is applied to excite Aδ- and C-fibers, then these fibers can be activated, but severe pain is caused to the subject
Solution Approach 1:
The patent applies localized stimulation to a restricted area of the nerve, activating only a limited number of Aδ- and C-fibers rather than all small fibers. This local activation approach reduces the total pain sensation while still generating sufficient signals for reliable detection and conduction velocity measurement.
Solution Approach 2:
The patent uses partial activation of small fiber populations rather than attempting to excite all Aδ- and C-fibers. By stimulating a subset of fibers, the system achieves reliable signal detection for conduction velocity analysis while minimizing the harmful pain effect that would result from complete activation.
3Measurement precision
If Aδ- and C-fibers are stimulated strongly enough to be detected, then conduction velocity data can be obtained, but the signals become indistinguishable from electronic background noise
Solution Approach 1:
The patent applies preliminary signal processing operations including averaging multiple sweeps and filtering to enhance the signal-to-noise ratio before conduction velocity calculation. These preliminary actions prepare the signal by removing random noise components and reinforcing consistent potential patterns, enabling accurate velocity measurement.
Solution Approach 2:
The system uses feedback mechanisms to adjust stimulation parameters and signal processing settings based on the detected signal characteristics. By monitoring the recorded potentials and their consistency across multiple trials, the system optimizes the detection parameters to maximize conduction velocity measurement precision while minimizing noise interference.
Data Source
AI summary
A method may include obtaining first and second time series (TS1), (TS2) of stimulation data, and a first and second time series of control data. TS1, TS2 may provide a plurality of pairs of data points such that each of the plurality of pairs include corresponding data points from both TS1 and TS2. The obtained time series may be analyzed by applying an algorithm (Alg) to TS1 and TS2 of stimulation data to create an algorithm value corresponding to each of the plurality of pairs of data points. Alg=(|TS1|+|TS2|)/2−|TS1−TS2|. Positive algorithm values for a predetermined period of time (AlgVarTime) may be summed to create a signal. Peak(s) in the signal may be determined, and a conduction velocity may be determined using a latency and a distance between a stimulus electrode and a recording electrode.