Two-Time-Series Analysis for Aδ- and C-Fiber Signal 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 differentiate stimulus-evoked events from noise by comparing identical stimuli responses, 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 conduction, 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 identical stimulus responses into an averaged time series, merging the small Aδ- and C-fiber potentials across multiple trials to produce a composite signal with sufficient amplitude for detection above background noise
Solution Approach 2:
The patent segments the total response time series into multiple identical stimulus responses, allowing separate analysis and averaging of individual responses to isolate small fiber potentials from noise through temporal segmentation
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 partial stimulation by using stimulus parameters that are subthreshold for Aβ-fibers but sufficient to activate Aδ- and C-fibers, achieving selective activation of pain fibers without the severe pain caused by supramaximal stimulation of all fiber types
Solution Approach 2:
The patent applies stimulus parameters tailored specifically for Aδ- and C-fiber activation (lower intensity, specific pulse durations) rather than uniform supramaximal stimulation, creating localized selective activation of target fibers while avoiding activation of larger Aβ-fibers that would cause pain
3Quantity of substance
If Aδ- and C-fibers are stimulated to activate sufficient numbers for detection, then signal amplitude increases, but the variation in conduction velocity minimizes summation of action potentials
Solution Approach 1:
The patent applies preliminary temporal averaging by collecting multiple identical stimulus responses before analysis, allowing the variable conduction velocities to be compensated through time-averaging that reconstructs the consistent latency patterns of Aδ- and C-fiber potentials across multiple trials
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.