NDLI: A new digital signal processing technique for the estimation of motor unit action potential templates

Content Provider	IEEE Xplore Digital Library
Author	Kanoun, S. Ali, N.
Copyright Year	2009
Description	Author affiliation: ALfateh university in Tripoli, Libya (Kanoun, S.)
Abstract	Accurate estimation of actual motor unit action potential template parameters from noisy measurements is quite important for quantitative electromyograhic (EMG) analysis. This is especially true in case of clinical applications. In This paper presents a new method for the accurate estimation of motor unit action potential (MUAP) templates, contaminated by instrumentation and biological noise, and by the interference of MUAPs of neighboring motor units. This method is based on three processes: Interfering MUAPs are detected by a sensitive detection algorithm. The sample points of these MAUPs are rejected and excluded from the estimation process. An α-trimmed mean is then calculated for each sample point of all MUAPs within a motor unit action potential train (MUAPT). To further smooth the baseline of the template while preserving its shape, a simple wavelet based de-noising technique is applied to the resulting template. The proposed method is compared to traditional estimation techniques such as mean, median, mode, σ-trimmed mean and Bayesian estimation. Using the isolated MUAPs within the 46 trains of concentric needle-detected MUAPs (25–447 per train) obtained using EMG signal decomposition, template baseline RMS levels and their spike shape fidelities were measured. Overall, this newly developed method compared to traditional estimation techniques provided the smoothest baselines and an acceptable spike fidelity factor.
Starting Page	537
Ending Page	542
File Size	149353
Page Count	6
File Format	PDF
ISBN	9781424458424
DOI	10.1109/ICCES.2009.5383206
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-14
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape measurement Instruments Noise reduction Interference Electromygraphy Motor unit action potential template Signal resolution Bayesian methods Baseline smoothing Digital signal processing Electromyography Interferences Rejection Pollution measurement Detection algorithms Discrete Wavelet Transform Threshold
Content Type	Text
Resource Type	Article

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