Time-Varying Delay Estimation Using Common Local All-Pass Filters with Application to Surface Electromyography

Gilliam, C, Bingham, A, Blu, T and Jelfs, B 2018, 'Time-Varying Delay Estimation Using Common Local All-Pass Filters with Application to Surface Electromyography', in Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Calgary, Canada, 15-20 April 2018, pp. 841-845.


Document type: Conference Paper
Collection: Conference Papers

Title Time-Varying Delay Estimation Using Common Local All-Pass Filters with Application to Surface Electromyography
Author(s) Gilliam, C
Bingham, A
Blu, T
Jelfs, B
Year 2018
Conference name ICASSP 2018: Signal Processing and Artificial Intelligence: Changing the World
Conference location Calgary, Canada
Conference dates 15-20 April 2018
Proceedings title Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)
Publisher IEEE
Place of publication United States
Start page 841
End page 845
Total pages 5
Abstract Estimation of conduction velocity (CV) is an important task in the analysis of surface electromyography (sEMG). The problem can be framed as estimation of a time-varying delay (TVD) between electrode recordings. In this paper we present an algorithm which incorporates information from multiple electrodes into a single TVD estimation. The algorithm uses a common all-pass filter to relate two groups of signals at a local level. We also address a current limitation of CV estimators by providing an automated way of identifying the innervation zone from a set of electrode recordings, thus allowing incorporation of the entire array into the estimation. We validate the algorithm on both synthetic and real sEMG data with results showing the proposed algorithm is both robust and accurate.
Subjects Signal Processing
Keyword(s) All-Pass Filters
Surface EMG
Muscle Conductance Velocity
Time-Varying Delay Estimation
DOI - identifier 10.1109/ICASSP.2018.8461390
Copyright notice © 2018 IEEE
ISBN 9781538646588
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