Paper Info
Title
A Simple and Efficient Near-lossless Compression Algorithm for Surface ElectroMyoGraphy Signals
Abstract
In this paper, a novel near-lossless compression algorithm meant for electromyography (EMG) signals is proposed and its performance is evaluated towards real EMG measurements. Differently from other near-lossless algorithms, the proposed one does not rely on either matrix decompositions or complex transformations but exploits only a straightforward dynamic range compression and a simple encoding technique. Therefore, considering its inherent low complexity and low memory requirements, it can be easily implemented in resources constrained microcontrollers as those included in low-cost measurement instruments and e-Health Internet of Things applications. The algorithm has been tested on a dataset including dynamic EMG measurements carried out in a real world measurement campaign on 8 different subjects, where, for each subject, the EMG signals were recorded from 8 different muscles during a pedaling session. Analytical and experimental results revealed that the proposed compression technique is able to achieve a compression ratio (CR) up to 80% with a percentage root mean square distortion (PRD) in the range between 0.34% and 13.7%. Moreover, differently from the other compression algorithms described in the literature, the proposed one allows fixing the maximum absolute error a priori thus making it possible to control and limit the desired distortion level besides the compression procedure.
Year
DOI
Venue
2022
10.1109/MEMEA54994.2022.9856570
2022 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA 2022)
Keywords
DocType
Citations 
Electromyography (EMG), near-lossless compression, biomedical signal processing
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Giuseppe Campobello121.72
Cristiano De Marchis200.68
Giovanni Gugliandolo301.01
Alberto Giacobbe400.34
Giovanni Crupi500.34
Nicola Donato601.69