Using the frequency signature to detect muscular activity in weak and noisy myoelectric signals. (July 2019)

• Record Type: Journal Article
• Authors: D'Anna, Carmen; Varrecchia, Tiwana; Schmid, Maurizio; Conforto, Silvia
• Is Part Of: Biomedical signal processing and control. Volume 52(2019)
• Page Start: 69
• Page End: 76
• Publication Date: 2019-07
• Subjects: Electromyography -- Low signal-to-noise ratio -- Timing detection -- Zero-crossing -- Hilbert; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.02.026
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The method is based on the frequency characteristics of the weak and noisy EMG signals. A clustering approach is proposed to detect muscular activity. The method was tested on a set of simulated EMG data. Abstract: The detection of muscular activity for signals characterized by low amplitude and low signal-to-noise ratio – weak and noisy – is a challenge in biomedical data processing. The aim of this paper is to introduce a method based only on the frequency characteristics of the weak and noisy EMG to detect muscular activity. The algorithm is window-based and consists of two processing steps: i) estimation of zero-crossings and mean instantaneous frequency of the signal; ii) clustering by a k-means approach to separate the muscular activity from the silent phases. We assessed the method on 320 simulated EMG signals that have been generated from a small number of synthetic motor units working at a low firing rate and then manipulated by adding Gaussian noise to simulate four different levels of low signal-to-noise ratio (SNR). Tests were carried on by changing the window dimension – fifteen different window lengths – and the amount of overlap of the window along the signal – four different values of overlapping. The performance of the algorithm was evaluated by calculating the temporal bias of the onset detection, the percentage error made when estimating the activity duration, and the F1 score as a measure of accuracy. The results showed that the algorithm performance does not depend from SNR but depends on both window length and overlap. The detection accuracy ranges from 96% to 98% depending on combinations of window length and overlap, while for specific combinations of window length and overlap, the amount of temporal bias fell below 20 ms. These results open promising scenarios for the application of this algorithm to real weak and noisy EMG data.