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High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG.
High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG.
Electromyogram (EMG) signals can provide valuable insights into muscle activation and health;however, achieving high-quality signals is challenging. EMG signals are susceptible to contamination, and the acquisition of high-quality EMG signals often requires lengthy setup times and experienced technicians. EMG signal quality analysis is an important domain of research to increase EMG integration into clinical practice, wearable devices, and long-term monitoring. This thesis develops techniques to assess EMG signal quality to improve the ease of EMG acquisition.A literature review of methods for EMG signal quality analysis was conducted. Key areas for further research were identified, including improved selection of high-quality channels within EMG arrays.A process for establishing a ground truth of EMG signal quality based on expert human ratings was developed and used to label an EMG dataset. The intra-rater reliability ranged from 0.76-0.90, and the inter-rater agreement was 0.83, indicating good reliability and that the human rated datasets could be used for future assessments.An automated method of quantifying EMG signal quality was developed in two stages. In Stage I, poor-quality EMG channels were detected and reconstructed. An interpolation-based method of detecting and reconstructing poor-quality channels in high-density EMG (HD-EMG) was developed. Compared with two other detection methods (root mean square and normalized mutual information), the interpolation-based method was most effective for identifying poor iii channels in HD-EMG (precision 95.0%, recall 100%). Reconstruction of poor-quality channels was best performed with a 2-dimensional spline interpolation (correlation = 0.99).In Stage II, machine learning was explored to distinguish between adequate, good, and excellent channels. A random forest regression model provided the best overall result with Spearman correlations of 0.90, 0.91, 0.74, and 0.73 for inter-electrode distances of 10, 20, 30, and 40 mm, respectively. The recommended EMG feature set was an amplitude-based feature (e.g., root mean square) combined with frequency-based feature(s) (e.g., mean frequency). In single channel EMG, the Spearman correlation dropped to 0.55; however, there was potential to distinguish between adequate and excellent quality channels.The automated methods for quantifying EMG signal quality developed in this thesis will lead to improved efficiencies and lower costs for EMG acquisition. Ph.D. journey. I truly could not have asked for a more knowledgeable, dedicated, and supportive supervisor. I would like to extend my sincere thanks to Dr. Dawn MacIsaac for lending her immense expertise and insight to my research. I am immensely grateful to the entire team at the UNB Institute of Biomedical Engineering, and the members of the MacIsaac lab for extending their insights and support. I was also fortunate to have the opportunity to work with Dr. Andrew Law and Dr. Sujoy Ghosh Hajra. Their insights and research outlook inspired me to become a better researcher.Many...
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