Evaluation of interpolation methods for EMG arrays

Farago, Emma; Chan, Adrian D. C.

doi:10.1109/i2mtc48687.2022.9806595

Cited by 3 publications

(9 citation statements)

References 15 publications

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“…In [ 13 ], various interpolation-based methods were explored for the reconstruction of a target channel in EMG arrays. The target channel is defined as the channel being interpolated.…”

Section: Methodsmentioning

confidence: 99%

“…Table 1 shows a summary of the 2D interpolation results [ 13 ]. A 2D biharmonic spline interpolation using the nearest 24 channels to the missing channel provided the best interpolation result (mean PRD = 15.5%, mean correlation = 0.98), and performance remained high even with fewer channels.…”

Section: Methodsmentioning

confidence: 99%

“…Following outlier detection, poor-quality channels could simply be discarded; however, downstream data processing often assumes a complete HD-EMG dataset, and recollecting the data is often undesirable. Reconstruction of poor or missing channels could assist in preserving the original dimensions of the HD-EMG array and simplify EMG clinical analysis or research [ 11 , 13 ]. Reconstruction of HD-EMG channels has been proposed based on interpolation methods [ 8 , 11 , 13 , 14 ], but there has been limited work on systematically investigating these methods.…”

Section: Introductionmentioning

confidence: 99%

“…Reconstruction of poor or missing channels could assist in preserving the original dimensions of the HD-EMG array and simplify EMG clinical analysis or research [ 11 , 13 ]. Reconstruction of HD-EMG channels has been proposed based on interpolation methods [ 8 , 11 , 13 , 14 ], but there has been limited work on systematically investigating these methods.…”

Section: Introductionmentioning

confidence: 99%

“…Farago et al [ 13 ] evaluated interpolation techniques by comparing the percent residual difference (PRD) and correlation between a target channel (simulated as a missing channel to be reconstructed) and an interpolation of nearby electrodes. They determined that a two-dimensional (2D) spline interpolation using the nearest 24 electrodes to the missing channel provided the best interpolation result (median PRD = 12.0%, median correlation = 0.99).…”

Section: Introductionmentioning

confidence: 99%

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Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

Farago

Chan

2023

Sensors

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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.

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“…In [ 13 ], various interpolation-based methods were explored for the reconstruction of a target channel in EMG arrays. The target channel is defined as the channel being interpolated.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

Farago

Chan

2023

Sensors

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A Pilot Study on Quantifying Signal Quality in High-Density Surface Electromyography

Farago,

Chan

2023

IEEE Trans. Instrum. Meas.

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Biomedical Signal Quality Analysis for Electromyography

Farago

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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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Evaluation of interpolation methods for EMG arrays

Cited by 3 publications

References 15 publications

Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

A Pilot Study on Quantifying Signal Quality in High-Density Surface Electromyography

Biomedical Signal Quality Analysis for Electromyography

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