Automatic detection of poor quality signals as a pre-processing scheme in the analysis of sEMG in swallowing

Cuadros-Acosta, J.; Duque, Andrés

doi:10.1016/j.bspc.2021.103122

Cited by 5 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this sense, several proposals offer filtering methods to improve the signal quality [2], [3]. However, since, in many cases, the quality of the signals is not optimal [4], it is necessary to use more than one feature to describe them. On the other hand, the choice of features to describe the signal directly influences the classification quality [5].…”

mentioning

confidence: 99%

Feature Set to sEMG Classification Obtained With Fisher Score

Toledo-Pérez,

Aviles,

Toledo-Pérez

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The best way to represent EMG signals for classification is a topic that has been widely studied due to the need to improve precision when identifying the type of movement being performed. However, by increasing the number of features when forming a matrix that represents the signals, the processing time increases since it not only involves calculating the features that are extracted from the signal but also the time that the classifier takes to answer. The central purpose of this research is to develop and validate a methodology that uses the Fisher Score to select a set of features in the classification of sEMG signals. This selected set is descriptive enough to achieve high levels of accuracy in detecting EMG signal patterns across multiple subjects. The analysis shows that using a variant of MAV, SSC, WAMP, RMS, and the maximum value together with the Shannon entropy and zero crossings of the Wavelet transform has an accuracy greater than 99%. Finally, a group of features is proposed to classify EMG signals that yield an accuracy greater than 98% and do not require more than 15 ms of processing time.

show abstract

mentioning

confidence: 99%

Feature Set to sEMG Classification Obtained With Fisher Score

Toledo-Pérez,

Aviles,

Toledo-Pérez

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Cuadros-Acosta et al [86] explored machine learning methods to assess the quality of EMG collected from the neck and jaw muscles for the purpose of assessing dysphagia. EMG segments were labeled as either good quality or bad quality.…”

Section: Single Channel Emgmentioning

confidence: 99%

“…SVM, k-nearest neighbours, and random forest models all achieved classification accuracies of over 97%. The authors recommended the use of a random forest classifier trained on three features (two power spectrum-derived features, and the mean of the RMS envelope [86]) to preserve computational efficiency.…”

Section: Single Channel Emgmentioning

confidence: 99%

Biomedical Signal Quality Analysis for Electromyography

Farago

View full text Add to dashboard Cite

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...

show abstract