Diagnosis of amyotrophic lateral sclerosis (ALS) disorders based on electromyogram (EMG) signal analysis and feature selection

• Autorzy: Mokdad Aicha , Debbal Sidi Mohammed El Amine , Meziani Fadia

Identyfikatory

DOI

10.2478/pjmpe-2020-0018

• Języki publikacji: EN

Abstrakty

EN

Electromyogram signal (EMG) provides an important source of information for the diagnosis of neuromuscular disorders. In this study, we proposed two methods of analysis which concern the bispectrum and continuous wavelet transform (CWT) of the EMG signal then a comparison is made to select which one is the most suitable to identify an abnormality in biceps brachii muscle in the main purpose is to assess the pathological severity in bifrequency and time-frequency analysis applying respectively bispectrum and CWT. Then four time and frequency features are extracted and three popular machine learning algorithms are implemented to differentiate neuropathy and healthy conditions of the selected muscle. The performance of these time and frequency features are compared using support vector machine (SVM), linear discriminate analysis (LDA) and K-Nearest Neighbor (KNN) classifier performance. The results obtained showed that the SVM classifier yielded the best performance with an accuracy of 95.8%, precision of 92.59% and specificity of 92%. followed by respectively KNN and LDA classifier that achieved respectively an accuracy of 92% and 91.5%, precision of 92% and 85.4%, and specificity of 92% and 83%.

Słowa kluczowe

EN

bispectrum; continuous wavelet transform; CWT; support vector machine; SVM; linear discriminate analysis; LDA; K-Nearest Neighbor; KNN

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2020
• Tom: Vol. 26, Iss. 3
• Strony: 155--160
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Mokdad Aicha

• Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bekr Belkaid Tlemcen, Tlemcen, Algeria

autor

Debbal Sidi Mohammed El Amine

• Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bekr Belkaid Tlemcen, Tlemcen, Algeria

autor

Meziani Fadia

• Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bekr Belkaid Tlemcen, Tlemcen, Algeria

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).