DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification. Issue 1 (16th June 2014)

• Record Type: Journal Article
• Authors: Doulah, Abul Barkat Mollah Sayeed Ud; Fattah, Shaikh Anowarul; Zhu, Wei‐Ping; Ahmad, M. Omair
• Is Part Of: Healthcare technology letters. Volume 1:Issue 1(2014)
• Page Start: 26
• Page End: 31
• Publication Date: 2014-06-16
• Subjects: electromyography -- diseases -- neuromuscular stimulation -- discrete cosine transforms -- feature extraction -- signal classification -- medical signal processing; within‐class compactness -- between‐class separation -- MUAP -- high‐energy DCT coefficients -- clinical EMG database -- K‐nearest neighbourhood classifier -- DCT‐based feature extraction -- template matching‐based decomposition technique -- EMG signal -- amyotrophic lateral sclerosis -- neuromuscular disease classification -- electromyography signal -- motor unit action potential -- discrete cosine transform domain feature extraction scheme; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2013.0036
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : A feature extraction scheme based on discrete cosine transform (DCT) of electromyography (EMG) signals is proposed for the classification of normal event and a neuromuscular disease, namely the amyotrophic lateral sclerosis. Instead of employing DCT directly on EMG data, it is employed on the motor unit action potentials (MUAPs) extracted from the EMG signal via a template matching‐based decomposition technique. Unlike conventional MUAP‐based methods, only one MUAP with maximum dynamic range is selected for DCT‐based feature extraction. Magnitude and frequency values of a few high‐energy DCT coefficients corresponding to the selected MUAP are used as the desired feature which not only reduces computational burden, but also offers better feature quality with high within‐class compactness and between‐class separation. For the purpose of classification, the K ‐nearest neighbourhood classifier is employed. Extensive analysis is performed on clinical EMG database and it is found that the proposed method provides a very satisfactory performance in terms of specificity, sensitivity and overall classification accuracy.