title: DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification.
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.
authors.affiliation: Department of Electrical and Electronic Engineering , BUET , Dhaka 1000 , Bangladesh.
authors.forename: Abul Barkat Mollah Sayeed Ud
authors.initials: AB
authors.lastname: Doulah
authors.affiliation: Department of Electrical and Electronic Engineering , BUET , Dhaka 1000 , Bangladesh.
authors.forename: Shaikh Anowarul
authors.initials: SA
authors.lastname: Fattah
authors.affiliation: Department of Electrical and Computer Engineering , Concordia University , Montreal , QC , H3G 1M8 , Canada.
authors.forename: Wei-Ping
authors.initials: WP
authors.lastname: Zhu
authors.affiliation: Department of Electrical and Computer Engineering , Concordia University , Montreal , QC , H3G 1M8 , Canada.
authors.forename: M Omair
authors.initials: MO
authors.lastname: Ahmad
keywords: DCT-based feature extraction; EMG signal; K-nearest neighbourhood classifier; MUAP; amyotrophic lateral sclerosis; between-class separation; clinical EMG database; discrete cosine transform domain feature extraction scheme; discrete cosine transforms; diseases; electromyography; electromyography signal; feature extraction; high-energy DCT coefficients; medical signal processing; motor unit action potential; neuromuscular disease classification; neuromuscular stimulation; signal classification; template matching-based decomposition technique; within-class compactness