access icon openaccess DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification

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.

Inspec keywords: signal classification; medical signal processing; discrete cosine transforms; electromyography; diseases; feature extraction; neuromuscular stimulation

Other keywords: clinical EMG database; high-energy DCT coefficients; DCT-based feature extraction; template matching-based decomposition technique; neuromuscular disease classification; MUAP; amyotrophic lateral sclerosis; K-nearest neighbourhood classifier; EMG signal; within-class compactness; motor unit action potential; discrete cosine transform domain feature extraction scheme; between-class separation; electromyography signal

Subjects: Biology and medical computing; Signal processing and detection; Integral transforms; Electrodiagnostics and other electrical measurement techniques; Electrical activity in neurophysiological processes; Function theory, analysis; Bioelectric signals; Digital signal processing; Integral transforms

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