Neuromuscular disease classification based on mel frequency cepstrum of motor unit action potential

In this paper, mel-frequency cepstral coefficient (MFCC) based feature extraction scheme is proposed for the classification of electromyography (EMG) signal into normal and a neuromuscular disease, namely the amyotrophic lateral sclerosis (ALS). Instead of employing the MFCC directly on EMG data, it is employed on the motor unit action potentials (MUAPs) extracted from the EMG signal via template matching based decomposition technique. Unlike conventional MUAP based methods, only one MUAP with maximum dynamic range is selected for MFCC based feature extraction. First few MFCCs 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 neighborhood (KNN) 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.