Automated diagnosis of muscle diseases from EMG signals using empirical mode decomposition based method. (January 2022)

• Record Type: Journal Article
• Authors: Dubey, Rahul; Kumar, Mohit; Upadhyay, Abhay; Pachori, Ram Bilas
• Is Part Of: Biomedical signal processing and control. Volume 71(2022)Part A
• Publication Date: 2022-01
• Subjects: Amyotrophic lateral sclerosis -- Electromyogram -- Myopathy -- Empirical mode decomposition -- Intrinsic mode functions; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103098
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper presents a new method based on EMD for classifying muscle diseases from electromyogram signals. In the proposed method, the suitable IMF for feature selection is determined using the t-test based approach. The two visual features namely area (A) and circumference (CF) are computed using the complex plane plot of the IMF. The proposed algorithm has been trained and validated using a feed forward neural network, SVM, and decision tree. The proposed method is given better performance compare to exiting methods in terms of performace parameter. Abstract: Muscle activity decreases due to various conditions like age factors and muscle diseases namely, amyotrophic lateral sclerosis (ALS) and myopathy. Electromyogram (EMG) signals are regularly explored by specialists to analyze the irregularity of muscles. Manual investigation of EMG signals is a tedious task for medical practitioners. Therefore, this work proposes a new method for classifying the ALS, myopathy, and normal EMG signals. In the proposed method, the empirical mode decomposition (EMD) method is applied to decompose the EMG signals into intrinsic mode functions (IMFs). The suitable IMFs for feature selection are selected using the t-test based approach and used to compute the foot distances denoted as fp 1 and fp 2 by constructing the complex plane plot. The quadrilateral is drawn over a complex plot by considering fp 1 and fp 2 as a diagonal of it, followed by calculating the area (A) and circumference (CF) of the quadrilateral. These measures are utilized for separating the three classes of myopathy, ALS, and normal EMG signals. The proposed algorithm has been trained and validated using a feed forward neural network (FFNN), support vector machine (SVM), and decision tree. The algorithm, when tested with a FFNN, achieved the maximum classification accuracy, sensitivity, and specificity of 99.53%, 99.25% and 99.60%, respectively.