An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection

Artameeyanant et al. SpringerPlus (2016) 5:2101 DOI 10.1186/s40064-016-3772-2 Open Access RESEARCH An EMG‑based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection Patcharin Artameeyanant1, Sivarit Sultornsanee2 and Kosin Chamnongthai1* *Correspondence: 1 Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, 126 Pracha‑uthit Rd., Bangmod, Thungkhru, Bangkok 10140, Thailand Full list of author information is available at the end of the article Abstract Background: Electromyography (EMG) signals recorded from healthy, myopathic, and amyotrophic lateral sclerosis (ALS) subjects are nonlinear, non-stationary, and similar in the time domain and the frequency domain. Therefore, it is difficult to classify these various statuses. Methods: This study proposes an EMG-based feature extraction method based on a normalized weight vertical visibility algorithm (NWVVA) for myopathy and ALS detection. In this method, sampling points or nodes based on sampling theory are extracted, and features are derived based on relations among the vertical visibility nodes with their amplitude differences as weights. The features are calculated via selective statistical mechanics and measurements, and the obtained features are assembled into a feature matrix as classifier input. Finally, powerful classifiers, such as k-nearest neighbor, multilayer perceptron neural network, and support vector machine classifiers, are utilized to differentiate signals of healthy, myopathy, and ALS cases. Results: Performance evaluation experiments are carried out, and the results revealed 98.36% accuracy, which corresponds to approximately a 2% improvement compared with conventional methods. Conclusions: An EMG-based feature extraction method using a NWVVA is proposed and implemented to detect healthy, ALS, and myopathy statuses. Keywords: EMG signal, Complex network, Normalized weight vertical visibility algorithm, Network measurements, k-Nearest neighbor, Multilayer perceptron neural network, Support vector machine Background Currently, amyotrophic lateral sclerosis (ALS), or neuropathy, a rapidly progressive, invariably fatal neurological disease that affects the neurons responsible for controlling voluntary muscles in the arms, legs, and face (Ahdab et al. 2013), is diagnosed in approximately 6000 people each year (ALS Association 2016). In the USA alone, the number of patients is estimated to be as many as 20,000. This disease belongs to a group of motor neuron disorders and eventually leads to death. According to previous studies, patients who are diagnosed live an average of 3 years, and 20, 10, and 5% of them die in 5, 10, and © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Artameeyanant et al. SpringerPlus (2016) 5:2101 20 years, respectively. Myopathy is a neuromuscular disorder that causes muscle cramps, stiffness, and spasms, and muscle weakness is the primary symptom due to dysfunction of muscle fibers and eventually causes death. In accordance with the 2005 statistics data of the USA (Oskarsson 2011), approximately 2.97 million patients have been diagnosed with myopathy. In diagnosing both aforementioned diseases, medical doctors first interview patients, although sometimes the patients are extremely weak and unavailable to even speak. In such cases, electromyography (EMG) is used to analyze muscle signals to assist a specialized neurological expert to diagnose both myopathy and ALS (Kincaid 2015; Weiss et al. 2015; Gitiaux et al. 2016). However, the number of neurological experts is quite limited, and therefore, an automatic system to assist diagnosis is urgently required. Such a system could be used not only for assisting diagnosis but also for periodic detection and monitoring. In performing diagnoses based on EMG signals, a primary issue is that the system must correctly classify an EMG signal as ALS or myopathic, because different therapies and drugs are used to treat the two disorders. In studying and developing this kind of system, EMG signals is regarded as an excellent approach for acquiring data (Yousefi and Hamilton-Wright 2014), which records the corresponding electrical to activity of motor units in the neuromuscular system. Analysis of EMG signals is generally performed in two cases. The first is for prosthetic device control and human–machine interactions (Naik and Kumar 2011; Naik et al. 2014, 2016a; Arjunan et al. 2014, 2015; Guo et al. 2015; Naik and Nguyen 2015). The second is for diagnosing disorders (Xie et al. 2014). Neuromuscular disorders are related to pathological changes in the structure of the motor unit and can be generally divided into two categories: muscular (myopathy) and neuronal (neuropathy) (Nikolic 2001) disorders. The need for distinct classification between myopathy and neuropathy originates from the differences between the causes of the diseases, which is a critical factor in determining treatment. The development of a highly accurate diagnostic system based on EMG readings would provide a promising way to improve the assessment of neuromuscular disorders (Gokgoz and Subasi 2015). Highly accurate classification problems depend on the crucial step of feature extraction. If features are extracted sufficiently well, it is possible to obtain outstanding classification performance. Previous studies related to feature extraction of EMG signals have been proposed in three main domains, the frequency domain, the time–frequency domain, and the complex network domain. In frequency analyses, fast Fourier transform (FFT) and autoregressive (AR) spectral models have been employed to extract features (Guler and Kocer 2005; Subasi et al. 2006; Kocer 2010; Sultornsanee et al. 2011). Power spectral analysis of FFT and AR can represent the characteristics of the signal. However, different subjects have different signal strengths in addition to nonlinearity and chaos. Various types of wavelets have been used to analyze EMG signals in the time–frequency domain (Gokgoz and Subasi 2015; Hu et al. 2005; Istenic et al. 2010; Subasi 2012a, b, 2013a, b). The advantage of the method is the ability to perform analyses in various sub-bands. However, computational complexity might occur at the initial stages, such as when selecting the mother wavelet. Additionally, the level of decomposition is related to the number of sub-bands. Using many sub-bands with various features in each sub-band results in a high dimension of input for the classifier. Mishra et al. (2016) and Naik et (...truncated)