Author(s): Akhila Devi.B.V | S.Suja Priyadharsini
Journal: International Journal of Soft Computing & Engineering
ISSN 2231-2307
Volume: 3;
Issue: 5;
Start page: 105;
Date: 2013;
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Keywords: Adaptive neuro-fuzzy inference system (ANFIS) | Discrete Wavelet Transform (DWT) | Electromyography (EMG) Fuzzy SVM (FSVM) | Support vector machine (SVM)
ABSTRACT
Biomedical signals are collection of electrical signals which generated from any organ that signal represents a physical variable of interest. Electromyography (EMG) is a technique for evaluating and recording of electrical activities produced from skeletal muscles. There are so many applications of EMG signals. Major interests lies in the field of clinical as well as biomedical engineering.EMG is used as a diagnostic tool for identifying neuromuscular disorders .Motor unit action potentials (MUPS) provides information about neuromuscular disorders. Traditionally neurophysiologist can access MUPs information from their shapes and patterns using an oscilloscope. But MUPs from different motor neurons will overlap leads to the formation of interference pattern and it is difficult to detect individual shapes accurately. For this reason a number of computer based quantitative EMG analysis algorithm have been developed. In this work, different types of learning methods were used to classify EMG signals. The model automatically classifies EMG signals into normal, myopathy and neuropathy. In order to extract useful information from the EMG signals different feature extraction methods such as discrete wavelet transform(DWT) and auto regressive modeling(AR)are implemented. Adaptive neuro-fuzzy inference system (ANFIS) with hybrid learning algorithm, support vector machine (SVM) and fuzzy support vector machine (FSVM) were compared in relation to their accuracy in the classification of EMG signals. Based on the impacts of features on the EMG signal classification, different results were obtained through analysis of the soft computing techniques.
Journal: International Journal of Soft Computing & Engineering
ISSN 2231-2307
Volume: 3;
Issue: 5;
Start page: 105;
Date: 2013;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Adaptive neuro-fuzzy inference system (ANFIS) | Discrete Wavelet Transform (DWT) | Electromyography (EMG) Fuzzy SVM (FSVM) | Support vector machine (SVM)
ABSTRACT
Biomedical signals are collection of electrical signals which generated from any organ that signal represents a physical variable of interest. Electromyography (EMG) is a technique for evaluating and recording of electrical activities produced from skeletal muscles. There are so many applications of EMG signals. Major interests lies in the field of clinical as well as biomedical engineering.EMG is used as a diagnostic tool for identifying neuromuscular disorders .Motor unit action potentials (MUPS) provides information about neuromuscular disorders. Traditionally neurophysiologist can access MUPs information from their shapes and patterns using an oscilloscope. But MUPs from different motor neurons will overlap leads to the formation of interference pattern and it is difficult to detect individual shapes accurately. For this reason a number of computer based quantitative EMG analysis algorithm have been developed. In this work, different types of learning methods were used to classify EMG signals. The model automatically classifies EMG signals into normal, myopathy and neuropathy. In order to extract useful information from the EMG signals different feature extraction methods such as discrete wavelet transform(DWT) and auto regressive modeling(AR)are implemented. Adaptive neuro-fuzzy inference system (ANFIS) with hybrid learning algorithm, support vector machine (SVM) and fuzzy support vector machine (FSVM) were compared in relation to their accuracy in the classification of EMG signals. Based on the impacts of features on the EMG signal classification, different results were obtained through analysis of the soft computing techniques.
