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Motor neuron diseases are the most common neurological disorders found in the age ranges between 35-70 years, which selectively affect the motor neurons. Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease that assails the nerve cells in the brain. This disease progressively degenerates the motor cells in the brain and spinal cord, which are responsible for controlling the muscles that enable human to move around, breathe, speak, and swallow. The electromyography (EMG) signals are the biomedical signals that are used to study the muscle function based on the electrical signal originated from the muscles. As the nervous system controls the muscle activity, the EMG signals can be viewed and analyzed in order to detect the indispensable features of the ALS disease in individuals. In this paper, analyzing the time and frequency domain behaviour of the EMG signals obtained from several normal persons and the ALS patients, some characteristic features, such as autocorrelation, zero crossing rate and Fourier transform are proposed to identify the ALS disease. For the pupose of classification, K-nearest neighbothood classifier is employed in a leave-one out cross validation technique. In order to show the classification performance, an EMG database consisted of 7 normal subjects aged 21-37 years and 6 ALS patients aged 35-67 years is considered and it is found that the proposed method is capable of distinctly separating the ALS patients from the normal persons.

Identifying the Motor Neuron Disease in EMG Signal Using Time and Frequency Domain Features with Comparison