Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease

Electromyography (EMG) signals is usable in order to applications of biomedical, clinical, modern human computer interaction and Evolvable Hardware Chip (EHW) improvement. Advanced methods are needed for perception, disassembly, classification and processing of EMG signals acquired from the muscles. Objective of this article is to show various methods and algorithms in order to analyze an electromyogram signal to ensure effective and efficient ways of understanding signal and its nature. Early diagnosis was indispensable and very important in medical health practice. For this reason, it is important to design accurate diagnostic methods. Today, diagnostic methods include evaluating the patient's story, blood tests, and muscle biopsies. In this article, analysis and Electromyogram signals classification and electromyography are mostly used. System has been successfully implemented utilizing MATLAB software that can distinguish EMG signals from different patients. This article also provides the researcher with a well understanding of electromyogram signalling and analysis processes. This information will auxiliary to improve stronger, more resilient and effective implementations.