Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/69754
Título: Analysis of EMG signals for automated diagnosis of myopathy
Autores/as: Singh, Anushikha
Dutta, Malay Kishore
Travieso González, Carlos Manuel 
Clasificación UNESCO: 3314 Tecnología médica
Palabras clave: Artificial Intelligence
Emg Signals
Myopathy
Signal Processing
Fecha de publicación: 2017
Conferencia: 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017 
Resumen: Myopathy is a very common muscular disease in which muscle fibers do not work properly resulting in muscle weakness, stiffness, cramps etc. Clinically, analysis of Electromyography (EMG) signals plays an important role in diagnosis of myopathy. This work presents signal processing based method for automated diagnosis of myopathy from EMG signals. EMG signals collected from biceps brachii (long head) muscles were analyzed for identification of myopathy using artificial intelligence method. Basic statistical features from EMG signals were extracted and studied to find out discrimination between normal and myopathy. Artificial neural network classifier was used for identification of myopathy. Experiments were carried on a comprehensive database of EMG signal and results are encouraging. The proposed method achieved 87% accuracy with 90% sensitivity for diagnosis of Myopathy disease.
URI: http://hdl.handle.net/10553/69754
ISBN: 9781538630044
DOI: 10.1109/UPCON.2017.8251122
Fuente: 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017,v. 2018-January, p. 628-631
Colección:Actas de congresos
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