University Of Cyprus
Research Center of Excellence

A parametric pattern recognition (PPR) algorithm for the identification of similar motor unit action potentials (MUAPS) (recorded with needle electrodes) based on time-domain features that can be very easily monitored and understood by the neurophysiologist. The PPR algorithm was made commercially available through the Excel EMG model, from CADWELL Labs, USA. Furthermore, a new algorithm for the identification of similar MUAPS based on the self-organizing feature maps algorithm that gave higher recognition accuracy was developed. MUAP features were then extracted in the time-domain, frequency-domain, and time-scale domains, and a multi-feature/multi-classifier diagnostic system for the diagnosis of neuromuscular disorders was proposed based on statistical, neural network and genetics based machine learning models. More recent work focused on multi-scale (entropy based wavelet analysis and Amplitude Modulation – Frequency Modulation (AM-FM)) surface EMG (SEMG) analysis recorded from the biceps brachii muscle of subjects suffering from neuromuscular disorders. This work is carried out in collaboration with the Department of Clinical Neurophysiology of the Cyprus Institute of Neurology and Genetics (CING).