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Neuromuscular Disorders Assessment by FPGA-Based SVM Classification of Synchronized EEG/EMG

  • Daniela De VenutoEmail author
  • Giovanni Mezzina
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 573)

Abstract

Exploiting the synchronized assessment of the neuromuscular implications, this paper proposes an embedded digital architecture for the assessment of the movements’ automatism and the reduction of pre-motor function capability. The study can enable a forward recognition of the Parkinson’s disease (PD) progression stages, which are characterized by muscular disorders. The architecture, implemented on Altera Cyclone V FPGA, classifies in real-time these physiological disorders during the walk. The system operates on 8 surface EMG (limbs) and 7 EEG (motor-cortex). The signals, synchronously acquired and processed, undergo to a features extraction (FE) in the time-frequency domains. The features are time-continuously processed (in chronological order) from an innovative on-going Support Vector Machine (SVM) classifier. The SVM identifies and categorizes the patient pathology severity. Experimental results from 4 subjects affected by mild (n = 2) and heavy PD (n = 2) show an accuracy 93.97% ± 2.1% in PD stages recognition.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical and Information EngineeringPolitecnico Di BariBariItaly

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