This paper presents neuromuscular disease classification using seventeen time-domain feature extraction, feature reduction, and a machine learning method. Electromyography (EMG) signals were collected from one of the neuromuscular diseases i.e. Parkinson Disease (PD). One of typical PD stages (classes) namely healthy, possible, probable, and definite were used in this study. Ten Subjects of each class participated in this study. The 10 subjects of the healthy class were collected from the University student and the other 10 subjects for each possible, probably, and definite class were collected from Kariadi General Hospital in Semarang, Indonesia. The EMG signals of each subject were proceeded with 17 time-domain feature extraction and followed by the dimensional feature reduction based on Principal Component Analysis (PCA). The total 17 features of each class were reduced up to 4 new features using PCA namely Principal Component (PC). A pair combination of PCs was used for training and testing using Support Vector Machine (SVM). A classification results show that the new features of PCA increased the classification accuracy.

Original languageEnglish
Title of host publicationIECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
ISBN (Electronic)9798350331820
Publication statusPublished - 2023
Event49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore
Duration: 16 Oct 202319 Oct 2023

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
ISSN (Print)2162-4704
ISSN (Electronic)2577-1647


Conference49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023


  • PCA
  • SVM
  • classification
  • machine learning
  • parkinson disease


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