A deep learning approach for Parkinson’s disease diagnosis from EEG signals

Abstract

An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and twenty normal subjects in this study. A thirteen-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage.

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Correspondence to U. Rajendra Acharya.

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Oh, S.L., Hagiwara, Y., Raghavendra, U. et al. A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput & Applic 32, 10927–10933 (2020). https://doi.org/10.1007/s00521-018-3689-5

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Keywords

  • Computer-aided detection system
  • Convolutional neural network
  • Deep learning
  • Parkinson’s disease