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

  • S.I. : Computer aided Medical Diagnosis
  • Published:
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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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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore

    Shu Lih Oh, Yuki Hagiwara & U. Rajendra Acharya

  2. Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India

    U. Raghavendra

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Rajamanickam Yuvaraj

  4. Department of Electronics and Instrumentation, SASTRA University, Thanjavur, India

    N. Arunkumar

  5. Kuwait College of Science and Technology, Doha, Kuwait

    M. Murugappan

  6. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore

    U. Rajendra Acharya

  7. School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, 47500, Subang Jaya, Malaysia

    U. Rajendra Acharya

Authors
  1. Shu Lih Oh
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  2. Yuki Hagiwara
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  3. U. Raghavendra
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  4. Rajamanickam Yuvaraj
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  5. N. Arunkumar
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  6. M. Murugappan
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  7. U. Rajendra Acharya
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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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  • DOI: https://doi.org/10.1007/s00521-018-3689-5

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