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

Oh, Lih Sh and Hagiwara, Yuki and Raghavendra, U and Yuvaraj, Rajamanickam and Arunkumar, N and Murugappan, Rama and Acharya, Rajendra U (2020) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Computing and Applications, 32. pp. 10927-10933. ISSN 0941-0643

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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

Item Type: Article
Uncontrolled Keywords: Computer-aided detection system � Convolutional neural network � Deep learning � Parkinson’s diseases
Subjects: Engineering > MIT Manipal > Instrumentation and Control
Depositing User: MIT Library
Date Deposited: 11 Aug 2021 09:53
Last Modified: 11 Aug 2021 09:53
URI: http://eprints.manipal.edu/id/eprint/157103

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