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

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 u...

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Veröffentlicht in:Neural computing & applications Jg. 32; H. 15; S. 10927 - 10933
Hauptverfasser: Oh, Shu Lih, Hagiwara, Yuki, Raghavendra, U., Yuvaraj, Rajamanickam, Arunkumar, N., Murugappan, M., Acharya, U. Rajendra
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.08.2020
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Artificial neural networks
Automation
Brain
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Deep learning
Diagnosis
Disease
Dopamine
Electroencephalography
Engineering
Epilepsy
Image Processing and Computer Vision
Machine learning
Medical diagnosis
Mental disorders
Neural networks
Neurons
Parkinson's disease
Probability and Statistics in Computer Science
S.I. : Computer aided Medical Diagnosis
Deep learning
Computer-aided detection system
Convolutional neural network
Parkinson’s disease
ISSN:0941-0643, 1433-3058
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-3689-5