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

• Published in: Neural computing & applications Vol. 32; no. 15; pp. 10927 - 10933
• Main Authors: Oh, Shu Lih, Hagiwara, Yuki, Raghavendra, U., Yuvaraj, Rajamanickam, Arunkumar, N., Murugappan, M., Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2020; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s00521-018-3689-5

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.