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

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...