@inproceedings{ae7020a7434a4012b3b4a0ecd34eb7ab,
title = "An EEG-based approach for Parkinson's disease diagnosis using capsule network",
abstract = "As the second most common neurodegenerative disease, Parkinson's disease has caused serious problems worldwide. However, the pathology and mechanism of PD are still unclear, and a systematic early diagnosis and treatment method for PD has not yet been established. Many patients with PD have not been diagnosed or misdiagnosed. In this paper, we proposed an EEG-based approach to diagnosing Parkinson's disease. The frequency band energy of the electroencephalogram (EEG) signal was mapped to the 2-dimensional image by using the interpolation method, and identified classification based on the capsule network (CapsNet) and achieved 89.34% classification accuracy for short-term EEG sections. By comparing the individual classification accuracy of different EEG frequency bands, we found that the gamma band has the highest accuracy, providing potential feature targets for the early diagnosis and clinical treatment of PD.",
keywords = "Parkinson's disease, capsule network, deep learning, electroencephalograph, machine learning",
author = "Shujie Wang and Gongshu Wang and Guangying Pei and Tianyi Yan",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022 ; Conference date: 15-04-2022 Through 17-04-2022",
year = "2022",
doi = "10.1109/ICSP54964.2022.9778541",
language = "English",
series = "2022 7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1641--1645",
booktitle = "2022 7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022",
address = "United States",
}