An EEG-based approach for Parkinson's disease diagnosis using Capsule network

Document detail

ID

oai:arXiv.org:2201.00628

Topic

Electrical Engineering and Systems... Computer Science - Artificial Inte... I.2.8

Author

Wang, Shujie Wang, Gongshu Pei, Guangying

Year

2021

listing date

3/31/2022

Keywords

classification eeg accuracy parkinson disease

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Abstract

As the second most common neurodegenerative disease, Parkinson's disease has caused serious problems worldwide.

However, the cause and mechanism of PD are not clear, and no systematic early diagnosis and treatment of PD have 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.

It mapped the frequency band energy of electroencephalogram(EEG) signals to 2-dimensional images using the interpolation method and identified classification using capsule network(CapsNet) and achieved 89.34% classification accuracy for short-term EEG sections.

A comparison of separate classification accuracy across different EEG bands revealed the highest accuracy in the gamma bands, suggesting that we need to pay more attention to the changes in gamma band changes in the early stages of PD.

;Comment: 6 pages,2 image, 3 tables

Wang, Shujie,Wang, Gongshu,Pei, Guangying, 2021, An EEG-based approach for Parkinson's disease diagnosis using Capsule network

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