Classification of Parkinson’s Disease Using the Frequency-Specific Changes of Resting Brain Activity

Jiaqi Tang, Runhan Zhang, Jiayi Pu

2022

Abstract

Resting state functional magnetic resonance imaging has become a widely used method for diagnosingof Parkinson’s disease. Nevertheless, machine-learning technology has not been used to better classify disease results from MRI signals. Here, the slow-frequency fluctuation amplitudes of patients and healthy controls are measured as input to the machine learning model. The features and classification capabilities of the machine learning model are respectively evaluated by the T-test and linear support vector machine. . The signals from three frequency bands (Slow-5, 0.01-0.03 Hz; Slow-4, 0.03-0.08 Hz; conventional, 0.01-0.08 Hz) are analyzed. We found that in the classification of Parkinson’s disease, Slow-4 signal provides more information than Slow-5, and its classification ability is comparable to traditional frequency bands. This study shows that machine-learning technology is a promising method of detecting abnormal areas and activities in Parkinson’s disease, and multi-band data can give us more specific message.

Paper Citation

in Harvard Style

Tang J., Zhang R. and Pu J. (2022). Classification of Parkinson’s Disease Using the Frequency-Specific Changes of Resting Brain Activity. In Proceedings of the 4th International Conference on Biotechnology and Biomedicine - Volume 1: ICBB; ISBN 978-989-758-637-8, SciTePress, pages 142-148. DOI: 10.5220/0012015000003633

in Bibtex Style

@conference{icbb22,
author={Jiaqi Tang and Runhan Zhang and Jiayi Pu},
title={Classification of Parkinson’s Disease Using the Frequency-Specific Changes of Resting Brain Activity},
booktitle={Proceedings of the 4th International Conference on Biotechnology and Biomedicine - Volume 1: ICBB},
year={2022},
pages={142-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012015000003633},
isbn={978-989-758-637-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Biotechnology and Biomedicine - Volume 1: ICBB
TI - Classification of Parkinson’s Disease Using the Frequency-Specific Changes of Resting Brain Activity
SN - 978-989-758-637-8
AU - Tang J.
AU - Zhang R.
AU - Pu J.
PY - 2022
SP - 142
EP - 148
DO - 10.5220/0012015000003633
PB - SciTePress