TY - GEN
T1 - Diagnosing Parkinson’s Disease Using Multimodal Physiological Signals
AU - Guo, Guoxin
AU - Wang, Shujie
AU - Wang, Shuaibin
AU - Zhou, Zhiyu
AU - Pei, Guangying
AU - Yan, Tianyi
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease. Due to the complex etiology and diverse clinical symptoms, it’s difficult to accurately diagnose PD. In this study, we applied multimodal physiological signals, which include electroencephalography (EEG), electrocardiogram (ECG), photoplethysmography (PPG), and respiratory (RA), to classify PD and healthy control (HC) based on a multimodal support vector machine (SVM). Our experiments achieved an accuracy of 96.03%. Besides, we performed statistical analysis on the four types of physiological data of the PD group and the HC group. Results showed that the EEG of non-dementia PD patients had a significant decrease in high-frequency power, and the high-frequency energy distribution of the normalized PPG signal increased compared with HC. The current study suggests that combining the physiological information of multiple models and machine learning methods could improve the diagnosis accuracy of PD disease and be a potentially effective method of clinical diagnosis.
AB - Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease. Due to the complex etiology and diverse clinical symptoms, it’s difficult to accurately diagnose PD. In this study, we applied multimodal physiological signals, which include electroencephalography (EEG), electrocardiogram (ECG), photoplethysmography (PPG), and respiratory (RA), to classify PD and healthy control (HC) based on a multimodal support vector machine (SVM). Our experiments achieved an accuracy of 96.03%. Besides, we performed statistical analysis on the four types of physiological data of the PD group and the HC group. Results showed that the EEG of non-dementia PD patients had a significant decrease in high-frequency power, and the high-frequency energy distribution of the normalized PPG signal increased compared with HC. The current study suggests that combining the physiological information of multiple models and machine learning methods could improve the diagnosis accuracy of PD disease and be a potentially effective method of clinical diagnosis.
KW - Electroencephalography
KW - Machine learning
KW - Multimodal physiological signals
KW - Parkinson’s disease
UR - http://www.scopus.com/inward/record.url?scp=85104743724&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-1288-6_9
DO - 10.1007/978-981-16-1288-6_9
M3 - Conference contribution
AN - SCOPUS:85104743724
SN - 9789811612879
T3 - Communications in Computer and Information Science
SP - 125
EP - 136
BT - Human Brain and Artificial Intelligence - Second International Workshop, HBAI 2020, Held in Conjunction with IJCAI-PRICAI 2020, Revised Selected Papers
A2 - Wang, Yueming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020
Y2 - 7 January 2021 through 7 January 2021
ER -