Seung-Bo Lee1, Yong-Jeong Kim2, Sungeun Hwang3, Hyoshin Son4, Sang Kun Lee5, Kyung-Il Park6, Young-Gon Kim7. 1. Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea. Electronic address: koreateam23@gmail.com. 2. Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea. Electronic address: somedaytobegood@gmail.com. 3. Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea. Electronic address: neurosung@gmail.com. 4. Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea. Electronic address: hson727@gmail.com. 5. Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea. Electronic address: sangkun2923@gmail.com. 6. Department of Neurology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea; Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea. Electronic address: ideopki@gmail.com. 7. Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea; AI Institute, Seoul National University, Seoul, Republic of Korea. Electronic address: younggon2.kim@gmail.com.
Abstract
INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm. METHOD: We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients. RESULTS: Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%). CONCLUSION: The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.
INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm. METHOD: We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients. RESULTS: Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%). CONCLUSION: The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.