Recognizing Parkinson’s disease gait patterns by vibes algorithm and Hilbert-Huang transform

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disorder all over the world. There are resting tremor, bradykinesia, and rarely dystonia, all of which are motor symptoms, among the manifestations of PD. But the direct use of these motor symptoms for diagnosis can be misleading since PD can be confused with other Parkinsonisms and further disorders with a similar symptom. Therefore gait can be used, which has significant dynamics in the detection of PD and is an extremely complex motion. In this paper, we employed a state-of-the-art ensemble learning algorithm, called the vibes algorithm, and the Hilbert-Huang Transform (HHT) to recognize PD gait patterns. We extracted the features by the processing of the signals, which come from sixteen sensors on the bottom of both feet, through HHT and sixteen statistical functions. We then performed the two-stage feature selection process by using the vibes algorithm and the OneRAttributeEval algorithm. Finally, we exploited the vibes algorithm and the Classification and Regression Trees as a base learner to differentiate between patients with PD and the control group. The classification accuracy, sensitivity and specificity rates of the proposed method are 98.79%, 98.92%, and 98.61%, respectively. Moreover, we thoroughly contrasted our method with the previous sixteen works. The experiment results demonstrated that our method is high-performance and maintains stability. We also found out two unrevealed markers that could provide support in clinical diagnosis for PD apart from the classification task.

Keywords

• Machine Learning
• Parkinson’s Disease
• Ensemble Learning
• Hilbert-Huang Transform
• Feature Engineering