Detection of Parkinson’s disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques

Abstract

Early detection of Parkinson’s disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.

Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease

“…4DFD also performed very well as a classifier (see Figure 5), separating PD and HC groups with very few exceptions. This means that 4DFD could be used as one of the features in advanced deep learning and machine learning methods for detecting PD [37,38].…”

Multidirectional Analysis of Curvelet Against Skin Cancer

“…the energy and entropy properties of sub-band S, which was acquired from the multi-resolution analysis, are defined as [29,30].…”

Temporal Patterns of EEG Connectivity Unveil Parkinson's Disease Progression: Insights from Machine Learning Analysis

“…Here, we considered only the non-medicated PD EEG time series. Demographic details such as age, gender, handedness, cognitive assessments, the mini mental state examination (MMSE), the North American adult reading test (NAART), and the Unified Parkinson's disease rating scale (UPDRS) were included 22,48,49 . PD severity varied based on Hoehn and Yahr scales, with an average disease duration of 4.5-3.5 years.…”