Novel automated PD detection system using aspirin pattern with EEG signals

Background and objective: Parkinson's disease (PD) is one of the most common diseases worldwide which reduces quality of life of patients and their family members. The electroencephalogram (EEG) signals coupled with various advanced machine-learning algorithms have been widely used to detect PD automatically. In this paper, we propose a novel aspirin pattern to detect PD accurately using EEG signals. Method: In this research, the feature generation ability of a chemical graph is investigated. Therefore, this work presents a new graph-based aspirin model for automated PD detection using EEG signals. The proposed method consists of (i) multilevel feature generation phase involving new aspirin pattern, statistical moments, and maximum absolute pooling (MAP), (ii) selection of most discriminative features using neighborhood component analysis (NCA), and (iii) classification using k nearest neighbor (kNN) for automated detection of PD and (iv) iterative majority voting. Results: A public dataset has been used to develop the proposed model. Two cases are created, and these cases consisted of two classes. Leave one subject out (LOSO) validation have been used to calculate robust results. Our proposal achieved 93.57% and 95.48% classification accuracies for Case 1 and Case 2 respectively. Conclusion: Our developed automated PD model is accurate and equipped to be tested with more diverse EEG datasets.