Abstract
Parkinson's disease (PD) is the second most common neurological disease having no specific medical test for its diagnosis. In this study, we consider PD detection based on multimodal voice data that was collected through two channels, i.e., Smart Phone (SP) and Acoustic Cardioid (AC). Four types of data modalities were collected through each channel, namely sustained phonation (P), speech (S), voiced (V), and unvoiced (U) modality. The contributions of this paper are twofold. First, it explores optimal data modality and features having better information about PD. Second, it proposes a MultiModal Data–Driven Ensemble (MMDD-Ensemble) approach for PD detection. The MMDD-Ensemble has two levels. At the first level, different base classifiers are developed that are driven by multimodal voice data. At the second level, the predictions of the base classifiers are fused using blending and voting methods. In order to validate the robustness of the propose method, six evaluation measures, namely accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC), are adopted. The proposed method outperformed the best results produced by optimal unimodal framework from both the key evaluation aspects, i.e., accuracy and AUC. Furthermore, the proposed method also outperformed other state-of-the-art ensemble models. Experimental results show that the proposed multimodal approach yields 96% accuracy, 100% sensitivity, 88.88% specificity, 0.914 of MCC, and 0.986 of AUC. These results are promising compared to the recently reported results for PD detection based on multimodal voice data.
Highlights
Parkinson’s disease (PD) is a neurodegenerative disease of the central nervous system (CNS) effecting approximately 6.3 million populations worldwide across all genders, races, and cultures
In order to evaluate the effectiveness of the proposed multimodal-based framework and to compare it against the best unimodal frameworks, we utilized receiver operating characteristics (ROC) curves and area under the curve (AUC) along with the above-discussed evaluation measures
The main objective of this experiment is to explore the optimal data modality and optimal feature set that would provide better PD detection accuracy for the data collected through Acoustic Cardioid (AC) channel
Summary
Parkinson’s disease (PD) is a neurodegenerative disease of the central nervous system (CNS) effecting approximately 6.3 million populations worldwide across all genders, races, and cultures. It causes partial or complete loss of speech, motor reflexes, and behavioral and mental processes (Jankovic, 2008; Khorasani and Daliri, 2014; Ali et al, 2019b). PD detection based on voice data enables telediagnosis of the disease (Tsanas et al, 2012; Sakar et al, 2013; Ali et al, 2019a). Automated learning system based on machine learning is required to provide an efficient way of evaluating the disease (Ravì et al, 2017)
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