Deep Learning based prediction and monitoring of Parkinson’s Disease using Voice Data

Abstract Accurately detecting Parkinson’s disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. An innovative deep-learning technique is proposed to predict and monitor the Parkinson’s Disease (PD) through voice data and Unified Parkinson’s Disease Rating Scale (UPDRS) score. The rise of an aging population over the world emphasizes the need to detect PD early, remotely and accurately. PD is a progressive neurological disorder that affects the Central Nervous System (CNS), leading to symptoms like tremors, stiffness, slow movements, balance and coordination difficulties, and speech disorders. Early detection and severity assessment of PD using Machine Learning is crucial. Speech recognition offers a new approach for diagnosis and monitoring of PD. This paper highlights Deep Learning models with acoustic features like jitter, shimmer, intensity and pitch for automatic detection and use of UPDRS score for severity assessment. Two datasets, one of them containing speech samples from PD patients and healthy individuals and another containing voice data and UPDRS score, with on disk size of 5.3 MB and 1.01 MB respectively, are used in this proposed work. A comparison between the proposed Residual Neural Network (ResNet) and other ten machine learning and ensemble learning methods based on relatively small data including 64 healthy individuals and 188 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 98 % on average and precision, 0.98 and F1-Score, 0.98.