Parkinson’s Disease Detection Based on Running Speech Data From Phone Calls
Christos Laganas, Dimitrios Iakovakis, Stelios Hadjidimitriou, Vasileios Charisis, Sofia B. Dias, Sevasti Bostantzopoulou, Zoe Katsarou, Lisa Klingelhoefer, Heinz Reichmann, Dhaval Trivedi, K. Ray Chaudhuri, Leontios J. Hadjileontiadis
Abstract
OBJECTIVE: Parkinson's Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which, often hinder timely and early diagnosis and treatment. The development of accessible, technology-based methods for longitudinal PD symptoms tracking in daily living, offers the potential for transforming disease assessment and accelerating diagnosis. METHODS: A privacy-aware method for classifying patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed. Voice features from running speech signals were extracted from passively-captured recordings over voice calls. Language-aware training of multiple- and single-instance learning classifiers was employed to fuse and predict on voice features and demographic data from a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients). RESULTS: By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively. CONCLUSIONS: The proposed approach outperforms other methods proposed for language-aware PD detection considering the ecological validity of the voice data. SIGNIFICANCE: This paper introduces for the first time a high-frequency, privacy-aware and unobtrusive PD screening tool based on analysis of voice samples captured during routine phone calls.