Parkinson's Disease Detection Based on Running Speech Data From Phone Calls.

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 the disease assessment and accelerating PD diagnosis. METHODS A privacy-aware method for classifying PD patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed here. Voice features from running speech signals were extracted from recordings passively captured over voice phone calls. Features are fed in a language-aware training of multiple- and single-instance learning classifiers, along with demographic variables, exploiting a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients) to classify PD. 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. Comparative analysis with other approaches for language-aware PD detection justified the efficiency of the proposed one, considering the ecological validity of the acquired voice data. CONCLUSIONS The present work demonstrates increased robustness in PD detection using voice data captured in-the-wild. SIGNIFICANCE A high-frequency, privacy-aware and unobtrusive PD screening tool is introduced for the first time, based on analysis of voice samples captured during routine phone calls.