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

Christos Laganas,Zoe Katsarou,Lisa Klingelhoefer,Vasileios Charisis,Heinz Reichmann,Dhaval Trivedi,K Ray Chaudhuri,Dimitrios Iakovakis,Sevasti Bostantzopoulou,Stelios Hadjidimitriou,Sofia B Dias,Leontios J Hadjileontiadis

doi:10.1109/tbme.2021.3116935

Abstract

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. 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). 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. The proposed approach outperforms other methods proposed for language-aware PD detection considering the ecological validity of the voice data. 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.

Highlights

P ARKINSON’S Disease (PD) is a progressive neurodegenerative disorder of high prevalence rate, with 1% of people above the age of 60 being affected [1]
In the context of the current work, sensitivity measures the proportion of actual PD patients that are correctly identified as having the condition, whereas specificity measures the proportion of actual healthy controls who are correctly identified as not having the condition
It is evident that passive capturing sustains adherence, while the combination of the classification results of the voicebased analysis could be useful for a passive-based screening tool for early PD patients

Summary

Introduction

P ARKINSON’S Disease (PD) is a progressive neurodegenerative disorder of high prevalence rate, with 1% of people above the age of 60 being affected [1]. Diagnosis of PD is often delayed, since symptoms are subtle at the early stages [6], [7] and their assessment usually requires an in-clinic evaluation of the subject’s condition by a movement disorders expert. The latter usually takes place based on standardized scales and questionnaires, such as the Unified Parkinson’s Disease Rating Scale (UPDRS) [8]. The standard medical practice regarding PD diagnosis is of subjective nature; its effectiveness depends on years

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