Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests

Abstract

Abstract-Tracking Parkinson's disease (PD) symptom progression often uses the Unified Parkinson's DiseaseRating Scale (UPDRS), which requires the patient's presence in clinic, and time-consuming physical examinations by trained medical staff. Thus, symptom monitoring is costly and logistically inconvenient for patient and clinical staff alike, also hindering recruitment for future large-scale clinical trials. Here, for the first time, we demonstrate rapid, remote replication of UPDRS assessment with clinically useful accuracy (about 7.5 UPDRS points difference from the clinicians' estimates), using only simple, self-administered, and non-invasive speech tests. We characterize speech with signal processing algorithms, extracting clinically useful features of average PD progression. Subsequently, we select the most parsimonious model with a robust feature selection algorithm, and statistically map the selected subset of features to UPDRS using linear and nonlinear regression techniques, which include classical least squares and nonparametric classification and regression trees (CART). We verify our findings on the largest database of PD speech in existence (~6,000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial). These findings support the feasibility of frequent, remote and accurate UPDRS tracking. This technology could play a key part in telemonitoring frameworks that enable large-scale clinical trials into novel PD treatments.

Markerless Analysis of Articulatory Movements in Patients With Parkinson's Disease

“…21,22 Instead, few results were obtained for the automation of exercises that involve facial muscles like those commonly performed in speech therapy protocols. The main reasons are related to the high costs of the methods used to study articulatory movements.…”

Relative fundamental frequency during vocal onset and offset in older speakers with and without Parkinson's disease

“…However, the specific pattern of the development of speech symptoms with disease progression is still unknown. Recent work in the field has indicated that acoustic measures of speech production can be highly predictive of patient function (e.g., Little et al, 2009;Tsanas et al, 2010). Our long-term goal is to identify a set of acoustic parameters that can be used as a biomarker for PD progression.…”

Analysis of Speech from People with Parkinson’s Disease through Nonlinear Dynamics

“…According to their results, it is possible to achieve classification rates of up to 91.4%. Additionally, in [6] the evolution of the PD through the time is studied using a set of features composed by different dysphonia measures (including some from NLD) and analyze their correlation with the evolution of the patients according to the Unified Parkinson Disease Rating Scale (UPDRS) [7] in a period of six months. The authors stated that UPDRS scale can be mapped with a precision of up to 6 points.…”