Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

Abstract

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p<0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.

Figure 1.

Overview of the clinical metric that quantifies average Parkinson's disease symptom severity, the Unified Parkinson's Disease Rating Scale (UPDRS). Speech appears explicitly twice. (Online version in colour.)

Figure 2.

Schematic of the steps from the data acquisition up to UPDRS estimation. The device that collects the data from the Parkinson's disease (PD) patient is known as the at-home-testing-device (AHTD). The black encircled box (steps 6–8) is the focus of this study. (Online version in colour)

Figure 3.

(a) Typical sustained vowel phonation signal. (b) The same signal magnified in the time axis. The horizontal axes are time in seconds and the vertical axes amplitude (no units). Clear overall amplitude decay over the duration of the phonation can be seen in panel (a). A careful look at the magnified signal (b) reveals that it is not exactly periodic, a characteristic that many dysphonia measures aim to address. (Online version in colour.)

Figure 4.

Scatter plots of the most relevant dysphonia measures against motor UPDRS ((a) males and (b) females) and total UPDRS ((c) males and (d) females), using the measures presented in tables 3 and 4. The horizontal axes are the normalized dysphonia measures and the vertical axes correspond to UPDRS. The grey lines are the best linear fit obtained using iteratively reweighted least squares—see [35] for details.

Figure 5.

Motor-UPDRS ((a) male and (c) female subject) and total-UPDRS ((b) male and (d) female subject) tracking over the six-month trial period for a male and a female subject with irregular UPDRS pattern. The ‘baseline’, ‘3-month’ and ‘6-month’ UPDRS scores are shown. The out-of-sample MAE and the standard deviation of MAE computed for the subjects presented in this figure are also quoted. The computation of the out-of-sample MAE and the confidence intervals reported in this figure were estimated from the average MAE of the six weekly error estimates throughout the six month duration of the trial for the specific individual. (Online version in colour.)