Suitability of dysphonia measurements for telemonitoring of Parkinson's disease

Abstract

We present an assessment of the practical value of existing traditional and non-standard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, Pitch Period Entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected 10 highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that non-standard methods in combination with traditional harmonics-to-noise ratios are best able to separate healthy from PD subjects. The selected non-standard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well-suited to telemonitoring applications.

Figure 1

Two selected examples of speech signals: (a) healthy, (b) subject with PD. The horizontal axis is time in seconds, the vertical axis is signal amplitude (no units).

Figure 2

Recurrence period density entropy (RPDE) and detrended fluctuation analysis (DFA) results for healthy subjects (left panels) and for subjects with Parkinson's (right panels); (a-b) recurrence period density P(T) for recurrence times T, (c-d) log-log plot of scaling window sizes L against fluctuation amplitudes F(L). See main text for more detailed descriptions.

Figure 3

Details of pitch period entropy (PPE) calculation: (a-b) pitch period p(t) in semitones relative to note C3 on the musical scale, (c-d) residual of pitch period r(t) after spectral whitening filter, (e-f) probability densities P(r) of residual pitch period r. PPE value is the entropy of this probability density). Left panels are for a healthy subject, right panel is for a person with Parkinson's.

Figure 4

Probability densities of some selected features after pre-processing by range normalization, in preparation for SVM classification (see Table II for a list of these features). The vertical axes are the probability densities P(x) of the normalized feature values x, estimated using the kernel density method with Gaussian kernel function. The dashed lines are for healthy subjects, the solid lines for Parkinson's subjects.

Figure 5

Plots of pairs of features after pre-processing by range normalization, showing examples of high correlation (a) and low correlation (b). One of each pair of highly correlated features is removed prior to classification.

Figure 6

SVM classification boundaries for some selected pairs of features after pre-processing by range normalization (see Table II for a list of these features). The `x' marks are for healthy subjects, the round marks for Parkinson's subjects. The light grey shaded areas are the regions in which subjects are predicted to have Parkinson's.