Parkinson's disease monitoring by biomechanical instability of phonation.

Patients suffering from Parkinson's disease (PD) may be successfully treated pharmacologically and surgically to preserve and even improve their life quality and health conditions. Although the progress of the disease cannot be stopped, at least mitigation of the most handicapping symptoms can be achieved. But both pharmacological and surgical treatments require the adequate monitoring of the disease stage of progress and the effects of treatment. Several techniques have been proposed for PD evolution monitoring, ranging from subjective auto-evaluation by questionnaires, or from gait and handwriting examination by specialists. Nevertheless, these techniques present certain difficulties, which make frequent evaluation impractical. On the other hand, it is known that speech acoustic analysis may estimate indicators of patient's conditions, and can be implemented for a frequent evaluation protocol; and under minimal help, it can be carried out at distance using communication technologies. The acoustic analysis, may be based on mel-cepstral coefficients, distortion features as jitter, shimmer, harmonic-to-noise contents, or pitch-perturbation estimates, among others. Phonation biomechanical parameter and tremor estimates are also good markers of PD. The present work proposes a combination of biomechanical features to predict PD progress using Bayesian likelihood estimation. This methodology proves to be very sensitive and allows a three-band based comparison: pre-treatment versus post-treatment in reference to a control subject or a normative population. Results from a study are presented, including eight patients recorded on a 4-week separation interval, meanwhile they were treated with medication, physical exercising and speech therapy. The conclusions show that certain distortion, biomechanical and tremor features are of special relevance to monitor PD phonation, and that they can be used as evolution markers.