Parkinson Disease Detection from Speech Articulation Neuromechanics

Abstract

Aim: The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease.Hypothesis: The work hypothesis is that the probability density function of the absolute joint velocity includes information on the stability of phonation when applied to sustained vowels, as well as on fluency if applied to connected speech.Methods: A dataset of sustained vowels recorded from Parkinson Disease patients is contrasted with similar recordings from normative subjects. The probability distribution of the absolute kinematic velocity of the jaw-tongue system is extracted from each utterance. A Random Least Squares Feed-Forward Network (RLSFN) has been used as a binary classifier working on the pathological and normative datasets in a leave-one-out strategy. Monte Carlo simulations have been conducted to estimate the influence of the stochastic nature of the classifier. Two datasets for each gender were tested (males and females) including 26 normative and 53 pathological subjects in the male set, and 25 normative and 38 pathological in the female set.Results: Male and female data subsets were tested in single runs, yielding equal error rates under 0.6% (Accuracy over 99.4%). Due to the stochastic nature of each experiment, Monte Carlo runs were conducted to test the reliability of the methodology. The average detection results after 200 Montecarlo runs of a 200 hyperplane hidden layer RLSFN are given in terms of Sensitivity (males: 0.9946, females: 0.9942), Specificity (males: 0.9944, females: 0.9941) and Accuracy (males: 0.9945, females: 0.9942). The area under the ROC curve is 0.9947 (males) and 0.9945 (females). The equal error rate is 0.0054 (males) and 0.0057 (females).Conclusions: The proposed methodology avails that the use of highly normalized descriptors as the probability distribution of kinematic variables of vowel articulation stability, which has some interesting properties in terms of information theory, boosts the potential of simple yet powerful classifiers in producing quite acceptable detection results in Parkinson Disease.

Non-invasive stimulation of the auditory feedback area for improved articulation in Parkinson's disease

“…Hypokinetic dysarthria (HD) in PD is a multidimensional speech disorder characterized by monopitch and monoloudness, reduced stress, imprecise consonants, airflow insufficiency, microperturbations in frequency/amplitude, impaired speech rate and rhythm etc. [1][2][3]. The pathophysiology of HD is not fully understood, and dopaminergic and surgical treatments have only limited effects on motor-speech dysfunction.…”

Vowel Articulation Dynamic Stability Related to Parkinson’s Disease Rating Features: Male Dataset

“…The interested reader may check several recent publications on this respect, involving the use of pattern recognition methods as probabilistic neural networks, support vector machines, or classification trees, using different sets of biomarkers to assess disease detection and progression. 10,11,34,36,47 A final consideration has to see with the differential neuromotor control of tongue and associated articulation organs with respect to other muscles in the limbs. It is accepted that distal extremities are affected first along with disease progression, before the decline of neuromotor control affects the whole body.…”

“…33 It has been shown that its histogram-derived probability density function p(v r ) contains information related to phonated intervals and pauses, syllable nuclei, vowel onsets and trails, and other dynamic features present in speech articulation. 34 In Fig. 2, the AKV pdf's from a PD patient contrasted against the same distribution from a healthy control are shown.…”

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

“…Up to 90% [7] of patients with PD develop a multi-dimensional speech disorder named hypokinetic dysarthria (HD) [8], which is manifested in phonation, articulation, and prosody [9][10][11]. In the area of phonation, insufficient breath support, reduction in phonation time, increased acoustic noise, instability of articulatory organs, microperturbations of frequency/amplitude, and harsh breathy voice quality has been observed [9,12]. HD leads to serious complications in daily communication of patients with PD [13].…”

“…In the field of acoustic analysis of PD phonation, the authors mostly focused on the sustained vowel /a/ [9]. Conventional phonatory features such as jitter, shimmer, harmonic-to-noise ratio, degree of unvoiced segments, and formant-based parameters extracted from this vowel have been widely used to diagnose PD [12,[19][20][21][22][23]. Although Hazan et al [24] employed analysis of sustained phonation for diagnosis of PD even in its early stage, based on the recent review [9], most of the researchers find relevant applications of the phonatory analysis especially in moderate or severe stages of this disorder.…”