Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD

Intensive treatment of dysarthria secondary to stroke

This study investigated the impact of a well-defined behavioral dysarthria treatment on acoustic and perceptual measures of speech in four adults with dysarthria secondary to stroke. A single-subject ABA experimental design was used to measure the effects of the Lee Silverman Voice Treatment (LSVT ® LOUD) on the speech of individual participants. Dependent measures included vocal sound pressure level, phonatory stability, vowel space area, and listener ratings of speech, voice and intelligibility. Statistically significant improvements (p \u3c 0.05) in vocal dB SPL and phonatory stability as well as larger vowel space area were present for all participants. Listener ratings suggested improved voice quality and more natural speech post-treatment. Speech intelligibility scores improved for one of four participants. These data suggest that people with dysarthria secondary to stroke can respond positively to intensive speech treatments such as LSVT. Further studies are needed to investigate speech treatments specific to stroke. © 2012 Informa UK, Ltd

Intensive Voice Treatment (LSVT® LOUD) for dysarthria secondary to stroke

Stroke is an increasing cause of disability in the United States. The frequent occurrence of communication disorders following stroke make the selection of appropriate treatment strategies of critical importance. This was a Phase I study to detect whether there was a positive treatment effect of intensive voice training (LSVT® LOUD) on two individuals with dysarthria secondary to chronic stroke. Data were collected using an A-B-A-A single subject design with three pre-, two post-, and two follow-up evaluations at 4 months following treatment. Vocal sound pressure level (SPL) changes for sustained phonation, monologue, reading, and picture description indicated increased vocal SPL following intensive treatment that was maintained at follow-up. Five listeners completed auditory-perceptual analyses of pre- and posttreatment speech samples for understandability (articulation clarity) and functional communication preference. Listeners preferred posttreatment speech samples of one participant but rated the post-treatment speech samples for the second participant as similar or worse. The second participant had greater language deficits than the first, which may have influenced listeners\u27 ratings of speech characteristics. Both participants and family members reported positive outcomes of treatment on functional communication rating scales and in post-treatment interviews. The application of intensive voice treatment to improve functional communication in individuals with dysarthria secondary to stroke is discussed. Copyright © 2009 Delmar Cengage Learning

Evidence-based treatment of voice and speech disorders in Parkinson disease

Purpose of review Voice and speech impairments are present in nearly 90% of people with Parkinson disease and negatively impact communication and quality of life. This review addresses the efficacy of Lee Silverman Voice Treatment (LSVT) LOUD to improve vocal loudness (as measured by vocal sound pressure level vocSPL) and functional communication in people with Parkinson disease. The underlying physiologic mechanisms of Parkinson disease associated with voice and speech changes and the strength of the current treatment evidence are discussed with recommendations for best clinical practice. Recent findings Two randomized control trials demonstrated that participants who received LSVT LOUD were significantly better on the primary outcome variable of improved vocSPL posttreatment than alternative and no treatment groups. Treatment effects were maintained for up to 2 years. In addition, improvements have been demonstrated in associated outcome variables, including speech rate, monotone, voice quality, speech intelligibility, vocal fold adduction, swallowing, facial expression and neural activation. Advances in technology-supported treatment delivery are enhancing treatment accessibility. Summary Data support the efficacy of LSVT LOUD to increase vocal loudness and functional communication in people with Parkinson disease. Timely intervention is essential for maximizing quality of life for people with Parkinson disease

Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests

Tracking Parkinson's disease (PD) symptom progression often uses the Unified Parkinson’s Disease Rating 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 non-parametric 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

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

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-administration speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments

Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease

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.
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Suitability of dysphonia measurements for telemonitoring of Parkinson's disease

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 meaures, 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