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.

Oculo-visual dysfunction in Parkinson's disease

This review describes the oculo-visual problems likely to be encountered in Parkinson's disease (PD) with special reference to three questions: (1) are there visual symptoms characteristic of the prodromal phase of PD, (2) is PD dementia associated with specific visual changes, and (3) can visual symptoms help in the differential diagnosis of the parkinsonian syndromes, viz. PD, progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB), multiple system atrophy (MSA), and corticobasal degeneration (CBD)? Oculo-visual dysfunction in PD can involve visual acuity, dynamic contrast sensitivity, colour discrimination, pupil reactivity, eye movement, motion perception, and visual processing speeds. In addition, disturbance of visuo-spatial orientation, facial recognition problems, and chronic visual hallucinations may be present. Prodromal features of PD may include autonomic system dysfunction potentially affecting pupil reactivity, abnormal colour vision, abnormal stereopsis associated with postural instability, defects in smooth pursuit eye movements, and deficits in visuo-motor adaptation, especially when accompanied by idiopathic rapid eye movement (REM) sleep behaviour disorder. PD dementia is associated with the exacerbation of many oculo-visual problems but those involving eye movements, visuo-spatial function, and visual hallucinations are most characteristic. Useful diagnostic features in differentiating the parkinsonian symptoms are the presence of visual hallucinations, visuo-spatial problems, and variation in saccadic eye movement dysfunction.

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

Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

A quantitative study of α-synuclein pathology in fifteen cases of dementia associated with Parkinson disease

The α-synuclein-immunoreactive pathology of dementia associated with Parkinson disease (DPD) comprises Lewy bodies (LB), Lewy neurites (LN), and Lewy grains (LG). The densities of LB, LN, LG together with vacuoles, neurons, abnormally enlarged neurons (EN), and glial cell nuclei were measured in fifteen cases of DPD. Densities of LN and LG were up to 19 and 70 times those of LB, respectively, depending on region. Densities were significantly greater in amygdala, entorhinal cortex (EC), and sectors CA2/CA3 of the hippocampus, whereas middle frontal gyrus, sector CA1, and dentate gyrus were least affected. Low densities of vacuoles and EN were recorded in most regions. There were differences in the numerical density of neurons between regions, but no statistical difference between patients and controls. In the cortex, the density of LB and vacuoles was similar in upper and lower laminae, while the densities of LN and LG were greater in upper cortex. The densities of LB, LN, and LG were positively correlated. Principal components analysis suggested that DPD cases were heterogeneous with pathology primarily affecting either hippocampus or cortex. The data suggest in DPD: (1) ratio of LN and LG to LB varies between regions, (2) low densities of vacuoles and EN are present in most brain regions, (3) degeneration occurs across cortical laminae, upper laminae being particularly affected, (4) LB, LN and LG may represent degeneration of the same neurons, and (5) disease heterogeneity may result from variation in anatomical pathway affected by cell-to-cell transfer of α-synuclein. © 2013 Springer-Verlag Wien.

Risk and benefit assessment for Tolcapone in the treatment of Parkinson's disease

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

A review of studies describing the use of acetyl cholinesterase inhibitors in Parkinson's disease dementia

Objective:  To review the literature relating to the use of acetyl cholinesterase inhibitors in Parkinson's disease dementia (PDD). Method:  MEDLINE (1966 – December 2004), PsychINFO (1972 – December 2004), EMBASE (1980 – December 2004), CINHAL (1982 – December 2004), and the Cochrane Collaboration were searched in December 2004. Results:  Three controlled trials and seven open studies were identified. Efficacy was assessed in three key domains: cognitive, neuropsychiatric and parkinsonian symptoms. Conclusion:  Cholinesterase inhibitors have a moderate effect against cognitive symptoms. There is no clear evidence of a noticeable clinical effect against neuropsychiatric symptoms. Tolerability including exacerbation of motor symptoms – in particular tremor – may limit the utility of cholinesterase inhibitors.

Cholinesterase inhibitors for Parkinson's disease dementia

Background - The loss of cholinergic, dopaminergic and noradrenergic innervations seen in Parkinson's Disease Dementia (PDD) suggest a potential role for cholinesterase inhibitors. Concerns have been expressed about a theoretical worsening of Parkinson's disease related symptoms, particularly movement symptoms. Objectives - To assess the efficacy, safety, tolerability and health economic data relating to the use of cholinesterase inhibitors in PDD. Search methods - The trials were identified from the Specialized Register of the Cochrane Dementia and Cognitive Improvement Group on 19 April 2005 using the search term parkinson*. This register contains records from major health care databases and many ongoing trial databases and is updated regularly. Comprehensive searches of abstracts from major scientific meetings were performed. Pharmaceutical companies were approached for information regarding additional and ongoing studies. Selection criteria - Randomized, double-blind, placebo-controlled studies assessing the effectiveness of cholinesterase inhibitors in PDD. Inclusion and exclusion criteria were stated to limit bias. Data collection and analysis - Two reviewers (IM, CF) independently reviewed the quality of the studies utilizing criteria from the Cochrane Collaboration Handbook. Medications were examined separately and as a group. The outcome measures assessed were in the following domains: neuropsychiatric features, cognition, global impression, daily living activities, quality of life, burden on caregiver, Parkinsonian related symptoms, treatment acceptability as determined by withdrawal from trials, safety as determined by the frequency of adverse events, institutionalisation, death and health economic factors. Main results - A detailed and systematic search of relevant databases identified one published randomized, double-blind, placebo-controlled study (Emre 2004) involving 541 patients that compared rivastigmine with placebo. Rivastigmine produced statistically significant improvements in several outcome measures. On the primary cognitive measure, the ADAS-Cog, rivastigmine was associated with a 2.80 point ADAS-Cog improvement [WMD -2.80, 95% Cl -4.26 to -1.34, P = 0.0002] and a 2.50 point ADCS-ADL improvement [95% Cl 0.43 to 4.57, P = 0.02] relative to placebo. Clinically meaningful (moderate or marked) improvement occurred in 5.3% more patients on rivastigmine, and meaningful worsening occurred in 10.1% more patients on placebo. Tolerability appeared to be a significant issue. Significantly more patients on rivastigmine dropped out of the study due to adverse events [62/362 versus 14/179, OR 2.44, 95% Cl 1.32 to 4.48, P = 0.004]. Nausea [20/179 versus 105/362, OR 3.25, 95% Cl 1.94 to 5.45, P < 0.00001], tremor [7/179 versus 37/362, OR 2.80, 95% Cl 1.22 to 6.41, P = 0.01] and in particular vomiting [3/179 versus 60/362, OR 11.66, 95% Cl 3.60 to 37.72, P < 0.0001] were significantly more common with rivastigmine. However, significantly fewer patients died on rivastigmine than placebo [4/362 versus 7/179, OR 0.27, 95% CI 0.08 to 0.95, P = 0.04] Authors' conclusions - Rivastigmine appears to improve cognition and activities of daily living in patients with PDD. This results in clinically meaningful benefit in about 15% of cases. There is a need for more studies utilising pragmatic measures such as time to residential care facility and both patient and carer quality of life assessments. Future trials should involve other cholinesterase inhibitors, utilise tools to analyse the data that limit any bias and measure health economic factors. It is unlikely that relying solely on the last observation carried forward (LOCF) is sufficient. Publication of the observed case data in the largest trial would assist (Emre 2004). Adverse events were associated with the cholinergic activity of rivastigmine, but may limit patient acceptability as evidenced by the high drop out rate in the active arm.

GABA-mediated changes in inter-hemispheric beta frequency activity in early-stage Parkinson's disease

In Parkinson's disease (PD), elevated beta (15-35Hz) power in subcortical motor networks is widely believed to promote aspects of PD symptomatology, moreover, a reduction in beta power and coherence accompanies symptomatic improvement following effective treatment with l-DOPA. Previous studies have reported symptomatic improvements that correlate with changes in cortical network activity following GABAA receptor modulation. In this study we have used whole-head magnetoencephalography to characterize neuronal network activity, at rest and during visually cued finger abductions, in unilaterally symptomatic PD and age-matched control participants. Recordings were then repeated following administration of sub-sedative doses of the hypnotic drug zolpidem (0.05mg/kg), which binds to the benzodiazepine site of the GABAA receptor. A beamforming based 'virtual electrode' approach was used to reconstruct oscillatory power in the primary motor cortex (M1), contralateral and ipsilateral to symptom presentation in PD patients or dominant hand in control participants. In PD patients, contralateral M1 showed significantly greater beta power than ipsilateral M1. Following zolpidem administration contralateral beta power was significantly reduced while ipsilateral beta power was significantly increased resulting in a hemispheric power ratio that approached parity. Furthermore, there was highly significant correlation between hemispheric beta power ratio and Unified Parkinson's Disease Rating Scale (UPDRS). The changes in contralateral and ipsilateral beta power were reflected in pre-movement beta desynchronization and the late post-movement beta rebound. However, the absolute level of movement-related beta desynchronization was not altered. These results show that low-dose zolpidem not only reduces contralateral beta but also increases ipsilateral beta, while rebalancing the dynamic range of M1 network oscillations between the two hemispheres. These changes appear to underlie the symptomatic improvements afforded by low-dose zolpidem.

High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones

The aim of this study is to accurately distinguish Parkinson's disease (PD) participants from healthy controls using self-administered tests of gait and postural sway. Using consumer-grade smartphones with in-built accelerometers, we objectively measure and quantify key movement severity symptoms of Parkinson's disease. Specifically, we record tri-axial accelerations, and extract a range of different features based on the time and frequency-domain properties of the acceleration time series. The features quantify key characteristics of the acceleration time series, and enhance the underlying differences in the gait and postural sway accelerations between PD participants and controls. Using a random forest classifier, we demonstrate an average sensitivity of 98.5% and average specificity of 97.5% in discriminating PD participants from controls. © 2014 IEEE.

Detecting and monitoring the symptoms of Parkinson's disease using smartphones:a pilot study

Background: Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. Methods: Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. Results: Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). Conclusion: Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.

Precompetitive data sharing as a catalyst to address unmet needs in Parkinson's disease

Parkinson's disease is a complex heterogeneous disorder with urgent need for disease-modifying therapies. Progress in successful therapeutic approaches for PD will require an unprecedented level of collaboration. At a workshop hosted by Parkinson's UK and co-organized by Critical Path Institute's (C-Path) Coalition Against Major Diseases (CAMD) Consortiums, investigators from industry, academia, government and regulatory agencies agreed on the need for sharing of data to enable future success. Government agencies included EMA, FDA, NINDS/NIH and IMI (Innovative Medicines Initiative). Emerging discoveries in new biomarkers and genetic endophenotypes are contributing to our understanding of the underlying pathophysiology of PD. In parallel there is growing recognition that early intervention will be key for successful treatments aimed at disease modification. At present, there is a lack of a comprehensive understanding of disease progression and the many factors that contribute to disease progression heterogeneity. Novel therapeutic targets and trial designs that incorporate existing and new biomarkers to evaluate drug effects independently and in combination are required. The integration of robust clinical data sets is viewed as a powerful approach to hasten medical discovery and therapies, as is being realized across diverse disease conditions employing big data analytics for healthcare. The application of lessons learned from parallel efforts is critical to identify barriers and enable a viable path forward. A roadmap is presented for a regulatory, academic, industry and advocacy driven integrated initiative that aims to facilitate and streamline new drug trials and registrations in Parkinson's disease.

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

Technology in Parkinson's disease:challenges and opportunities

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society.