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

2010 ◽  
Vol 8 (59) ◽  
pp. 842-855 ◽  
Author(s):  
Athanasios Tsanas ◽  
Max A. Little ◽  
Patrick E. McSharry ◽  
Lorraine O. Ramig
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Signal Processing ◽  
Symptom Severity ◽  
Large Scale ◽  
Rating Scale ◽  
Signal Processing Algorithm ◽  
Wide Range ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

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.

scholarly journals Voice Assessments for Detecting Patients with Parkinson’s Diseases in Different Stages

2018 ◽  
Vol 8 (6) ◽  
pp. 4265
Author(s):  
Elmehdi Benmalek ◽  
Jamal Elmhamdi ◽  
Abdelilah Jilbab
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Principal Component ◽  
Support Vector ◽  
Speech Signal Processing ◽  
Parkinson’S Diseases ◽  
Wide Range ◽  
Signal Processing Algorithms ◽  
Linear Svm

<p class="IJASEITParagraph">Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to detect patients with Parkinson’s disease (PD). So we have computed 19 dysphonia measures from sustained vowels collected from 375 voice samples from healthy and people suffer from PD. All the features are analysed and the more relevant ones are selected by the Principal component analysis (PCA) to classify the subjects in 4 classes according to the UPDRS (unified Parkinson’s disease Rating Scale) score. We used k-folds cross validation method with (k=4) validation scheme; 75% for training and 25% for testing, along with the Support Vector Machines (SVM) with its different types of kernels. The best result obtained was 92.5% using the PCA and the linear SVM.</p>

Assessment System for Parkinson's Disease Tremor and Correlation Analysis With Applied Signal Processing Algorithms

2020 ◽  
Vol 3 (4) ◽  
Author(s):  
Na Zhu ◽  
Nathaniel S. Miller
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Signal Processing ◽  
Bandpass Filter ◽  
Assessment System ◽  
Therapeutic Interventions ◽  
Frequency Domains ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Rotational Motions

Abstract Accurate measurement and assessment of Parkinson's disease (PD) tremor is important for patients, clinicians, and researchers to track changes in disease progression and the effectiveness of therapeutic interventions. This study measured resting, postural, and kinetic tremor from patient's most-affected hand with accelerometers and gyrometers; thus, the linear and rotational motions in the x, y, z directions were obtained. Data were collected when patients were both ON and OFF their anti-PD medications. A bandpass filter was applied to extract raw tremor information, and several signal processing algorithms were used to analyze the data in both time and frequency domains, including the correlations between motions in different directions. The results of medication effectiveness on PD tremor and the correlational analyses were discussed.

Assessment of Parkinson’s Disease Tremor and Correlation Analysis With Applied Signal Processing

2019 ◽  
Author(s):  
Na Zhu ◽  
Nathaniel S. Miller
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Signal Processing ◽  
Correlation Analysis ◽  
Bandpass Filter ◽  
Therapeutic Interventions ◽  
Frequency Domains ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Rotational Motions

Abstract Accurate measurement and assessment of Parkinson’s disease (PD) tremor is important for patients, clinicians, and researchers to track changes in disease progression and the effectiveness of therapeutic interventions. This study measured resting, postural, and kinetic tremor from patient’s most-affected hand with accelerometers and gyrometers, thus the linear and rotational motions in the x, y, z directions were obtained. Data were collected when patients were both ON and OFF their anti-PD medications. A bandpass filter was applied to extract raw tremor information and several signal processing algorithms were used to analyze the data in both time and frequency domains, including the correlations between motions at different directions. The results of medication effectiveness on PD tremor and the correlational analyses will be discussed.

scholarly journals Computational medication regimen for Parkinson’s disease using reinforcement learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yejin Kim ◽  
Jessika Suescun ◽  
Mya C. Schiess ◽  
Xiaoqian Jiang
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Reinforcement Learning ◽  
Symptom Severity ◽  
Rating Scale ◽  
Motor Symptom ◽  
Sequential Decision ◽  
Medication Regimen ◽  
Severity Scores ◽  
Decision Making Rule

AbstractOur objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson’s disease patients, the Parkinson’s Progression Markers Initiative database, we derived clinically relevant disease states and an optimal combination of medications for each of them by using policy iteration of the Markov decision process (MDP). We focused on 8 combinations of medications, i.e., Levodopa, a dopamine agonist, and other PD medications, as possible actions and motor symptom severity, based on the Unified Parkinson Disease Rating Scale (UPDRS) section III, as reward/penalty of decision. We analyzed a total of 5077 visits from 431 PD patients with 55.5 months follow-up. We excluded patients without UPDRS III scores or medication records. We derived a medication regimen that is comparable to a clinician’s decision. The RL model achieved a lower level of motor symptom severity scores than what clinicians did, whereas the clinicians’ medication rules were more consistent than the RL model. The RL model followed the clinician’s medication rules in most cases but also suggested some changes, which leads to the difference in lowering symptoms severity. This is the first study to investigate RL to improve the pharmacological approach of PD patients. Our results contribute to the development of an interactive machine-physician ecosystem that relies on evidence-based medicine and can potentially enhance PD management.

SIGNAL PROCESSING ALGORITHMS FOR STARING SINGLE PIXEL HYPERSPECTRAL SENSORS

2008 ◽  
Vol 18 (03) ◽  
pp. 661-673
Author(s):  
DIMITRIS MANOLAKIS ◽  
MICHAEL ROSSACCI ◽  
ERIN O'DONNELL ◽  
FRANCIS M. D'AMICO
Keyword(s):  
Signal Processing ◽  
Thermal Emission ◽  
Photon Flux ◽  
Spectral Signature ◽  
Hyperspectral Sensors ◽  
Pixel Sensors ◽  
Wide Range ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Single Pixel

Remote sensing of chemical warfare agents (CWA) with stand-off hyperspectral sensors has a wide range of civilian and military applications. These sensors exploit the spectral changes in the ambient photon flux produced thermal emission or absorption after passage through a region containing the CWA cloud. In this work we focus on (a) staring single-pixel sensors that sample their field of view at regular intervals of time to produce a time series of spectra and (b) scanning single or multiple pixel sensors that sample their FOV as they scan. The main objective of signal processing algorithms is to determine if and when a CWA enters the FOV of the sensor. We shall first develop and evaluate algorithms for staring sensors following two different approaches. First, we will assume that no threat information is available and we design an adaptive anomaly detection algorithm to detect a statistically-significant change in the observed spectrum. The algorithm processes the observed spectra sequentially-in-time, estimates adaptively the background, and checks whether the next spectrum differs significantly from the background based on the Mahalanobis distance or the distance from the background subspace. In the second approach, we will assume that we know the spectral signature of the CWA and develop sequential-in-time adaptive matched filter detectors. In both cases, we assume that the sensor starts its operation before the release of the CWA; otherwise, staring at a nearby CWA-free area is required for background estimation. Experimental evaluation and comparison of the proposed algorithms is accomplished using data from a long-wave infrared (LWIR) Fourier transform spectrometer.

scholarly journals Confirmatory Factor Analysis of the Motor Unified Parkinson’s Disease Rating Scale

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Stefanie D. Vassar ◽  
Yvette M. Bordelon ◽  
Ron D. Hays ◽  
Natalie Diaz ◽  
Rebecca Rausch ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Symptom Severity ◽  
Rating Scale ◽  
Confirmatory Factor ◽  
Disease Rating ◽  
The Right ◽  
Gait Posture

The motor examination section of the unified Parkinson’s disease rating scale (UPDRS) is widely used in research but few studies have examined whether subscales exist that tap relatively distinct motor abnormalities. We analyzed data from 193 persons enrolled in a population-based study in Central California. Patients were examined after overnight PD medication washout (“OFF” state) and approximately one hour after taking medication (“ON” state). We performed confirmatory factor analysis of the UPDRS for OFF and ON state examinations; correlations, reliability, and relative validity of resulting subscales were evaluated. A model with five factors (gait/posture, tremor, rigidity, bradykinesia affecting the left extremities, bradykinesia affecting the right extremities) fit the data well, with similar results for OFF and ON states. Internal consistency reliability coefficients were 0.90 or higher for all subscales. The gait/posture subscale most strongly discriminated across levels of patient reported PD symptom severity and of how PD affects them on a daily basis. Compared to the right sided bradykinesia subscale, the left sided bradykinesia subscale had higher discrimination across levels of self-reported PD symptom severity and functional impairment. This supports motor UPDRS containing multiple subscales that can be analyzed separately and provide information distinct from the total score that may be useful in clinical studies.

Hand Function as Predictor of Motor Symptom Severity in Individuals with Parkinson’s Disease

Gerontology ◽  
2021 ◽  
pp. 1-8
Author(s):  
Thiago da Silva Rocha Paz ◽  
Vera Lúcia Santos de Britto ◽  
Bruna Yamaguchi ◽  
Vera Lúcia Israel ◽  
Alessandra Swarowsky ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Rating Scale ◽  
Handgrip Strength ◽  
Statistical Significance ◽  
Hand Function ◽  
Freezing Of Gait ◽  
Motor Symptom ◽  
Cross Sectional

<b><i>Introduction:</i></b> Parkinson’s disease (PD) leads to deficits in upper limb strength and manual dexterity and consequently resulting in functional impairment. Handgrip strength is correlated with the motor symptom severity of the disease, but there is a gap in the literature about the influence of freezing in PD patients. <b><i>Objective:</i></b> The objective is to study the correlation between handgrip strength and motor symptom severity considering the freezing phenomenon and to verify variables that can predict Unified Parkinson’s Disease Rating Scale (UPDRS) III. <b><i>Methods:</i></b> This is a multicenter cross-sectional study in PD. 101 patients were divided into 2 groups: freezing of gait (FOG) (<i>n</i> = 51) and nonfreezing (nFOG) (<i>n</i> = 52). Freezing of Gait Questionnaire (FOGQ); UPDRS II and III sections; Hoehn and Yahr (HY) scale; handgrip dynamometry (HD); 9 Hole Peg Test (9-HPT) were assessed. <b><i>Results:</i></b> In both groups, HD was correlated to UPDRS III (nFOG: −0.308; FOG: −0.301), UPDRS total (nFOG: −0.379; FOG: −0.368), UPDRS item 23 (nFOG: −0.404; FOG: −0.605), and UPDRS item 24 (nFOG: −0.405; FOG: −0.515). For the correlation to UPDRS II (0.320) and 9-HPT (−0.323), only nFOG group presented significance. For the UPDRS 25 (−0.437), only FOG group presented statistical significance. The UPDRS III can be predicted by 9-HPT, age, and HY in nFOG patients (Adjusted <i>R</i><sup>2</sup> = 0.416). In FOG group, UPDRS III can be predicted by HD, 9-HPT, age, and HY (Adjusted <i>R</i><sup>2</sup> = 0.491). <b><i>Conclusion:</i></b> Handgrip strength showed to be predictive of motor impairment only in the FOG group. Our results showed clinical profile differences of motor symptoms considering freezers and nonfreezers with PD.

Sign in / Sign up

Export Citation Format

Share Document