scholarly journals A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2630 ◽  
Author(s):  
Erika Rovini ◽  
Carlo Maremmani ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Objective Assessment ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Motor Tasks ◽  
Wearable System ◽  
Support Tool ◽  
Average Accuracy ◽  
Open Issue

Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring.

scholarly journals Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Atiqur Rahman ◽  
Sanam Shahla Rizvi ◽  
Aurangzeb Khan ◽  
Aaqif Afzaal Abbasi ◽  
Shafqat Ullah Khan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dimensionality Reduction ◽  
Intelligent System ◽  
Neurological Diseases ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Discriminant

Parkinson’s disease (PD) is one of the most common and serious neurological diseases. Impairments in voice have been reported to be the early biomarkers of the disease. Hence, development of PD diagnostic tool will help early diagnosis of the disease. Additionally, intelligent system developed for binary classification of PD and healthy controls can also be exploited in future as an instrument for prodromal diagnosis. Notably, patients with rapid eye movement (REM) sleep behaviour disorder (RBD) represent a good model as they develop PD with a high probability. It has been shown that slight speech and voice impairment may be a sensitive marker of preclinical PD. In this study, we propose PD detection by extracting cepstral features from the voice signals collected from people with PD and healthy subjects. To classify the extracted features, we propose to use dimensionality reduction through linear discriminant analysis and classification through support vector machine. In order to validate the effectiveness of the proposed method, we also developed ten different machine learning models. It was observed that the proposed method yield area under the curve (AUC) of 88%, sensitivity of 73.33%, and specificity of 84%. Moreover, the proposed intelligent system was simulated using publicly available multiple types of voice database. Additionally, the data were collected from patients under on-state. The obtained results on the public database are promising compared to the previously published work.

scholarly journals Assessing Parkinson’s Disease at Scale Using Telephone-Recorded Speech: Insights from the Parkinson’s Voice Initiative

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1892
Author(s):  
Siddharth Arora ◽  
Athanasios Tsanas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Screening Tool ◽  
Population Based ◽  
Remote Detection ◽  
Support Vector ◽  
Support Tool ◽  
Specialized Equipment ◽  
Fold Cross Validation ◽  
Diagnostic Support

Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings. Using the standard telephone network, we processed 11,942 sustained vowel /a/ phonations from a US-English cohort comprising 1078 PD and 5453 control participants. We characterized each phonation using 304 dysphonia measures to quantify a range of vocal impairments. Given that this is a highly unbalanced problem, we used the following strategy: we selected a balanced subset (n = 3000 samples) for training and testing using 10-fold cross-validation (CV), and the remaining (unbalanced held-out dataset, n = 8942) samples for further model validation. Using robust feature selection methods we selected 27 dysphonia measures to present into a radial-basis-function support vector machine and demonstrated differentiation of PD participants from controls with 67.43% sensitivity and 67.25% specificity. These findings could help pave the way forward toward the development of an inexpensive, remote, and reliable diagnostic support tool for PD using voice as a digital biomarker.

PRESENTING A NEW DECISION SUPPORT SYSTEM FOR SCREENING PARKINSON’S DISEASE PATIENTS USING SYMLET WAVELET

2019 ◽  
Vol 31 (04) ◽  
pp. 1950026
Author(s):  
Yashar Sarbaz ◽  
Behzad Abedi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Support ◽  
Healthy Subjects ◽  
Disease Diagnosis ◽  
Gait Disorders ◽  
Support Vector ◽  
Double Support ◽  
Number Of Patients ◽  
Classifier Performance

Objective: Parkinson’s Disease (PD) is a neurodegenerative disease that is categorized by tremor, rigidity, and bradykinesia. Currently, there is no standard method to diagnose patients with PD. One of the common symptoms of PD is gait disorders which are caused by rigid muscles. Gait disorders may start some years before disease diagnosis. Therefore, better understanding of the gait signal can lead to early diagnosis of PD. Methods: Computer-aided system has been useful in early detection of PD symptoms. In the present study, gait disturbances have received attention as potential biomarkers for early diagnosing of PD. Time and frequency analysis of gait signals together can provide more useful information. Wavelet-based features were extracted from stride, swing and double support time signals of healthy subjects and PD patients. These signals were decomposed into five levels using “sym4” wavelet. Mean and standard deviation (SD) of the absolute values of the approximation and detailed coefficients at each level were computed. Then final features were picked accordingly to obtain the best result for the classification. Results: Support Vector Machine (SVM) was employed for classification of patients and healthy people. The classifier performance was measured based on accuracy, sensitivity and specificity. The classifier performance is obtained with 93.3% accuracy employing linear kernel. Conclusions: The proposed system can be employed as a Decision Support Systems (DSSs) for early diagnosing of PD. Presenting DSSs can be employed to screen suspected cases of PD disease for further evaluation. Studying large number of patients and healthy subjects may lead to more precise study on PD. Also, it seems that using other different classifiers, along with our features, can reduce the diagnosis error.

scholarly journals Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 247 ◽  
Author(s):  
Donato Impedovo ◽  
Giuseppe Pirlo ◽  
Gennaro Vessio
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Intelligent Systems ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Screening Tests ◽  
Diagnostic Tools ◽  
Handwriting Analysis ◽  
Dynamic Features ◽  
Support Tool

Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit impairments of previously learned motor skills, such as handwriting. Therefore, handwriting can be considered a powerful marker to develop automatized diagnostic tools. In this paper, we investigated if and to which extent dynamic features of the handwriting process can support PD diagnosis at earlier stages. To this end, a subset of the publicly available PaHaW dataset has been used, including those patients showing only early to mild degree of disease severity. We developed a classification framework based on different classifiers and an ensemble scheme. Some encouraging results have been obtained; in particular, good specificity performances have been observed. This indicates that a handwriting-based decision support tool could be used to administer screening tests useful for ruling in disease.

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

Pursuing Multiple Biomarkers for Early Idiopathic Parkinson’s Disease Diagnosis

2021 ◽  
Author(s):  
Yareth Gopar-Cuevas ◽  
Ana P. Duarte-Jurado ◽  
Rosa N. Diaz-Perez ◽  
Odila Saucedo-Cardenas ◽  
Maria J. Loera-Arias ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Diagnosis ◽  
Idiopathic Parkinson’S Disease ◽  
Idiopathic Parkinson's Disease ◽  
Multiple Biomarkers
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