scholarly journals A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mahnaz Behroozi ◽  
Ashkan Sami
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Classification Accuracy ◽  
Pattern Analysis ◽  
Majority Vote ◽  
Central Tendency ◽  
Classification Result ◽  
Sound Recordings ◽  
Multiple Classifier ◽  
New Framework

Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson’s disease (PD) have attracted many researchers. For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson’s disease (PWP). Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD. However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms. Thus, summarizing the vocal tests may lead into loss of valuable information. In order to address this issue, the classification setting must takewhathas been said into account. As a solution, we introduced a new framework that applies an independent classifier for each vocal test. The final classification result would be a majority vote from all of the classifiers. When our methodology comes with filter-based feature selection, it enhances classification accuracy up to15%.

scholarly journals Classifying Parkinson’s Disease Patients With Syntactic and Socio-emotional Verbal Measures

2020 ◽  
Vol 12 ◽  
Author(s):  
Sandra Baez ◽  
Eduar Herrera ◽  
Catalina Trujillo ◽  
Juan F. Cardona ◽  
Jesus A. Diazgranados ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Classification Accuracy ◽  
Functional Role ◽  
Affective State ◽  
Joint Analysis ◽  
Long Distance ◽  
Social Emotions ◽  
Role Assignment ◽  
Joint Assessment

Frontostriatal disorders, such as Parkinson’s disease (PD), are characterized by progressive disruption of cortico-subcortical dopaminergic loops involved in diverse higher-order domains, including language. Indeed, syntactic and emotional language tasks have emerged as potential biomarkers of frontostriatal disturbances. However, relevant studies and models have typically considered these linguistic dimensions in isolation, overlooking the potential advantages of targeting multidimensional markers. Here, we examined whether patient classification can be improved through the joint assessment of both dimensions using sentential stimuli. We evaluated 31 early PD patients and 24 healthy controls via two syntactic measures (functional-role assignment, parsing of long-distance dependencies) and a verbal task tapping social emotions (envy, Schadenfreude) and compared their classification accuracy when analyzed in isolation and in combination. Complementarily, we replicated our approach to discriminate between patients on and off medication. Results showed that specific measures of each dimension were selectively impaired in PD. In particular, joint analysis of outcomes in functional-role assignment and Schadenfreude improved the classification accuracy of patients and controls, irrespective of their overall cognitive and affective state. These results suggest that multidimensional linguistic assessments may better capture the complexity and multi-functional impact of frontostriatal disruptions, highlighting their potential contributions in the ongoing quest for sensitive markers of PD.

scholarly journals Semantic Memory and Lexical Availability in Parkinson’s Disease: A Statistical Learning Study

2021 ◽  
Vol 13 ◽  
Author(s):  
Juan F. Cardona ◽  
Johan S. Grisales-Cardenas ◽  
Catalina Trujillo-Llano ◽  
Jesús A. Diazgranados ◽  
Hugo F. Urquina ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Semantic Memory ◽  
Classification Accuracy ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Semantic Fluency ◽  
Semantic Categories ◽  
Semantic Fields ◽  
Living Things

Parkinson’s disease (PD) is a neurodegenerative disorder that causes a progressive impairment in motor and cognitive functions. Although semantic fluency deficits have been described in PD, more specific semantic memory (SM) and lexical availability (LA) domains have not been previously addressed. Here, we aimed to characterize the cognitive performance of PD patients in a set of SM and LA measures and determine the smallest set of neuropsychological (lexical, semantic, or executive) variables that most accurately classify groups. Thirty early-stage non-demented PD patients (age 35–75, 10 females) and thirty healthy controls (age 36–76, 12 females) were assessed via general cognitive, SM [three subtests of the CaGi battery including living (i.e., elephant) and non-living things (i.e., fork)], and LA (eliciting words from 10 semantic categories related to everyday life) measures. Results showed that PD patients performed lower than controls in two SM global scores (picture naming and naming in response to an oral description). This impairment was particularly pronounced in the non-living things subscale. Also, the number of words in the LA measure was inferior in PD patients than controls, in both larger and smaller semantic fields, showing a more inadequate recall strategy. Notably, the classification algorithms indicated that the SM task had high classification accuracy. In particular, the denomination of non-living things had a classification accuracy of ∼80%. These results suggest that frontostriatal deterioration in PD leads to search strategy deficits in SF and the potential disruption in semantic categorization. These findings are consistent with the embodied view of cognition.

Fuzzy Classification Methods Based Diagnosis of Parkinson’s disease from Speech Test Cases

2019 ◽  
Vol 12 (2) ◽  
pp. 100-120
Author(s):  
Niousha Karimi Dastjerd ◽  
Onur Can Sert ◽  
Tansel Ozyer ◽  
Reda Alhajj
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Learning Approaches ◽  
Healthy Individuals ◽  
Data Set ◽  
Sound Recordings ◽  
Acceptable Accuracy ◽  
Computing Model ◽  
Neuro Fuzzy ◽  
Fuzzy Classifiers

Background: Together with the Alzheimer’s disease, Parkinson’s disease is considTogether with the Alzheimer’s disease, Parkinson’s disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease.ered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease. This paper contributes an attractive prediction framework based on some machine learning approaches. Several fuzzy classifiers have been employed in the process to distinguish people with Parkinsonism from healthy individuals. The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” available from the UCI repository. The results reported in this paper are better than the results reported by Sakar et al., where the same dataset was used, but with different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al. Objectives: This paper contributes an attractive prediction framework based on some machine learning approaches for distinguishing people with Parkinsonism from healthy individuals. Methods: Several fuzzy classifiers such as Inductive Fuzzy Classifier, Fuzzy Rough Classifier and two types of neuro-fuzzy classifiers have been employed. Results: The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” of 40 subjects available on the UCI repository. Conclusion: The results achieved show that FURIA, MLP- Bagging - SGD, genfis2 and scg1 performed the best among the fuzzy rough, WEKA, adaptive neuro-fuzzy and neuro-fuzzy classifiers, respectively. The worst performance belongs to nearest neighborhood, IBK, genfis3 and scg3 among the formerly mentioned classifiers. The results reported in this paper are better in comparison to the results reported in Sakar et al., where the same dataset was used, with utilization of different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al.

Metabolic pattern analysis of 18F-FDG PET as a marker for Parkinson’s disease: a systematic review and meta-analysis

2019 ◽  
Vol 30 (7) ◽  
pp. 743-756 ◽  
Author(s):  
Si-Chun Gu ◽  
Qing Ye ◽  
Can-Xing Yuan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Diagnostic Accuracy ◽  
Fdg Pet ◽  
Pattern Analysis ◽  
Statistical Parametric Mapping ◽  
Sensitivity Analyses ◽  
Small Samples ◽  
Summary Receiver Operating Characteristic ◽  
Metabolic Pattern

Abstract A large number of articles have assessed the diagnostic accuracy of the metabolic pattern analysis of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in Parkinson’s disease (PD); however, different studies involved small samples with various controls and methods, leading to discrepant conclusions. This study aims to consolidate the available observational studies and provide a comprehensive evaluation of the clinical utility of 18F-FDG PET for PD. The methods included a systematic literature search and a hierarchical summary receiver operating characteristic approach. Sensitivity analyses according to different pattern analysis methods (statistical parametric mapping versus scaled subprofile modeling/principal component analysis) and control population [healthy controls (HCs) versus atypical parkinsonian disorder (APD) patients] were performed to verify the consistency of the main results. Additional analyses for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) were conducted. Fifteen studies comprising 1446 subjects (660 PD patients, 499 APD patients, and 287 HCs) were included. The overall diagnostic accuracy of 18F-FDG in differentiating PD from APDs and HCs was quite high, with a pooled sensitivity of 0.88 [95% confidence interval (95% CI), 0.85–0.91] and a pooled specificity of 0.92 (95% CI, 0.89–0.94), with sensitivity analyses indicating statistically consistent results. Additional analyses showed an overall sensitivity and specificity of 0.87 (95% CI, 0.76–0.94) and 0.93 (95% CI, 0.89–0.96) for MSA and 0.91 (95% CI, 0.78–0.95) and 0.96 (95% CI, 0.92–0.98) for PSP. Our study suggests that the metabolic pattern analysis of 18F-FDG PET has high diagnostic accuracy in the differential diagnosis of parkinsonian disorders.

scholarly journals Traditional Chinese Medicine symptom pattern analysis for Parkinson's disease

2017 ◽  
Vol 37 (5) ◽  
pp. 688-694 ◽  
Author(s):  
Chen Hongzhi ◽  
He Jiancheng ◽  
Teng Long ◽  
Yuan Canxing ◽  
Zhang Zhe
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Pattern Analysis ◽  
Symptom Pattern

Dopaminergic treatment modulates sensory attenuation at the onset of the movement in Parkinson's disease: A test of a new framework for bradykinesia

2016 ◽  
Vol 31 (1) ◽  
pp. 143-146 ◽  
Author(s):  
Antonella Macerollo ◽  
Jui-Cheng Chen ◽  
Prasad Korlipara ◽  
Thomas Foltynie ◽  
John Rothwell ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Sensory Attenuation ◽  
New Framework

scholarly journals Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients

2021 ◽  
Author(s):  
Aleksandar Miladinovic ◽  
Milos Ajcevic ◽  
Pierpaolo Busan ◽  
Joanna Jarmolowska ◽  
Giulia Silveri ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Transfer Learning ◽  
Classification Accuracy

scholarly journals Gait stride-to-stride variability and foot clearance pattern analysis in Idiopathic Parkinson’s Disease and Vascular Parkinsonism

2019 ◽  
Vol 92 ◽  
pp. 98-104 ◽  
Author(s):  
Flora Ferreira ◽  
Miguel F. Gago ◽  
Estela Bicho ◽  
Catarina Carvalho ◽  
Nafiseh Mollaei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Pattern Analysis ◽  
Idiopathic Parkinson’S Disease ◽  
Vascular Parkinsonism ◽  
Idiopathic Parkinson's Disease ◽  
Foot Clearance

Parkinson's Disease Detection with Gait Recognition using Soft Computing Techniques

2016 ◽  
pp. 357-377 ◽  
Author(s):  
Anupam Shukla ◽  
Chandra Prakash Rathore ◽  
Neera Bhansali
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Classification Accuracy ◽  
Gait Recognition ◽  
Back Propagation ◽  
Disease Diagnosis ◽  
Automated Classification ◽  
Feed Forward Back Propagation ◽  
Degenerative Disorder ◽  
Sensitivity Specificity

Parkinson's disease is a degenerative disorder of the central nervous system which occurs as a result of dopamine loss, a chemical mediator that is responsible for body's ability to control the movements. It's a very common disease among elder population effecting approx 6.3 million people worldwide across all genders, races and cultures. In this chapter, authors have proposed an automated classification system based on Artificial Neural Network using Feed Forward Back-propagation Algorithm for Parkinson's disease diagnosis by analyzing gait of a person. The system is trained, tested and validated by a gait dataset consisting data of Parkinson's disease patients and healthy persons. The system is evaluated based on several measuring parameters like sensitivity, specificity, and classification accuracy. For the proposed system observed classification accuracy is 97.11% using 19 features of gait, and 95.55% using 10 prominent features of gait selected by Genetic Algorithm.

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