A new hybrid intelligent system for accurate detection of Parkinson's disease

2014 ◽  
Vol 113 (3) ◽  
pp. 904-913 ◽  
Author(s):  
M. Hariharan ◽  
Kemal Polat ◽  
R. Sindhu
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Intelligent System ◽  
Accurate Detection ◽  
Hybrid Intelligent System

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.

Enzyme Immunoassay-Based Platform for Accurate Detection of Serum Pathological α-Synuclein in Parkinson’s Disease Patients

2020 ◽  
Vol 11 (24) ◽  
pp. 4179-4190
Author(s):  
Benjamin W. Schlichtmann ◽  
Naveen Kondru ◽  
Monica M. Hepker ◽  
Anumantha G. Kanthasamy ◽  
Vellareddy Anantharam ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Enzyme Immunoassay ◽  
Accurate Detection

scholarly journals Cloud-Based Parkinson’s Disease Diagnosis Using Machine Learning

2021 ◽  
Vol 8 (6) ◽  
pp. 915-922
Author(s):  
Ahmed R. Nasser ◽  
Ali M. Mahmood
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Treatment ◽  
Intelligent System ◽  
Feature Selection Method ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Accuracy Measurement ◽  
Diagnosis Model

Parkinson’s disease (PD) harms the human brain's nervous system and can affect the patient's life. However, the diagnosis of PD diagnosis in the first stages can lead to early treatment and save costs. In this paper, a cloud-based machine learning diagnosing intelligent system is proposed for the PD with respect to patient voice. The proposed system is composed of two stages. In the first stage, two machine learning approaches, Random-Forest (RF) and Long-Short-Term-Memory (LSTM) are applied to generate a model that can be used for early treatment of PD. In this stage, a feature selection method is used to choose the minimum subset of the best features, which can be utilized later to generate the classification model. In the second stage, the best diagnosis model is deployed in cloud computing. In this stage, an Android application is also designed to provide the interface to the diagnosis model. The performance evaluation of the diagnosis model is conducted based on the F-score accuracy measurement. The result shows that the LTSM model has superior accuracy with 95% of the F-score compared with the RF model. Therefore, the LSTM model is selected for implementing a cloud-based PD diagnosing application using Python and Java.

scholarly journals Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson’s Disease

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Laureano Moro-Velazquez ◽  
Jorge A. Gomez-Garcia ◽  
Juan I. Godino-Llorente ◽  
Francisco Grandas-Perez ◽  
Stefanie Shattuck-Hufnagel ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cross Validation ◽  
Automatic Detection ◽  
Automatic Identification ◽  
New Approaches ◽  
Accurate Detection ◽  
Manner Of Articulation ◽  
The Impact ◽  
Detection Schemes

AbstractLiterature documents the impact of Parkinson’s Disease (PD) on speech but no study has analyzed in detail the importance of the distinct phonemic groups for the automatic identification of the disease. This study presents new approaches that are evaluated in three different corpora containing speakers suffering from PD with two main objectives: to investigate the influence of the different phonemic groups in the detection of PD and to propose more accurate detection schemes employing speech. The proposed methodology uses GMM-UBM classifiers combined with a technique introduced in this paper called phonemic grouping, that permits observation of the differences in accuracy depending on the manner of articulation. Cross-validation results reach accuracies between 85% and 94% with AUC ranging from 0.91 to 0.98, while cross-corpora trials yield accuracies between 75% and 82% with AUC between 0.84 and 0.95, depending on the corpus. This is the first work analyzing the generalization properties of the proposed approaches employing cross-corpora trials and reaching high accuracies. Among the different phonemic groups, results suggest that plosives, vowels and fricatives are the most relevant acoustic segments for the detection of PD with the proposed schemes. In addition, the use of text-dependent utterances leads to more consistent and accurate models.

An intelligent system for Parkinson’s diagnosis using hybrid feature selection approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Intelligent System ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Feature Reduction ◽  
Selection Methods ◽  
Selection Approach ◽  
Feature Selection Approach

Parkinson’s is the second most common neurodegenerative disorder after Alzheimer’s disease which adversely affects the nervous system of the patients. During the nascent stage, the symptoms of Parkinson’s disease are mild and sometimes go unnoticeable but as the disease progresses the symptoms go severe, so its diagnosis at an early stage is not easy. Recent research has shown that changes in speech or distortion in voice can be taken effectively used for early Parkinson’s detection. In this work, the authors propose a system of Parkinson's disease detection using speech signals. As the feature selection plays an important role during classification, authors have proposed a hybrid MIRFE feature selection approach. The result of the proposed feature selection approach is compared with the 5 standard feature selection methods by XGBoost classifier. The proposed MIRFE approach selects 40 features out of 754 features with a feature reduction ratio of 94.69%. An accuracy of 93.88% and area under curve (AUC) of 0.978 is obtained by the proposed system.

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