Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis

Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis

In this paper, a novel approach was developed for Parkinson’s disease (PD) diagnosis based on speech disorders. When the literature about the speech disorders-based PD diagnosis was reviewed, it was seen that the most of approaches were concentrated on the feature selection as the dataset...

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Autores principales: Fatih Demir, Abdulkadir Sengur, Ali Ari, Kamran Siddique, Mohammed Alswaitti
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Convolutional structure
deep LSTM network
feature mapping
PD diagnosis
speech disorders
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Acceso en línea:https://doaj.org/article/480129834d834f12bb790643444cb80e
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spelling oai:doaj.org-article:480129834d834f12bb790643444cb80e2021-11-18T00:03:36ZFeature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis2169-353610.1109/ACCESS.2021.3124765https://doaj.org/article/480129834d834f12bb790643444cb80e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598908/https://doaj.org/toc/2169-3536In this paper, a novel approach was developed for Parkinson’s disease (PD) diagnosis based on speech disorders. When the literature about the speech disorders-based PD diagnosis was reviewed, it was seen that the most of approaches were concentrated on the feature selection as the datasets contained a huge number of features. In contrast, in the proposed approach, instead of eliminating some of the features by using any feature selection method, all features were initially used for forming a mapping procedure where the input feature vectors were converted to the input images. Then, a deep Long Short Term Memory (LSTM) network was employed for PD detection where the obtained images were used. The deep LSTM network carried out both feature extraction and classification processes and its training was carried out in an end-to-end fashion. The activations in the convolutional layer were converted to sequence data through the sequence-folding and sequence-unfolding layers. The activations in the LSTM output with learning parameters were conveyed to the Softmax layer for the classification process. A publically available PD dataset was used in the experimental works and classification accuracy, sensitivity, specificity, precision, and F-score metrics were used for performance evaluation. The obtained accuracy, sensitivity, specificity, precision and F-score values were 94.27%, 0.960, 0.960, 0.910 and 0.930, respectively. The obtained results were also compared with some of the published results and it had seen that most of the achievements of the proposed method are better than the compared methods.Fatih DemirAbdulkadir SengurAli AriKamran SiddiqueMohammed AlswaittiIEEEarticleConvolutional structuredeep LSTM networkfeature mappingPD diagnosisspeech disordersElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149456-149464 (2021)
institution DOAJ
collection DOAJ
language EN
topic Convolutional structure
deep LSTM network
feature mapping
PD diagnosis
speech disorders
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Convolutional structure
deep LSTM network
feature mapping
PD diagnosis
speech disorders
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Fatih Demir
Abdulkadir Sengur
Ali Ari
Kamran Siddique
Mohammed Alswaitti
Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis
description In this paper, a novel approach was developed for Parkinson’s disease (PD) diagnosis based on speech disorders. When the literature about the speech disorders-based PD diagnosis was reviewed, it was seen that the most of approaches were concentrated on the feature selection as the datasets contained a huge number of features. In contrast, in the proposed approach, instead of eliminating some of the features by using any feature selection method, all features were initially used for forming a mapping procedure where the input feature vectors were converted to the input images. Then, a deep Long Short Term Memory (LSTM) network was employed for PD detection where the obtained images were used. The deep LSTM network carried out both feature extraction and classification processes and its training was carried out in an end-to-end fashion. The activations in the convolutional layer were converted to sequence data through the sequence-folding and sequence-unfolding layers. The activations in the LSTM output with learning parameters were conveyed to the Softmax layer for the classification process. A publically available PD dataset was used in the experimental works and classification accuracy, sensitivity, specificity, precision, and F-score metrics were used for performance evaluation. The obtained accuracy, sensitivity, specificity, precision and F-score values were 94.27%, 0.960, 0.960, 0.910 and 0.930, respectively. The obtained results were also compared with some of the published results and it had seen that most of the achievements of the proposed method are better than the compared methods.
format article
author Fatih Demir
Abdulkadir Sengur
Ali Ari
Kamran Siddique
Mohammed Alswaitti
author_facet Fatih Demir
Abdulkadir Sengur
Ali Ari
Kamran Siddique
Mohammed Alswaitti
author_sort Fatih Demir
title Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis
title_short Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis
title_full Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis
title_fullStr Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis
title_full_unstemmed Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis
title_sort feature mapping and deep long short term memory network-based efficient approach for parkinson’s disease diagnosis
publisher IEEE
publishDate 2021
url https://doaj.org/article/480129834d834f12bb790643444cb80e
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AT aliari featuremappinganddeeplongshorttermmemorynetworkbasedefficientapproachforparkinsonx2019sdiseasediagnosis
AT kamransiddique featuremappinganddeeplongshorttermmemorynetworkbasedefficientapproachforparkinsonx2019sdiseasediagnosis
AT mohammedalswaitti featuremappinganddeeplongshorttermmemorynetworkbasedefficientapproachforparkinsonx2019sdiseasediagnosis
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