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Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms

Due to overlapping tremor features, the medical diagnosis of Parkinson’s disease (PD) and essential tremor (ET) mainly relies on the clinical experience of doctors, which often leads to misdiagnosis. Seven predictive models using machine learning algorithms including random forest (RF), eXtreme Grad...

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Autores principales: Xing, Xupo, Luo, Ningdi, Li, Shun, Zhou, Liche, Song, Chengli, Liu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978337/
https://www.ncbi.nlm.nih.gov/pubmed/35386595
http://dx.doi.org/10.3389/fnins.2022.701632
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author Xing, Xupo
Luo, Ningdi
Li, Shun
Zhou, Liche
Song, Chengli
Liu, Jun
author_facet Xing, Xupo
Luo, Ningdi
Li, Shun
Zhou, Liche
Song, Chengli
Liu, Jun
author_sort Xing, Xupo
collection PubMed
description Due to overlapping tremor features, the medical diagnosis of Parkinson’s disease (PD) and essential tremor (ET) mainly relies on the clinical experience of doctors, which often leads to misdiagnosis. Seven predictive models using machine learning algorithms including random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), ridge classification (Ridge), backpropagation neural network (BP), and convolutional neural network (CNN) were evaluated and compared aiming to better differentiate between PD and ET by using accessible demographics and tremor information of the upper limbs. The tremor information including tremor acceleration and surface electromyogram (sEMG) signals were collected from 398 patients (PD = 257, ET = 141) and then were used to train the established models to separate PD and ET. The performance of the models was evaluated by indices of accuracy and area under the curve (AUC), which indicated the ensemble learning models including RF and XGBoost showed the best overall predictive ability with accuracy above 0.84 and AUC above 0.90. Furthermore, the relative importance of sex, age, four postures, and five tremor features was analyzed and ranked showing that the dominant frequency of sEMG of flexors, the average amplitude of sEMG of flexors, resting posture, and winging posture had a greater impact on the diagnosis of PD, whereas sex and age were less important. These results provide a reference for the intelligent diagnosis of PD and show promise for use in wearable tremor suppression devices.
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spelling pubmed-89783372022-04-05 Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms Xing, Xupo Luo, Ningdi Li, Shun Zhou, Liche Song, Chengli Liu, Jun Front Neurosci Neuroscience Due to overlapping tremor features, the medical diagnosis of Parkinson’s disease (PD) and essential tremor (ET) mainly relies on the clinical experience of doctors, which often leads to misdiagnosis. Seven predictive models using machine learning algorithms including random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), ridge classification (Ridge), backpropagation neural network (BP), and convolutional neural network (CNN) were evaluated and compared aiming to better differentiate between PD and ET by using accessible demographics and tremor information of the upper limbs. The tremor information including tremor acceleration and surface electromyogram (sEMG) signals were collected from 398 patients (PD = 257, ET = 141) and then were used to train the established models to separate PD and ET. The performance of the models was evaluated by indices of accuracy and area under the curve (AUC), which indicated the ensemble learning models including RF and XGBoost showed the best overall predictive ability with accuracy above 0.84 and AUC above 0.90. Furthermore, the relative importance of sex, age, four postures, and five tremor features was analyzed and ranked showing that the dominant frequency of sEMG of flexors, the average amplitude of sEMG of flexors, resting posture, and winging posture had a greater impact on the diagnosis of PD, whereas sex and age were less important. These results provide a reference for the intelligent diagnosis of PD and show promise for use in wearable tremor suppression devices. Frontiers Media S.A. 2022-03-21 /pmc/articles/PMC8978337/ /pubmed/35386595 http://dx.doi.org/10.3389/fnins.2022.701632 Text en Copyright © 2022 Xing, Luo, Li, Zhou, Song and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Xing, Xupo
Luo, Ningdi
Li, Shun
Zhou, Liche
Song, Chengli
Liu, Jun
Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms
title Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms
title_full Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms
title_fullStr Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms
title_full_unstemmed Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms
title_short Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms
title_sort identification and classification of parkinsonian and essential tremors for diagnosis using machine learning algorithms
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978337/
https://www.ncbi.nlm.nih.gov/pubmed/35386595
http://dx.doi.org/10.3389/fnins.2022.701632
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