An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest
Parkinson’s disease (PD) is a neurodegenerative disorder characterized mainly by motor-related impairment, an accurate, quantitative, and objective diagnosis is an effective way to slow the disease deterioration process. In this paper, a user-friendly auxiliary diagnostic system for PD is...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9853627/ |
| _version_ | 1797805179205058560 |
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| author | Min Chen Zhanfang Sun Fei Su Yan Chen Degang Bu Yubo Lyu |
| author_facet | Min Chen Zhanfang Sun Fei Su Yan Chen Degang Bu Yubo Lyu |
| author_sort | Min Chen |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a neurodegenerative disorder characterized mainly by motor-related impairment, an accurate, quantitative, and objective diagnosis is an effective way to slow the disease deterioration process. In this paper, a user-friendly auxiliary diagnostic system for PD is constructed based on the upper limb movement conditions of 100 subjects consisting of 50 PD patients and 50 healthy subjects. This system includes wearable sensors that collect upper limb movement data, host computer for data processing and classification, and graphic user interface (GUI). The genetic algorithm optimized random forest classifier is introduced to classify PD and normal states based on the selected optimal features, and the 50 trials leave-one-out cross-validation is used to evaluate the performance of the classifier, with the highest accuracy of 94.4%. The classification accuracy among different upper limb movement tasks and with the different number of sensors are compared, results show that the task with only alternation hand movement also has satisfactory classification accuracy, and sensors on both wrists performance better than one sensor on a single wrist. The utility of the proposed system is illustrated by neurologists with a deployed GUI during the clinical inquiry, opening the possibility for a wide range of applications in the auxiliary diagnosis of PD. |
| first_indexed | 2024-03-13T05:47:12Z |
| format | Article |
| id | doaj.art-bf12c202aef349c8bafb2dbe9adfa41b |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:47:12Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-bf12c202aef349c8bafb2dbe9adfa41b2023-06-13T20:07:59ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01302254226310.1109/TNSRE.2022.31978079853627An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random ForestMin Chen0https://orcid.org/0000-0003-1702-1820Zhanfang Sun1Fei Su2https://orcid.org/0000-0002-2585-6564Yan Chen3Degang Bu4Yubo Lyu5School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, ChinaSchool of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, ChinaSchool of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, ChinaDepartment of Neurology, Shandong Provincial Hospital (Provincial Hospital Affiliated to Shandong First Medical University), Jinan, ChinaSchool of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, ChinaMedical Imaging Center, Shanghai Jiahui International Hospital, Shanghai, ChinaParkinson’s disease (PD) is a neurodegenerative disorder characterized mainly by motor-related impairment, an accurate, quantitative, and objective diagnosis is an effective way to slow the disease deterioration process. In this paper, a user-friendly auxiliary diagnostic system for PD is constructed based on the upper limb movement conditions of 100 subjects consisting of 50 PD patients and 50 healthy subjects. This system includes wearable sensors that collect upper limb movement data, host computer for data processing and classification, and graphic user interface (GUI). The genetic algorithm optimized random forest classifier is introduced to classify PD and normal states based on the selected optimal features, and the 50 trials leave-one-out cross-validation is used to evaluate the performance of the classifier, with the highest accuracy of 94.4%. The classification accuracy among different upper limb movement tasks and with the different number of sensors are compared, results show that the task with only alternation hand movement also has satisfactory classification accuracy, and sensors on both wrists performance better than one sensor on a single wrist. The utility of the proposed system is illustrated by neurologists with a deployed GUI during the clinical inquiry, opening the possibility for a wide range of applications in the auxiliary diagnosis of PD.https://ieeexplore.ieee.org/document/9853627/Parkinson’s diseaseauxiliary diagnostic systemwearable sensorsrandom forestgenetic algorithm |
| spellingShingle | Min Chen Zhanfang Sun Fei Su Yan Chen Degang Bu Yubo Lyu An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest IEEE Transactions on Neural Systems and Rehabilitation Engineering Parkinson’s disease auxiliary diagnostic system wearable sensors random forest genetic algorithm |
| title | An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest |
| title_full | An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest |
| title_fullStr | An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest |
| title_full_unstemmed | An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest |
| title_short | An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest |
| title_sort | auxiliary diagnostic system for parkinson x2019 s disease based on wearable sensors and genetic algorithm optimized random forest |
| topic | Parkinson’s disease auxiliary diagnostic system wearable sensors random forest genetic algorithm |
| url | https://ieeexplore.ieee.org/document/9853627/ |
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