A deep learning approach for Parkinson’s disease diagnosis from EEG signals
An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are u...
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| Veröffentlicht in: | Neural computing & applications Jg. 32; H. 15; S. 10927 - 10933 |
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| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Springer London
01.08.2020
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and
twenty
normal subjects in this study. A
thirteen
-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage. |
|---|---|
| AbstractList | An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and
twenty
normal subjects in this study. A
thirteen
-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage. An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and twenty normal subjects in this study. A thirteen-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage. |
| Author | Oh, Shu Lih Hagiwara, Yuki Yuvaraj, Rajamanickam Raghavendra, U. Acharya, U. Rajendra Arunkumar, N. Murugappan, M. |
| Author_xml | – sequence: 1 givenname: Shu Lih surname: Oh fullname: Oh, Shu Lih organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic – sequence: 2 givenname: Yuki surname: Hagiwara fullname: Hagiwara, Yuki organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic – sequence: 3 givenname: U. surname: Raghavendra fullname: Raghavendra, U. organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education – sequence: 4 givenname: Rajamanickam surname: Yuvaraj fullname: Yuvaraj, Rajamanickam organization: School of Electrical and Electronic Engineering, Nanyang Technological University – sequence: 5 givenname: N. surname: Arunkumar fullname: Arunkumar, N. organization: Department of Electronics and Instrumentation, SASTRA University – sequence: 6 givenname: M. surname: Murugappan fullname: Murugappan, M. organization: Kuwait College of Science and Technology – sequence: 7 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra email: aru@np.edu.sg organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University |
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| ContentType | Journal Article |
| Copyright | The Natural Computing Applications Forum 2018 The Natural Computing Applications Forum 2018. Copyright Springer Nature B.V. Aug 2020 |
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