EEG-Centered Parkinson's Disease Prediction System Using Gaussian Kernel Discrete Wavelet Transform and Leaky Single Peak Triangle Context Convolutional Neural Network Deep Learning Technique
Main Article Content
Abstract
Parkinson's disease (PD), a neurodegenerative illness where dopamine-producing brain cells are destroyed, slows down motor performance. To identify PD, numerous techniques have recently been established. In this research, a novel idiosyncratic Gaussian Kernel Discrete Wavelet Transform (GKDWT) and a Leaky Single Peak Triangle Context Convolutional Neural Network (LeakySPTC-CNN)-centered effective Deep Learning (DL) model have been developed for the prediction of PD. The Electroencephalogram (EEG) signal is first acquired and then divided into one-second segments. The model then carried out signal filtering and spectrogram conversion. The features are then extracted with the support of GKDWT, and feature selection and ranking are carried out using the Quadratic Chimp Optimization Algorithm (QChOA). The LeakySPTC-CNN is used to categorize both ill and healthy people. The model has a promising future, according to the experiential evaluation, and it also offers a better way to diagnose Parkinson's disease (PD) early.
Keywords:
Parkinson's disease (PD), Electroencephalogram (EEG), Convolutional Neural Network (CNN), Gaussian Kernel function (GK), Wiener Filter (WF)
Article Details
How to Cite
SWARNALATHA, R; SALUNKHE, Sachin.
EEG-Centered Parkinson's Disease Prediction System Using Gaussian Kernel Discrete Wavelet Transform and Leaky Single Peak Triangle Context Convolutional Neural Network Deep Learning Technique.
Medical Research Archives, [S.l.], v. 11, n. 5, may 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/3699>. Date accessed: 04 dec. 2024.
doi: https://doi.org/10.18103/mra.v11i5.3699.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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2. Smith K Khare, Varun Bajaj and Rajendra Acharya U, “PDCNNet an automatic framework for the detection of parkinson’s disease using EEG signals”, IEEE Sensors Journal, vol. 21, no. 15, pp. 17017-17024, 2021. doi: 10.1109/JSEN.2021.3080135.
3. Mehrbakhsh Nilashi, Hossein Ahmadi, Abbas Sheikhtaheri, Roya Naemi, Reem Alotaibi,Ala Abdulsalam Alarood, Asmaa Munshi, Tarik A Rashid and Jing Zhao, “Remote tracking of parkinson’s disease progression using ensembles of deep belief network and self-organizing map”, Expert Systems with Applications, vol. 159, pp. 1-13, 2020. https://doi.org/10.1016/j.eswa.2020.113562
4. Abhishek Mahajan, R Swarnalatha , Kashif Sherwani and Neelesh Kumar,” LabVIEW Based Monitoring and Rehabilitation Module for Freezing of Gait in Parkinson’s Disease” Journal of Medical Engineering and Technology, 43(1), 48-54, 2019. https://doi.org/10.1080/03091902.2019.1609608
5. Francis P Grenn, Anni Moore, Sara Bandres-Ciga, Lynne Krohn, Cornelis Blauwendraat and on behalf of the International Parkinson’s Disease Genomics Consortium (IPDGC), “Assessment of ANG variants in parkinson’s disease”, Neurobiology of Aging, vol. 104, pp. 1-4, 2021. https://doi.org/10.1016/j.neurobiolaging.2021.03.006
6. Prashanth R and Sumantra Dutta Roy, “Early detection of parkinson’s disease through patient questionnaire and predictive modelling”, International Journal of Medical Informatics, vol. 119, pp. 75-87, 2018. https://doi.org/10.1016/j.ijmedinf.2018.09.008
7. Rajamanickam Yuvaraj, Rajendra Acharya U and Yuki Hagiwara, “A novel parkinson’s disease diagnosis index using higher-order spectra features in EEG signals”, Neural Computing and Applications, vol. 30, pp. 1225-1235, 2016. https://doi.org/10.1007/s00521-016-2756-z
8. Amira S Ashour, Amira El-Attar, Nilanjan Dey, Hatem Abd El-Kader and Mostafa M Abd El-Naby, “Long short term memory based patient- dependent model for FOG
detection in parkinson’s disease”, Pattern Recognition Letters, vol. 131, pp. 23-29, 2019. https://doi.org/10.1016/j.patrec.2019.11.036
9. Neeraja Padman, R Swarnalatha , Varsha Venkatesh, Neelesh Kumar, “Telediagnosis of Parkinson’s Disease Symptom Severity using H&Y scale”, Journal of Engineering Science and Technology. Vol 15, No 3, June 2020. pp 1466-1480.
10. R Swarnalatha, Neelesh Kumar , Amrin Jameel Shanaz "Design of an Insole using Force Sensing Resistors for Gait Analysis and Validation using Zebris FDM System" Journal of Engineering and Applied Sciences. Vol. 15, No. 2, pp. 430-436, 2020. DOI: 10.36478/jeasci.2020.430.436
11. Turker Tuncer, Sengul Dogan and Rajendra Acharya U, “Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels”, Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 211-220, 2019. https://doi.org/10.1016/j.bbe.2019.05.006
12. Shu Lih Oh, Yuki Hagiwara, Raghavendra U, Rajamanickam Yuvaraj, Arunkumar N, Murugappan M and Rajendra Acharya U, “A deep learning approach for Parkinson’s disease diagnosis from EEG signals”, Neural Computing and Applications, vol. 32, no. 15-16, pp. 10927-10933, 2018. https://doi.org/10.1007/s00521-018-3689-5
13. Xinjie Shi, Tianqi Wang, Lan Wang, Hanjun Liu and Nan Yan, “Hybrid convolutional recurrent neural networks outperform CNN and RNN in task-state EEG detection for parkinson’s disease”, Proceedings of APSIPA Annual Summit and Conference, 18-21 November 2019, Lanzhou, China, 2019. doi: 10.1109/APSIPAASC47483.2019.9023190.
14. Jinting Wan, Guosheng Yi and Jiang Wang, “EEG sub-band abnormality of early-stage parkinson's disease with mild cognitive impairment”, Proceedings of the 39th Chinese Control Conference, July 27-29, 2020, Shenyang, China, 2020. doi: 10.23919/CCC50068.2020.9188888.
15. Ying Wang, Floris Beuving, Jorik Nonnekes, Mike X Cohen, Xi Long, Ronald M Aarts and Richard van Wezel, “Freezing of gait detection in parkinson’s disease via
multimodal analysis of EEG and accelerometer signals”, 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 20-24 July 2020, Montreal, QC, Canada, 2020. doi: 10.1109/EMBC44109.2020.9175288
16. Zehong Cao, Alka Rachel John, Hsiang-Ting Chen, Kaylena Ehgoetz Martens, Matthew Georgiades, Moran Gilat, Hung T Nguyen, Simon J. G Lewis and Chin-Teng Lin, “Identification of EEG dynamics during freezing of gait and voluntary stopping in patients with parkinson’s disease”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1774-1783, 2021. doi: 10.1109/TNSRE.2021.3107106.
17. Seyed Alireza Khoshnevis and Ravi Sankar, “Classification of the stages of parkinson’s disease using novel higherorder statistical features of EEG signals”, Neural Computing and Applications, vol. 33, pp. 7615-7627, 2020. https://doi.org/10.1007/s00521-020-05505-2
18. Anita Pal, Madhuri Behari, Vinay Goyal and Ratna Sharma, “Study of EEG microstates in parkinson’s disease a potential biomarker”, Cognitive Neurodynamics, vol. 15, no. 3, pp. 463-471, 2020. https://doi.org/10.1007/s11571-020-09643-0
19. Syed Aamir Ali Shah, Lei Zhang and Abdul Bais, “Dynamical system based compact deep hybrid network for classification of parkinson disease related EEG signals”, Neural Networks, vol. 130, pp. 75-84, 2020. https://doi.org/10.1016/j.neunet.2020.06.018Get rights and content
20. Hanrui Zhang, Kaiwen Deng, Hongyang Li, Roger L. Albin and Yuanfang Guan, “Deep learning identifies digital biomarkers for self-reported parkinsons disease”, Patterns, vol. 1, no. 3, pp. 1-12, 2020. https://doi.org/10.1016/j.patter.2020.100042
21. Smith K Khare, Varun Bajaj and Rajendra Acharya U, “Detection of parkinsons disease using automated tunable Q wavelet transform technique with EEG signals”, Biocybernetics and Biomedical Engineering, vol. 41, no. 2, pp. 679-689, 2021. https://doi.org/10.1016/j.bbe.2021.04.008
22. Erfan Naghsh, Mohamad Farzan Sabahi and Soosan Beheshti, “Spatial analysis of EEG signals for Parkinson’s disease stage detection”, Signal, Image and Video Processing, vol. 14, no. 2, pp. 397-405, 2019. https://doi.org/10.1007/s11760-019-01564-8
23. Ana Paula S de Oliveira, Maíra Araújo de Santana, Maria Karoline S Andrade, Juliana Carneiro Gomes, Marcelo C. A Rodrigues and Wellington P dos Santos, “Early diagnosis of parkinson’s disease using EEG, machine learning and partial directed coherence”, Research on Biomedical Engineering, vol. 36, no. 3, pp. 311-331, 2020. https://doi.org/10.1007/s42600-020-00072-w
24. Soojin Lee, Ramy Hussein, Rabab Ward, Jane Wang Z and Martin J McKeown, “A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson’s disease”, Journal of Neuroscience Methods, vol. 361, pp. 1-12, 2021. https://doi.org/10.1016/j.jneumeth.2021.109282
25. Fahim Anjum Md, Soura Dasgupta, Raghuraman Mudumbai, Arun Singh, James F Cavanagh and Nandakumar S Narayanan, “Linear predictive coding distinguishes spectral EEG features of Parkinson’s disease”, Parkinsonism and Related Disorders, vol. 79, pp. 79-85, 2020. https://doi.org/10.1016/j.parkreldis.2020.08.001