Rag‐bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogram
Abstract Electroencephalogram (EEG) signal is mostly utilised to monitor epilepsy to revitalize the close loop brain. Several classical methods devised to identify seizures rely on visual analysis of EEG signals which is a costly and complex task if channel count increases. A novel method, namely, a...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-04-01
|
| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558686762958848 |
|---|---|
| author | Prabin Jose Johnrose Sundaram Muniasamy Jaffino Georgepeter |
| author_facet | Prabin Jose Johnrose Sundaram Muniasamy Jaffino Georgepeter |
| author_sort | Prabin Jose Johnrose |
| collection | DOAJ |
| description | Abstract Electroencephalogram (EEG) signal is mostly utilised to monitor epilepsy to revitalize the close loop brain. Several classical methods devised to identify seizures rely on visual analysis of EEG signals which is a costly and complex task if channel count increases. A novel method, namely, a rag‐Rider optimisation algorithm (rag‐ROA) is devised for training a deep recurrent neural network (Deep RNN) to discover epileptic seizures. Here the input EEG signals are splitted to different channels wherein each channel undergoes feature extraction. The features like Holoentropy, relative energy, fluctuation index, tonal power ratio, spectral features along with the proposed Taylor‐based delta amplitude modulation spectrogram (Taylor‐based delta AMS) are mined from each channel. The proposed Taylor‐based delta AMS is designed by integrating the delta AMS and Taylor series. The probabilistic principal component analysis (PPCA) is employed to reduce the feature dimension. The dimensionally reduced feature vector is classified with Deep RNN using rag‐ROA, which is designed by integrating rag‐bull rider along with the four other riders available in the Rider optimisation algorithm (ROA). Thus, the resulted output of the proposed rag‐ROA‐based deep RNN is employed for EEG seizure detection. The proposed rag‐ROA‐based Deep RNN showed improved results with maximal accuracy of 88.8%, maximal sensitivity of 91.9%, and maximal specificity of 89.9% than the existing methods, such as Wavelet + SVM, HWPT + RVM, MVM‐FzEN, and EWT + RF, using the TUEP dataset. |
| format | Article |
| id | doaj-art-c522a9e430d8450e998ca08994777de1 |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-c522a9e430d8450e998ca08994777de12025-02-03T01:31:55ZengWileyIET Signal Processing1751-96751751-96832021-04-0115212214010.1049/sil2.12019Rag‐bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogramPrabin Jose Johnrose0Sundaram Muniasamy1Jaffino Georgepeter2Department of Electronics and Communication Engineering Kamaraj College of Engineering and Technology Madurai IndiaDepartment of Electronics and Communication Engineering VSB Engineering College Karur IndiaDepartment of Electronics and Communication Engineering Aditya College of Engineering Surampalem IndiaAbstract Electroencephalogram (EEG) signal is mostly utilised to monitor epilepsy to revitalize the close loop brain. Several classical methods devised to identify seizures rely on visual analysis of EEG signals which is a costly and complex task if channel count increases. A novel method, namely, a rag‐Rider optimisation algorithm (rag‐ROA) is devised for training a deep recurrent neural network (Deep RNN) to discover epileptic seizures. Here the input EEG signals are splitted to different channels wherein each channel undergoes feature extraction. The features like Holoentropy, relative energy, fluctuation index, tonal power ratio, spectral features along with the proposed Taylor‐based delta amplitude modulation spectrogram (Taylor‐based delta AMS) are mined from each channel. The proposed Taylor‐based delta AMS is designed by integrating the delta AMS and Taylor series. The probabilistic principal component analysis (PPCA) is employed to reduce the feature dimension. The dimensionally reduced feature vector is classified with Deep RNN using rag‐ROA, which is designed by integrating rag‐bull rider along with the four other riders available in the Rider optimisation algorithm (ROA). Thus, the resulted output of the proposed rag‐ROA‐based deep RNN is employed for EEG seizure detection. The proposed rag‐ROA‐based Deep RNN showed improved results with maximal accuracy of 88.8%, maximal sensitivity of 91.9%, and maximal specificity of 89.9% than the existing methods, such as Wavelet + SVM, HWPT + RVM, MVM‐FzEN, and EWT + RF, using the TUEP dataset.https://doi.org/10.1049/sil2.12019brainelectroencephalographyfeature extractionmedical signal detectionmedical signal processingneurophysiology |
| spellingShingle | Prabin Jose Johnrose Sundaram Muniasamy Jaffino Georgepeter Rag‐bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogram IET Signal Processing brain electroencephalography feature extraction medical signal detection medical signal processing neurophysiology |
| title | Rag‐bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogram |
| title_full | Rag‐bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogram |
| title_fullStr | Rag‐bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogram |
| title_full_unstemmed | Rag‐bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogram |
| title_short | Rag‐bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogram |
| title_sort | rag bull rider optimisation with deep recurrent neural network for epileptic seizure detection using electroencephalogram |
| topic | brain electroencephalography feature extraction medical signal detection medical signal processing neurophysiology |
| url | https://doi.org/10.1049/sil2.12019 |
| work_keys_str_mv | AT prabinjosejohnrose ragbullrideroptimisationwithdeeprecurrentneuralnetworkforepilepticseizuredetectionusingelectroencephalogram AT sundarammuniasamy ragbullrideroptimisationwithdeeprecurrentneuralnetworkforepilepticseizuredetectionusingelectroencephalogram AT jaffinogeorgepeter ragbullrideroptimisationwithdeeprecurrentneuralnetworkforepilepticseizuredetectionusingelectroencephalogram |