Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder
Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, an...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Online Access: | https://www.mdpi.com/2079-7737/11/8/1220 |
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| author | Anwer Mustafa Hilal Amani Abdulrahman Albraikan Sami Dhahbi Mohamed K. Nour Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah |
| author_facet | Anwer Mustafa Hilal Amani Abdulrahman Albraikan Sami Dhahbi Mohamed K. Nour Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah |
| author_sort | Anwer Mustafa Hilal |
| collection | DOAJ |
| description | Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively. |
| first_indexed | 2024-03-09T11:52:55Z |
| format | Article |
| id | doaj.art-e455c7a628c1460db528a946b8024796 |
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| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-09T11:52:55Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-e455c7a628c1460db528a946b80247962023-11-30T23:13:39ZengMDPI AGBiology2079-77372022-08-01118122010.3390/biology11081220Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse AutoencoderAnwer Mustafa Hilal0Amani Abdulrahman Albraikan1Sami Dhahbi2Mohamed K. Nour3Abdullah Mohamed4Abdelwahed Motwakel5Abu Sarwar Zamani6Mohammed Rizwanullah7Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj 16278, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Makkah 24382, Saudi ArabiaResearch Centre, Future University in Egypt, New Cairo 11745, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj 16278, Saudi ArabiaEpileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively.https://www.mdpi.com/2079-7737/11/8/1220deep learningfeature selectionEEG signalsepileptic seizure recognitionclassificationkrill herd algorithm |
| spellingShingle | Anwer Mustafa Hilal Amani Abdulrahman Albraikan Sami Dhahbi Mohamed K. Nour Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder Biology deep learning feature selection EEG signals epileptic seizure recognition classification krill herd algorithm |
| title | Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder |
| title_full | Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder |
| title_fullStr | Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder |
| title_full_unstemmed | Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder |
| title_short | Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder |
| title_sort | intelligent epileptic seizure detection and classification model using optimal deep canonical sparse autoencoder |
| topic | deep learning feature selection EEG signals epileptic seizure recognition classification krill herd algorithm |
| url | https://www.mdpi.com/2079-7737/11/8/1220 |
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