Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

Abugabah, Ahed; AlZubi, Ahmad Ali; Al‐Maitah, Mohammed; Alarifi, Abdulaziz

doi:10.1007/s12652-020-02520-y

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…In the study of Abugabah et al, the first 10 patients in the Freiburg dataset were analyzed, 15 statistical features were screened with the help of the krill swarm algorithm, and the distinction between seizures and non-onsets was completed under the artificial algae optimization neural network, which finally obtained 98.9% accuracy [ 57 ]. Malekzadeh et al proposed a seizure detection algorithm that combines handcrafted and deep learning features [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

et al. 2022

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Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.

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Section: Discussionmentioning

confidence: 99%

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

et al. 2022

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“…In the first stage, the AAA-ODBN exploited the AAA-FS technique to elect feature subsets. AAA mimics real algae to survive by determining and moving towards a suitable platform and recreating the upcoming generation [17]. In these subsections, they would concisely examine AAA as follows:…”

Section: The Proposed Modelmentioning

confidence: 99%

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

Duhayyim¹,

Mohamed²,

Alrowais³

et al. 2023

Computer Systems Science and Engineering

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The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.

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“…Recently, neural network-based approaches have been broadly utilized in computer vision, video classification, medical image analysis, and the best results. It is a type of artificial neural network inspired by the hierarchical model of the visual cortex [31]. CNNs is a multilayered structure that includes convolutional layers, pooling layers, activation functions and fully connected layers.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

Mehmood¹,

Abugabah²,

AlZubi³

et al. 2022

Computer Systems Science and Engineering

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Alzheimer's disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of AD automatically. In the proposed system, we use segmented MRI scans. Input data samples of three classes include 110 normal control (NC), 110 mild cognitive impairment (MCI) and 105 AD subjects are used in this paper. The data is acquired from the ADNI database and gray matter (GM) images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models. The proposed approaches segregate among NC, MCI, and AD. While testing both methods applied on the segmented data samples, the highest performance results of the classification in terms of accuracy on NC vs. AD are 95.33% and 89.87%, respectively. The proposed methods distinguish between NC vs. MCI and MCI vs. AD patients with a classification accuracy of 90.74% and 86.69%. The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.

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Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

Cited by 13 publications

References 21 publications

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

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