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Palanisamy KK, Rengaraj A. Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM. Brain Sci 2024; 14:848. [PMID: 39199539 PMCID: PMC11352876 DOI: 10.3390/brainsci14080848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Revised: 08/06/2024] [Accepted: 08/17/2024] [Indexed: 09/01/2024] Open
Abstract
In humans, epilepsy is diagnosed through electroencephalography (EEG) signals. Epileptic seizures (ESs) arise due to anxiety. The detection of anxiety-based seizures is challenging for radiologists, and there is a limited availability of anxiety-based EEG signals. Data augmentation methods are required to increase the number of novel samples. An epileptic seizure arises due to anxiety, which manifests as variations in EEG signal patterns consisting of changes in the size and shape of the signal. In this study, anxiety EEG signals were synthesized by applying data augmentation methods such as random data augmentation (RDA) to existing epileptic seizure signals from the Bonn EEG dataset. The data-augmented anxiety seizure signals were processed using three algorithms-(i) fuzzy C-means-particle swarm optimization-long short-term memory (FCM-PS-LSTM), (ii) particle swarm optimization-long short-term memory (PS-LSTM), and (iii) parrot optimization LSTM (PO-LSTM)-for the detection of anxiety ESs via EEG signals. The predicted accuracies of detecting ESs through EEG signals using the proposed algorithms-namely, (i) FCM-PS-LSTM, (ii) PS-LSTM, and (iii) PO-LSTM-were about 98%, 98.5%, and 96%, respectively.
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Affiliation(s)
| | - Arthi Rengaraj
- Department of ECE, Faculty of Engineering & Technology, SRM Institute of Science and Technology, Ramapuram Campus, Ramapuram, Chennai 600089, India;
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Liu C, Chen W, Li M. A hybrid EEG classification model using layered cascade deep learning architecture. Med Biol Eng Comput 2024; 62:2213-2229. [PMID: 38507121 DOI: 10.1007/s11517-024-03072-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024]
Abstract
The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional studies focus on computing or learning a set of features from EEG to distinguish between different patterns. However, the extraction of characteristic information becomes increasingly difficult as the number of EEG types increases. To address this issue, a creative EEG classification technique is proposed by employing a principal component analysis network (PCANet) coupled with phase space reconstruction (PSR) and power spectrum density (PSD). We have introduced the PSR and PSD to prepare the inputs, where dynamic and frequency information are exposed from deep within PCANet. It is remarkable that a layered cascade strategy is designed to make a powerful deep learner according to the rule of one network vs one task (OVO). The proposed method has achieved greater effects than the individual models and shown superior performance in comparison with state-of-the-art algorithms, which present 98.0% of sensitivity, 99.90% of specificity, and 99.07% of accuracy. Our ensemble PCANet model works in an assembly line-like manner, obviating the need for hand-craft features. Results demonstrate that the proposed scheme can greatly enhances the accuracy and robustness of seizure detection from EEG signals.
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Affiliation(s)
- Chang Liu
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China
| | - Wanzhong Chen
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China
| | - Mingyang Li
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China.
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3
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Zhao W, Wang WF, Patnaik LM, Zhang BC, Weng SJ, Xiao SX, Wei DZ, Zhou HF. Residual and bidirectional LSTM for epileptic seizure detection. Front Comput Neurosci 2024; 18:1415967. [PMID: 38952709 PMCID: PMC11215953 DOI: 10.3389/fncom.2024.1415967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 05/28/2024] [Indexed: 07/03/2024] Open
Abstract
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.
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Affiliation(s)
- Wei Zhao
- Chengyi College, Jimei University, Xiamen, China
| | - Wen-Feng Wang
- Shanghai Institute of Technology, Shanghai, China
- London Institute of Technology, International Academy of Visual Arts and Engineering, London, United Kingdom
| | | | | | - Su-Jun Weng
- Chengyi College, Jimei University, Xiamen, China
| | | | - De-Zhi Wei
- Chengyi College, Jimei University, Xiamen, China
| | - Hai-Feng Zhou
- Marine Engineering Institute, Jimei University, Xiamen, China
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Al-Haddad LA, Alawee WH, Basem A. Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning. Comput Biol Med 2024; 169:107894. [PMID: 38154161 DOI: 10.1016/j.compbiomed.2023.107894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/04/2023] [Accepted: 12/21/2023] [Indexed: 12/30/2023]
Abstract
In the rapidly advancing field of biomedical engineering, effective real-time control of artificial limbs is a pressing research concern. Addressing this, the current study introduces a pioneering method for augmenting task recognition in prosthetic control systems, combining a ReliefF-based Deep Neural Networks (DNNs) approach. This paper has leveraged the MILimbEEG dataset, a comprehensive rich source collection of EEG signals, to calculate statistical features of Arithmetic Mean (AM), Standard Deviation (SD), and Skewness (S) across various motor activities. Supreme Feature Selection (SFS), of the adopted time-domain features, was performed using the ReliefF algorithm. The highest scored DNN-ReliefF developed model demonstrated remarkable performance, achieving accuracy, precision, and recall rates of 97.4 %, 97.3 %, and 97.4 %, respectively. In contrast, a traditional DNN model yielded accuracy, precision, and recall rates of 50.8 %, 51.1 %, and 50.8 %, highlighting the significant improvements made possible by incorporating SFS. This stark contrast underscores the transformative potential of incorporating ReliefF, situating the DNN-ReliefF model as a robust platform for forthcoming advancements in real-time prosthetic control systems.
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Affiliation(s)
- Luttfi A Al-Haddad
- Training and Workshops Center, University of Technology- Iraq, Baghdad, Iraq.
| | - Wissam H Alawee
- Training and Workshops Center, University of Technology- Iraq, Baghdad, Iraq; Control and Systems Engineering Department, University of Technology- Iraq, Baghdad, Iraq
| | - Ali Basem
- Air Conditioning Engineering Department, Faculty of Engineering, Warith Al-Anbiyaa University, Iraq
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Vieira JC, Guedes LA, Santos MR, Sanchez-Gendriz I. Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:9871. [PMID: 38139715 PMCID: PMC10747117 DOI: 10.3390/s23249871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 12/24/2023]
Abstract
Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models-without resorting to deep learning-is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.
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Affiliation(s)
- Jusciaane Chacon Vieira
- Department of Computer Engineering and Automation—DCA, Federal University of Rio Grande do Norte—UFRN, Natal 59078-900, RN, Brazil; (L.A.G.); (M.R.S.); (I.S.-G.)
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Huang H, Chen P, Wen J, Lu X, Zhang N. Multiband seizure type classification based on 3D convolution with attention mechanisms. Comput Biol Med 2023; 166:107517. [PMID: 37778214 DOI: 10.1016/j.compbiomed.2023.107517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/28/2023] [Accepted: 09/19/2023] [Indexed: 10/03/2023]
Abstract
Electroencephalogram (EEG) signal contains important information about abnormal brain activity, which has become an important basis for epilepsy diagnosis. Recently, epilepsy EEG signal classification methods mainly extract features from the perspective of a single domain, which cannot effectively utilize the spatial domain information in EEG signals. The redundant information in EEG signals will affect the learning features with the increase of convolution layer and multi-domain features, resulting in inefficient learning and a lack of distinguishing features. To tackle these issues, we propose an end-to-end 3D convolutional multiband seizure-type classification model based on attention mechanisms. Specifically, to process preprocessed electroencephalogram (EEG) data, a multilevel wavelet decomposition is applied to obtain the joint distribution information in the two-dimensional time-frequency domain across multiple frequency bands. Subsequently, this information is transformed into three-dimensional spatial data based on the electrode configuration. Discriminative joint activity features in the time, frequency, and spatial domains are then extracted by a series of parallel 3D convolutional sub-networks, where 3D channels and spatial attention mechanisms improve the ability to learn critical global and local information. A multi-layer perceptron is finally implemented to integrate the extracted features and further map them to the classification results. Experimental results on the TUSZ dataset, the world's largest publicly available seizure corpus, show that 3D-CBAMNet significantly outperforms the state-of-the-art methods, indicating effectiveness in the seizure type classification task.
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Affiliation(s)
- Hui Huang
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| | - Peiyu Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Jianfeng Wen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Xuzhe Lu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Nan Zhang
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
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Joshi RK, M. VK, Agrawal M, Rao A, Mohan L, Jayachandra M, Pandya HJ. Spatiotemporal analysis of interictal EEG for automated seizure detection and classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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