EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

Handa, Palak; Mathur, Monika; Goel, Nidhi

doi:10.1007/s42979-023-01958-z

Cited by 5 publications

References 72 publications

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Optimizing epileptic seizure recognition performance with feature scaling and dropout layers

Omar,

Abd El-Hafeez

2023

Neural Comput & Applic

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Epilepsy is a widespread neurological disorder characterized by recurring seizures that have a significant impact on individuals' lives. Accurately recognizing epileptic seizures is crucial for proper diagnosis and treatment. Deep learning models have shown promise in improving seizure recognition accuracy. However, optimizing their performance for this task remains challenging. This study presents a new approach to optimize epileptic seizure recognition using deep learning models. The study employed a dataset of Electroencephalography (EEG) recordings from multiple subjects and trained nine deep learning architectures with different preprocessing techniques. By combining a 1D convolutional neural network (Conv1D) with a Long Short-Term Memory (LSTM) network, we developed the Conv1D + LSTM architecture. This architecture, augmented with dropout layers, achieved an effective test accuracy of 0.993. The LSTM architecture alone achieved a slightly lower accuracy of 0.986. Additionally, the Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU) architectures performed exceptionally well, with accuracies of 0.983 and 0.984, respectively. Notably, standard scaling proved to be advantageous, significantly improving the accuracy of both BiLSTM and GRU compared to MinMax scaling. These models consistently achieved high test accuracies across different percentages of Principal Component Analysis (PCA), with the best results obtained when retaining 50% and 90% of the features. Chi-square feature selection also enhanced the classification performance of BiLSTM and GRU models. The study reveals that different deep learning architectures respond differently to feature scaling, PCA, and feature selection methods. Understanding these nuances can lead to optimized models for epileptic seizure recognition, ultimately improving patient outcomes and quality of life.

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Optimizing epileptic seizure recognition performance with feature scaling and dropout layers

Omar,

Abd El-Hafeez

2023

Neural Comput & Applic

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Joint Low-Rank Representation and Transfer Multi-View Learning Approach for Eeg Epilepsy Classification

ZANG,

JIAO,

QIN

et al. 2024

J. Mech. Med. Biol.

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Classification of epileptic electroencephalograms can provide strong technical support for early warning and monitoring of epilepsy. However, Electroencephalogram (EEG) has characteristics of specificity, multi-feature, and susceptibility to noise interference. Therefore, this paper proposes a joint low-rank representation and transfer multi-view learning (LRR-TML) approach for EEG epilepsy classification. The LRR-TML approach inherits the advantages of low-rank representation, filters redundant features and noise information, and focuses on the main feature and significant feature of EEG. The LRR-TML approach introduces the ideas of transfer learning and multi-view learning on the framework of low-rank representation, mines the linear correlation between different domains in the subspace through low-rank representation, and effectively transfers the discriminative knowledge of the source domain to the target domain. According to the principles of complementarity and consistency of views, the LRR-TML approach comprehensively describes EEG by integrating various features, maximizing the consistency between different views and minimizing the divergence between views. Experimental results on the Bonn dataset show that the LRR-TML approach achieves the best classification results among all comparison approaches.

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Harnessing Machine Learning and Deep Learning for Improved Epileptic Seizure Detection: A Review

Chaturvedi

2023

Proceedings of the 5th International Conference on Information Management &Amp; Machine Intelligence

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EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

Cited by 5 publications

References 72 publications

Optimizing epileptic seizure recognition performance with feature scaling and dropout layers

Optimizing epileptic seizure recognition performance with feature scaling and dropout layers

Joint Low-Rank Representation and Transfer Multi-View Learning Approach for Eeg Epilepsy Classification

Harnessing Machine Learning and Deep Learning for Improved Epileptic Seizure Detection: A Review

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