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 3 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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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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Automatic detection of epileptic seizure using machine learning-based IANFIS-LightGBM system

Saranya,

Bharathi

2024

IFS

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A sudden increase in electrical activity in the brain is a defining feature of one of the severe neurological diseases known as epilepsy. This abnormality appears as a seizure, and identifying seizures is an important field of research. An essential technique for examining the features of neurological issues brain activities, and epileptic seizures is electroencephalography (EEG). In EEG data, analyzing epileptic irregularities visually requires a lot of time from neurologists. For accurate detection of epileptic seizures, numerous scientific techniques have been used with EEG data, and most of these techniques have produced promising results. For EEG signal classification with a high classification accuracy rate, the present research proposes an enhanced machine learning-based epileptic seizure detection model. The present research provides a hybrid Improved Adaptive Neuro-Fuzzy Inference System (IANFIS)-Light Gradient Boosting Machine (LightGBM) technique for automatically detecting and diagnosing epilepsy from EEG data. The experimental findings were supported by EEG records made available by the German University of Bonn and scalp EEG data acquired at Children’s Hospital Boston. The suggested IANFIS-LightGBM, according to the results, offers the most significant classification accuracy ratings in both situations.

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

Cited by 3 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

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

Automatic detection of epileptic seizure using machine learning-based IANFIS-LightGBM system

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