A self-attention model for cross-subject seizure detection

Deep Clustering for Epileptic Seizure Detection

Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, which are often unpredictable and increase mortality and morbidity risks.

OBJECTIVE

The objective of this study is to address the challenges of EEG-based epileptic seizure detection by introducing a novel methodology, Deep Embedded Gaussian Mixture (DEGM).

METHODS

The DEGM method begins with a deep autoencoder (DAE) for embedding the input EEG data, followed by Singular Value Decomposition (SVD) to enhance the representational quality of the embedding while achieving dimensionality reduction. A Gaussian Mixture Model (GMM) is then employed for clustering purposes. Unlike conventional supervised machine learning and deep learning techniques, DEGM leverages deep clustering (DC) algorithms for more effective seizure detection.

RESULTS

Empirical results from two real-world epileptic datasets demonstrate the notable performance of DEGM. The method's effectiveness is particularly remarkable given the substantial size of the datasets, showcasing its ability to handle large-scale EEG data efficiently.

CONCLUSION

In conclusion, the DEGM methodology provides a novel and effective approach for EEG-based epileptic seizure detection, addressing key challenges such as data variability and artifact contamination.

SIGNIFICANCE

By combining deep autoencoders, SVD, and GMM, DEGM achieves superior clustering performance compared to existing methods, representing a significant advancement in biomedical research and clinical applications for epilepsy. Its robust performance on large datasets underscores its potential for improving seizure detection accuracy, ultimately contributing to better patient outcomes.

Application of deconvolutional networks for feature interpretability in epilepsy detection

Introduction

Scalp electroencephalography (EEG) is commonly used to assist in epilepsy detection. Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model's interpretability but has not been applied in seizure detection.

Methods

To address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. The epilepsy detection performance for patient-independent was evaluated on the CHB-MIT dataset. Then, the SE module was removed from the model and integrated the model with Inception, ResNet, and CBAM modules separately.

Results

The method showed superior advancement, stability, and reliability compared to the other three methods. The method demonstrated a G-Mean of 82.7% for sensitivity (SEN) and specificity (SPE) on the CHB-MIT dataset. In addition, The contributions of each channel to the seizure detection task have also been quantified, which led us to find that the FZ, CZ, PZ, FT9, FT10, and T8 brain regions have a more pronounced impact on epileptic seizures.

Discussion

This article presents a novel algorithm for epilepsy detection that accurately identifies seizures in different patients and enhances the model's interpretability.

An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection

Automatic detection of epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific models and have shown promising results, which is not suitable for clinical application, especially when new patient data are used for diagnosis in EEG epileptic seizure detection (ESD). Therefore, the proposed study introduces a novel hybrid deep learning approach consisting of a one-dimensional convolutional neural network (1D CNN), a Multi-Long Short-Term Memory Network (MLSTM) with a multi-attention layer (MAT) for patient-specific, cross-patient, and patient-independent seizure detection. The 1D CNN model extracts spatial features, while the MLSTM extracts temporal features from segmented EEG data. Moreover, the MAT layer conducts feature fusion and identifies relevant patterns. Experiments conducted using the CHB-MIT EEG dataset confirm our method's superiority over other sibling and state-of-the-art methods by an average of 2% in classification accuracy, recall, specificity, and G.mean of using patient-specific, cross-patient, and patient-independent seizure detection, demonstrating a robust and effective framework in EEG ESD.

RIHANet: A Residual-based Inception with Hybrid-Attention Network for Seizure Detection using EEG signals

Increasing attention is being given to machine learning methods designed to aid clinicians in treatment selection. Therefore, this has aroused a heightened focus on the auto-detect system of epilepsy utilizing electroencephalogram(EEG) data. However, in order for the recognition model to accurately capture a wide range of features related to channel, frequency, and temporal information, it is necessary to have EEG data that is correctly represented. To tackle this challenge, we propose a Residual-based Inception with Hybrid-Attention Network(RIHANet) to achieve automatic seizure detection. Initially, by employing Empirical Mode Decomposition and Short-time Fourier Transform(EMD-STFT) for data processing, it can improve the quality of time-frequency representation of EEG. Additionally, by applying a novel Residual-based Inception to the network architecture, the detection model can learn local and global multiscale spatial-temporal features. Furthermore, the Hybrid Attention model designed is used to obtain relationships between EEG signals from multiple perspectives, including channels, sub-spaces, and global. Using four public datasets, the suggested approach outperforms the results in the most recent scholarly publications.