Deep learning approaches for diagnosing seizure based on EEG signal analysis

TL;DR

This paper introduces a new deep learning method for detecting seizures from EEG signals, which improves accuracy and adapts better to individual patient data.

Contribution

The novel EDTL framework combines pre-trained models with a custom CNN for improved seizure detection robustness and generalization.

Findings

The EDTL model achieved 99.23% AUC performance on seizure detection.

The framework improves adaptability to patient-specific variability and noise robustness.

Comparative evaluation showed EDTL outperformed individual models on standard datasets.

Abstract

Epilepsy is diagnosed in about 1% of the world’s population as a common brain disease. Timely prediction and detection of seizures can significantly improve the lives of epilepsy patients.

The study has garnered considerable attention over recent years, particularly in the context of advanced computational methods. However, current seizure detection methods still face several limitations, including high inter-patient variability, noisy and non-stationary EEG signals, and the limited generalization ability of single deep learning (DL) models. This paper presents an Ensemble of Deep Transfer Learning (EDTL) models for personalized seizure detection. The technique combines ResNet and EfficientNet methods along with a customized two-Dimensional Convolutional Neural Network (2DCNN) method for patient-specific seizure detection using EEG data. Raw data from the recordings of seizure patients is transformed into EEG signals. Personalized sliding windows are used to extract and store spectrograms for the patients. Patient-specific features are extracted from individual records. EEG signals are normalized for consistent scaling. Short Time Fourier Transform (STFT) is then applied for continuous window slicing over short time intervals. To address the limitations above, the proposed EDTL framework integrates general-purpose pre trained models with a domain-specific custom 2DCNN to capture complementary features. This design improves robustness against noise, enhances adaptability to patient-specific variability, and achieves better generalization compared to individual models. The transformed data is then passed on to train and optimize the models independently and later combined into EDTL.

A comparative evaluation is performed using standard evaluation metrics on two datasets, the CHB-MIT Scalp EEG Database and Turkish Epilepsy EEG Dataset. The proposed EDTL models are evaluated against the individual models on standard performance metrics, with the EDTL achieving the highest performance of 99.23% on the AUC.

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