Development of Deep Learning Algorithms for Automatic Detection of Subtle Patterns in EEG Signals for the Diagnosis and Monitoring of Subclinical Seizure Activity

Hussein Mohammed Qasim

doi:10.51173/eetj.v3i2.48

Authors

Hussein Mohammed Qasim Biomedical Engineering Technical Department, Hakim Sabzevari University, Sabzevar, Iran

DOI:

https://doi.org/10.51173/eetj.v3i2.48

Keywords:

EEG, Subclinical Seizures, Deep Learning;, CNN, LSTM, Hybrid Architecture, Automated Detection, Biomedical Signal Processing

Abstract

Subclinical seizures are subtle neurologic events that remain largely undetectable during standard electroencephalography (EEG), and thus present critical barriers to timely diagnosis and monitoring of affected individuals. In this paper, we propose a deep learning framework for automatic detection of subtle patterns in EEG signals that are indicative of subclinical seizure activity. The available high-quality EEG data were preprocessed with band-pass and notch filters, ICA-based artifact removal, as well as splitting into fixed-sized epochs. Multiple input representations including raw signals, spectrograms, and wavelet transforms were used to broaden the feature extraction power of the model. A variety of deep learning architectures (such as CNNs, LSTM networks and hybrid CNN-LSTM models) were all designed and trained based on supervised training. The performance of the models was assessed using accuracy, sensitivity, specificity, F1-score and AUC. The hybrid CNN–LSTM model outperformed, with high sensitivity and accuracy in the detection of sub-clinical seizures across subjects. The obtained results suggest that deep learning can effectively encode complex spatiotemporal patterns observed in EEG activity although they are often overlooked by traditional methods, serving as a reliable, unbiased tool for neurological diagnostics. This work demonstrates that it may be feasible for intelligent EEG analysis systems to aid in 24/7 patient monitoring, improve clinical management decisions and interventions, not only when seizures are full-blown but also in the early intervention and treatment of subclinical seizure control.

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