Dynamic Time Warping Enhanced CNN-LSTM for Robust Seizure Prediction in EEG Data

Subramani, Bharath (2024) Dynamic Time Warping Enhanced CNN-LSTM for Robust Seizure Prediction in EEG Data. Masters thesis, Dublin, National College of Ireland.

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Abstract

Epilepsy is known to be one of the most frequent neurological disorders whose manifestations cause severe burden to affected patients and their families because of the strictly chronic and often unpredictable character of the disease. It is essential to detect epileptic seizures in real time to enhance patients’ safety and quality of life; nevertheless, there is a fundamental problem concerning the high non-linearity of actual EEG signals. This paper presents a new approach integrating DTW with CNN-LSTM and applies it to the problem of improved seizure prediction. DTW helps to extract temporal patterns, and the CNN-LSTM helps in feature representation and learning of sequences. To eradicate interferences in the EEG signal, the model employs a Butterworth bandpass filter, which does not eliminate all the frequencies deemed necessary. On a publicly available EEG dataset, though the proposed hybrid model check marked superior accuracy, sensitivity, and specificity over conventional approaches for mental disorder diagnosis yielding test accuracy of 95. 62%. This makes TMS capable of real-time clinical application, according to this study’s finding. Thus, the findings of the study provide theoretical framework for seizure prediction that will help in improving the potential of better management of epilepsy in future. More refinement of the model’s parameters will be done in future work and integration of patient-specific data to increase accuracy for future seizure watch and patient management.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 Aug 2025 11:27
Last Modified:	26 Aug 2025 11:27
URI:	https://norma.ncirl.ie/id/eprint/8639

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