Citation

Lim, Zheng You and Pang, Ying Han and Ooi, Shih Yin and Khoh, Wee How and Hiew, Fu San (2024) MLTCN-EEG: metric learning-based temporal convolutional network for seizure EEG classification. Neural Computing and Applications. ISSN 0941-0643

Text MLTCN-EEG_ metric learning-based temporal convolutional network for seizure EEG classification.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

The incorporation of artificial intelligence (AI) into medical data processing is increasingly prevalent due to its diagnostic and analytical competencies. Numerous deep learning models have been applied to medical data analyses, including the Temporal Convolutional Network (TCN) for its competence of temporal pattern abstraction. However, TCN may be suboptimal for modeling longer-range dependencies in EEG data, lacking metric learning and data clustering mechanisms. In other words, there is neither metric learning nor a data clustering mechanism in the conventional TCN architecture. Therefore, an enhanced TCN model is devised, namely Metric Learning-based Temporal Convolutional Network for EEG signals (MLTCN-EEG), to tackle the challenges associated with classifying seizure and non-seizure EEG signals. Specifically, in the proposed model, metric learning is integrated into the TCN architecture to learn discriminative feature spaces. This enhances the extraction of complex patterns inherent in EEG data, boosting the model’s classification competency. In other words, this study formulates and integrates a specialized metric learning component within the temporal convolutional architecture, enabling the model to discern subtle variations crucial for accurate seizure identification. Despite facing the common challenge of unbalanced training data in deep neural network training, this study also explores and assesses two methods for balancing datasets: sub-sampling and Deep Convolutional Generative Adversarial Network (DCGAN). The empirical results demonstrate that MLTCN-EEG with a DCGAN-balanced dataset exhibits superior performance compared to other existing techniques, showcasing its efficacy in distinguishing seizure events with superior classification performance. The proposed model achieves an accuracy of 99.15%, precision of 100%, recall rate of 98.35%, specificity of 100% and a remarkable F1 score of 99.16% using the University of California Irvine (UCI) Seizure datasets. The proposed model also attained an accuracy of 82.66%, precision of 68.09%, recall rate of 67.95%, specificity of 88.13% and F1 score of 68.02% using Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset. These results highlight the potential impact of MLTCN-EEG and DCGAN in advancing the classification of epileptic EEG signals for improved medical diagnosis and therapy

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jan 2025 05:45
Last Modified:	03 Jan 2025 05:45
URII:	http://shdl.mmu.edu.my/id/eprint/13303

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