An Ultra-Low Latency and High-Precision Stacked-CNN Model for Epileptic Seizure Prediction

Yishan Wu; Shiyue Su; Daolin Cui

doi:10.54097/6nch8y90

Authors

Yishan Wu
Shiyue Su
Daolin Cui

DOI:

https://doi.org/10.54097/6nch8y90

Keywords:

Ensemble Learning, Stacked-CNN Classifier, Transformer-based Features, Multi-feature Fusion.

Abstract

Epilepsy, a prevalent and intricate chronic neurological disorder, poses significant challenges in clinical diagnosis and treatment, especially in promptly identifying pre-seizure periods. To comprehensively address the challenges of computational efficiency, storage cost, and real-time performance in multi-channel EEG analysis, this paper proposes a novel multimodal feature extraction framework specifically designed for single-channel electroencephalogram (EEG) signal modeling. The framework integrates traditional analytical techniques which include time and frequency analysis, short-time Fourier transform (STFT) and discrete wavelet transform (DWT) and addresses the inherent limitation of poor generalization in these methods. To mitigate this issue, we introduce nonlinear approaches such as local neighbor descriptive pattern (LNDP) and deep learning models like Transformer, thereby constructing a multimodal heterogeneous feature extraction architecture. Additionally, to overcome the limitation of long training times commonly observed in convolutional neural network (CNN) -based methods, we incorporate efficient models such as Gradient Boosting Decision Trees (GBDT) and propose a stacked convolutional neural network model, significantly improving prediction efficiency. To validate the generalization capability of the model, we transfer the trained system to the Children’s Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) database. The results demonstrate that the proposed model achieves the testing accuracy of 95% with an inference time of 4 ms. Moreover, in transfer learning scenarios, the model attains a loss of approximately 7.56e-9 and processes 1128 data in merely 130 ms, which is approximately one twentieth of the time required by a multi-scale CNN model. These results highlight the model's potential to improve patients with epilepsy outcomes through timely and accurate seizure prediction.

Downloads

Download data is not yet available.

References

[1] Thijs R D, Surges R, O’Brien T J, et al. Epilepsy in adults [J]. The Lancet, 2019, 393 (10172): 689-701.

[2] Arnold S T, Dodson W E. Epilepsy in children [J]. Bailliere’s Clinical Neurology, 1996, 5 (4): 783-802.

[3] Song Y, Zhang J. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine [J]. Journal of Neuroscience Methods, 2016, 257: 45-54.

[4] Bou Assi E, Nguyen D K, Rihana S, et al. Towards accurate prediction of epileptic seizures: A review [J]. Biomedical Signal Processing and Control, 2017, 34: 144-157.

[5] Abou Jaoude M, Jing J, Sun H, et al. Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning [J]. Clinical Neurophysiology, 2020, 131 (1): 133-141.

[6] Cheng C, Liu Y, You B, et al. Multilevel Feature Learning Method for Accurate Interictal Epileptiform Spike Detection [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 2506-2516.

[7] Daoud H, Bayoumi M A. Efficient Epileptic Seizure Prediction Based on Deep Learning [J]. IEEE Transactions on Biomedical Circuits and Systems, 2019, 13 (5): 804-813.

[8] Yin X, Fang W, Liu Z, et al. A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection [J]. Scientific Reports, 2024, 14 (1): 5111.

[9] Cao J, Zhu J, Hu W, et al. Epileptic Signal Classification with Deep EEG Features by Stacked CNNs [J]. IEEE Transactions on Cognitive and Developmental Systems, 2020, 12 (4): 709-722.

[10] Siddiqui M.K., Morales-Menendez R., Huang X, et al. A review of epileptic seizure detection using machine learning classifiers [J]. Brain Inf, 2020, 7, 5.

[11] Nhu D, Janmohamed M, Shakhatreh L, et al. Automated Interictal Epileptiform Discharge Detection from Scalp EEG Using Scalable Time-series Classification Approaches [J]. INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2023, 33 (01).

[12] Andrzejak R G, Lehnertz K, Mormann F, et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state [J]. Physical Review E, 2001, 64 (6): 061907.

[13] Shoeb A H. Application of machine learning to epileptic seizure onset detection and treatment [D]. Massachusetts Institute of Technology, 2009.

[14] Vaswani A, Shazeer N, Parmar N, et al. Attention is All you Need [C] // Advances in Neural Information Processing Systems: Systems. 2017, 30.

[15] Pattnaik S, Dash M, Sabut S K. DWT-based feature extraction and classification for motor imaginary EEG signals [C] // 2016 International Conference on Systems in Medicine and Biology (ICSMB). IEEE, 2016: 186-201.

[16] Lee J, Lee I, Kang J. Self-Attention Graph Pooling [C] // Proceedings of the 36th International Conference on Machine Learning. 2019: 3734-3743.

[17] Ullah I, Hussain M, Qazi E ul H, et al. An automated system for epilepsy detection using EEG brain signals based on deep learning approach [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2018, 107: 61-71.

[18] AkyolKemal. Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection [J]. Expert Systems with Applications, 2020, 148.

[19] Hu S, Liu J, Yang R, et al. Exploring the Applicability of Transfer Learning and Feature Engineering in Epilepsy Prediction Using Hybrid Transformer Model[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2023, 31: 1321-1332.