基于深度学习的儿童发作间期癫痫样放电检测研究

doi:10.3969/j.issn.0258-8021.2025.05.008

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (5500 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Interictal epileptiform discharges (IED) are crucial for epilepsy diagnosis, but the non-stationarity of EEG signals make IED detection complicated. Traditional manual interpretation of EEG is subjective and time-consuming. With the development of machine learning and deep learning, computer-assisted models have been proposed in the field of IED detection. CNN-based IED detection methods have achieved promising results, but CNN is less effective in capturing long-range dependencies within time series data. Transformer is good at processing sequential data by adopting a self-attention mechanism, which enables it to capture long-term dependencies. This study proposes a novel Transformer-based IED detection method, which first uses simple convolution to extract local features of IEDs and then employs a Transformer to further model the long-range dependencies of these features. To address the scarcity of IED data, a new Transformer-based generative adversarial network (GAN) is also designed to augment the IED data. Based on an analysis of 11 pediatric epilepsy patients, the new method achieved an average accuracy of 96.11%, average recall of 97.08%, and average precision of 93.85% in the binary classification task on the augmented dataset. In the multi-class classification task, the average recall reached 93.47%, and the average precision was 93.84%. This study provides valuable reference for the future application of deep learning in automatic IED detection.

Key words： interictal epileptiform discharges (IED) detection electroencephalogram (EEG) Transformer generative adversarial network (GAN) deep learning

Received: 28 June 2024

PACS:

R318

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Rao Wenhao
	Chen Duo
	Zhang Ling
	Jiang Jun

Cite this article:

Rao Wenhao,Chen Duo,Zhang Ling, et al. Research on Detection of Interictal Epileptiform Discharges in Children Based on Deep Learning[J]. Chinese Journal of Biomedical Engineering, 2025, 44(5): 579-590.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.05.008 OR http://cjbme.csbme.org/EN/Y2025/V44/I5/579

[1] Oluigbo OC, Salma A, Rezai RA, et al.Deep brain stimulation for neurological disorders[J].IEEE Reviews in Biomedical Engineering, 2012, 5: 88-99.
[2] Supriya S, Siuly S, Wang H, et al.Epilepsy detection from EEG using complex network techniques: a review[J].IEEE Reviews in Biomedical Engineering, 2023, 16: 292-306.
[3] 梁思颖,李灵晓,蒋赛赛,等.血清8-OHdG MDA及GSH-Px水平检测在小儿癫痫发作诊断中的应用价值[J].中国妇幼保健,2024,39(8):1458-1461.
[4] Ramele R, Villar AJ, Santos MJ.EEG waveform analysis of P300 ERP with applications to brain computer interfaces[J].Brain Sciences, 2018, 8(11): 199.
[5] Acharya UR, Sree SV, Swapna G, et al.Automated EEG analysis of epilepsy: a review[J].Knowledge-Based Systems, 2013, 45: 147-165.
[6] Chen Zixu, Lu Guoliang, Xie Zhaohong, et al.A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis[J].IEEE Access, 2020, 8: 20080-20092.
[7] Thomas J, Jin J, Thangavel P, et al.Automated detection of interictal epileptiform discharges from scalp electroencephalograms by convolutional neural networks[J].International Journal of Neural Systems, 2020, 30(11): 2050030.
[8] De Oliveira PG, Queiroz C, Da Silva FL.Spike detection based on a pattern recognition approach using a microcomputer[J].Electroencephalography and Clinical Neurophysiology, 1983, 56(1): 97-103.
[9] Gabor AJ, Seyal M.Automated interictal EEG spike detection using artificial neural networks[J].Electroencephalography and Clinical Neurophysiology, 1992, 83(5): 271-280.
[10] Lodder SS, Askamp J, Van Putten M, et al.Interictal spike detection using a database of smart templates[J].Clinical Neurophysiology.2013, 124(12): 2328-2335.
[11] Thomas J, Jin J, Dauwels J, et al.Automated epileptiform spike detection via affinity propagation-based template matching[C]//Proceedings of the 2017 39th annual international conference of the IEEE Engineering in Medicine and Biology Society.Piscataway: IEEE, 2017: 3057-3060.
[12] Bagheri E, Jin J, Dauwels J, et al.Classifier cascade to aid in detection of epileptiform transients in interictal EEG[C]//Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing.Calgary: IEEE, 2018: 970-974.
[13] Chan HL, Ouyang Y, Huang PJ, et al.Deep neural networks for the detection of temporal-lobe epileptiform discharges from scalp electroencephalograms[J].Biomedical Signal Processing and Control, 2023, 84: 104698.
[14] Tjepkema-Cloostermans MC, De Carvalho RC, Van Putten MJ.Deep learning for detection of focal epileptiform discharges from scalp EEG recordings[J].Clinical Neurophysiology, 2018, 129(10): 2191-2196.
[15] Abdi-Sargezeh B, Shirani S, Sanei S, et al.A review of signal processing and machine learning techniques for interictal epileptiform discharge detection[J].Computers in Biology and Medicine, 2023, 168: 107782.
[16] Thuwajit P, Rangpong P, Sawangjai P, et al.EEGWaveNet: multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection[J].IEEE Transactions on Industrial Informatics, 2022, 18(8): 5547-5557.
[17] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al.Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE, 2016: 770-778.
[18] Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al.Deep learning with convolutional neural networks for EEG decoding and visualization[J].Human Brain Mapping, 2017, 38(11): 5391-5420.
[19] Lawhern VJ, Solon AJ, Waytowich NR, et al.EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces[J].Journal of Neural Engineering, 2018, 15(5): 056013.
[20] Lourenço C, Tjepkema-Cloostermans MC, Teixeira LF, et al.Deep learning for interictal epileptiform discharge detection from scalp EEG recordings[C]//Proceedings of the 15th Mediterranean Conference on Medical and Biological Engineering and Computing-MEDICON 2019.Coimbra: Springer International Publishing, 2020: 1984-1997.
[21] Prasanth T, Thomas J, Yuvaraj R, et al.Deep learning for interictal epileptiform spike detection from scalp EEG frequency sub bands[C]//Proceeings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society.Piscataway: IEEE, 2020: 3703-3706.
[22] Jing Jin, Sun Haoqi, Kim JA, et al.Development of expert-level automated detection of epileptiform discharges during electroencephalogram interpretation[J].JAMA Neurology, 2020, 77(1): 103-108.
[23] Geng D, Alkhachroum A, Bicchi MAM, et al.Deep learning for robust detection of interictal epileptiform discharges[J].Journal of Neural Engineering, 2021, 18(5): 056015.
[24] Zhang Ling, Wang Xiaolu, Jiang Jun, et al.Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN)[J].Frontiers in Molecular Biosciences, 2023, 10: 1146606.
[25] Pascual D, Amirshahi A, Aminifar A, et al.EpilepsyGAN: synthetic epileptic brain activities with privacy preservation[J].IEEE Transactions on Biomedical Engineering, 2020, 68(8): 2435-2446.
[26] Emami H, Aliabadi MM, Dong M, et al.Spa-GAN: spatial attention GAN for image-to-image translation[J].IEEE Transactions on Multimedia, 2020, 23: 391-340.
[27] Qin Zhiwei, Liu Zhao, Zhu Ping, et al.A GAN-based image synthesis method for skin lesion classification[J].Computer Methods and Programs in Biomedicine, 2020, 195:105568.
[28] Smith KE, Smith AO.Conditional GAN for timeseries generation[EB/OL].http://arxiv.org/abs/2006.16477,2020-06-30/2024-07-09.
[29] Vaswani A, Shazeer N, Parmar N, et al.Attention is all you need[J].Advances in Neural Information Processing Systems, 2017, 30: 5998-6008.
[30] Xie Jun, Zhang Jie, Sun Jiayao, et al.A Transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 2126-2136.
[31] Song Yonghao, Jia Xueyu, Yang Lie, et al.Transformer-based spatial-temporal feature learning for EEG decoding[EB/OL].https://arxiv.org/pdf/2106.11170.pdf,2021-06-11/2024-07-09.
[32] Bagchi S, Bathula D R.EEG-ConvTransformer for single-trial EEG-based visual stimulus classification[J].Pattern Recognition, 2022, 129: 108757.
[33] Song Yonghao, Zheng Qingqing, Liu Bingchuan, et al.EEG Detector: convolutional transformer for EEG decoding and visualization[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 31: 710-719.
[34] Mao Xudong, Li Qing, Xie Haoran, et al.Least squares generative adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision.Venice: IEEE, 2017: 2794-2802.
[35] Van der Maaten L, Hinton G.Visualizing data using t-SNE[J].Journal of Machine Learning Research, 2008, 9(11): 2579-2605.
[36] Keelawat P, Thammasan N, Numao M, et al.A comparative study of window size and channel arrangement on EEG-emotion recognition using deep CNN[J].Sensors, 2021, 21(5): 1678.