SPERTL: Epileptic Seizure Prediction using EEG with ResNets and Transfer Learning
SPERTL: Epileptic Seizure Prediction using EEG with ResNets and Transfer Learning
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DOI：
10.1109/bhi56158.2022.9926767
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Umair Mohammad;Fahad Saeed
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文献类型：
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作者：
Umair Mohammad;Fahad Saeed
癫痫是一种慢性疾病，会导致反复的无端癫痫发作，许多癫痫患者要么对药物产生抵抗力，要么不适合手术治疗。因此，这些反复出现的不可预测的癫痫发作可能会对生活质量产生严重的负面影响，包括受伤风险增加、社会污名化、无法参加驾驶等基本活动，以及可能减少获得医疗保健的机会。一个预测系统，提前通知患者和照顾者潜在的即将到来的癫痫发作不仅是可取的，而且是迫切需要的。在本文中，我们通过设计和开发仅使用带有剩余神经网络(ResNets)的脑电(EEG)数据和转移学习(TL)-(SPERTL)来预测患者特定的癫痫发作(ES)模型。我们利用20名癫痫发作预测时间(SPH)为5分钟的患者的脑电数据来训练我们提出的模型，并使用验证数据绘制精确回忆曲线，以选择最佳阈值。对未知数据的测试表明，我们的模型优于最先进的方法，获得了最高的平均敏感度88.1%，特异度92.3%，准确率92.3%。我们的结果还表明，所提出的模型在保持较高的阳性预测率的同时，较不容易受到误报的影响。
Epilepsy is a chronic condition that causes repeat unprovoked seizures and many epileptics either develop resistance to medications and/or are not suitable candidates for surgical solutions. Hence, these recurring unpredictable seizures can have a severely negative impact on quality of life including an elevated risk of injury, social stigmatization, inability to take part in essential activities such as driving and possibly reduced access to healthcare. A predictive system that informs patients and caregivers about a potential upcoming seizure ahead of time is not only desirable but an urgent necessity. In this paper, we contribute by designing and developing patient-specific epileptic seizure (ES) prediction models using only electroencephalography (EEG) data with residual neural networks (ResNets) and transfer learning (TL) - (SPERTL). We train our proposed model on EEG data from 20 patients with a seizure prediction horizon (SPH) of 5 minutes and use the validation data to plot precision-recall curves for selecting the best thresholds. Testing on unseen data shows our model outperforms the state-of-the-art methods by achieving the highest average sensitivity of 88.1%, specificity of 92.3%, and accuracy of 92.3%. Our results also demonstrate the proposed model is less susceptible to false positives while maintaining a high positive prediction rate.