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Abstract：
Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.
摘要：
深度学习（DL）正在成为一种成功的技术，用于自动检测和区分可能被错过或错误分类的自发性癫痫发作。在这里，我们提出了一种基于最佳DL模型的系统架构，用于非重叠窗口技术的二进制和多组分类，我们在TUSZ数据集上进行了测试。系统准确检测癫痫发作(87.7%Sn，91.16％Sp）并仔细区分八种发作类型（95-100％Acc）。EEG采样率从50Hz增加到250Hz，提高了模型性能：癫痫发作检测的精度提高了5％，和癫痫发作分化7%。低采样率是使用EEG数据训练可靠模型的合理解决方案。将EEG电极的数量从21个减少到8个并不影响癫痫发作的检测，但癫痫发作的分化显着恶化：98.24±0.17与85.14±3.14%召回。在检测癫痫发作时，所有电极提供同等的信息输入，但是在癫痫发作分化中，它们的信息价值各不相同。我们用可解释的ML改进了模型的可解释性。激活最大化强调了八种癫痫发作类型特有的EEG模式的存在。癫痫源的皮质投影描绘了全身性癫痫发作和局灶性癫痫发作之间的差异。可解释的ML技术证实，我们的系统将生物学上有意义的特征识别为脑电图中癫痫活动的指标。
