通过概率预测实现实时癫痫发作检测的更短延迟
Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction
AI总结：
针对实时癫痫发作检测延迟较长的问题，提出一种基于概率预测的深度学习框架，通过引入交叉期软标签、多尺度STFT与3D-CNN特征提取、校正加权及累积决策规则，在CHB-MIT和SWEC-ETHZ数据集上实现显著缩短的检测延迟，较现有方法至少缩短50%。
摘要：
尽管最近的研究提出了具有良好敏感度性能的癫痫发作检测算法，但仍然存在一个挑战，即它们在实时场景中难以实现显著较短的检测延迟。在本文中，我们提出了一种新颖的深度学习框架，旨在通过概率预测缩短癫痫发作检测延迟。我们是首个将癫痫发作检测任务从传统的二分类转换为概率预测的研究，具体做法是引入来自癫痫导向脑电（EEG）记录的交叉期，并提出一种对交叉期样本使用软标签的标记规则。此外，我们提出了一种新颖的基于多尺度短时傅里叶变换（STFT）的特征提取方法，并结合3D-CNN架构，以准确捕获样本的预测概率。同时，我们还提出了校正加权策略以增强预测概率，以及累积决策规则以实现显著更短的检测延迟。我们在两个普遍使用的数据集——CHB-MIT头皮EEG数据集和SWEC-ETHZ颅内EEG数据集上，以患者特异性的留一发作交叉验证方案实现了所提出的框架。最终，所提出的算法在CHB-MIT数据集上成功在交叉期检测了99次发作中的94次，EEG发作后检测到100%的发作，交叉样本平均校正预测发作概率（RPIP）误差为14.84%，检测延迟为2.3秒，误检率（FDR）为0.08/h。同时，在SWEC-ETHZ数据集上，在交叉期检测了89次发作中的84次，EEG发作后检测到100%的发作，RPIP误差为16.17%，检测延迟为4.7秒，FDR为0.08/h。所获得的检测延迟比先前研究中报告的最先进结果至少短50%。
英文摘要：
Although recent studies have proposed seizure detection algorithms with good sensitivity performance, there is a remained challenge that they were hard to achieve significantly short detection latency in real-time scenarios. In this manuscript, we propose a novel deep learning framework intended for shortening epileptic seizure detection latency via probabilistic prediction. We are the first to convert the seizure detection task from traditional binary classification to probabilistic prediction by introducing a crossing period from seizure-oriented EEG recording and proposing a labeling rule using soft-label for crossing period samples. And, a novel multiscale STFT-based feature extraction method combined with 3D-CNN architecture is proposed to accurately capture predictive probabilities of samples. Furthermore, we also propose rectified weighting strategy to enhance predictive probabilities, and accumulative decision-making rule to achieve significantly shorter detection latency. We implement the proposed framework on two prevalent datasets -- CHB-MIT scalp EEG dataset and SWEC-ETHZ intracranial EEG dataset in patient-specific leave-one-seizure-out cross-validation scheme. Eventually, the proposed algorithm successfully detected 94 out of 99 seizures during crossing period and 100% seizures detected after EEG onset, averaged 14.84% rectified predictive ictal probability (RPIP) errors of crossing samples, 2.3 s detection latency, 0.08/h false detection rate (FDR) on CHB-MIT dataset. Meanwhile, 84 out of 89 detected seizures during crossing period, 100% detected seizures after EEG onset, 16.17% RPIP errors, 4.7 s detection latency, and 0.08/h FDR are achieved on SWEC-ETHZ dataset. The obtained detection latencies are at least 50% shorter than state-of-the-art results reported in previous studies.
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