发表机构
University of Melbourne（墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用腕戴式加速度计和基于时间序列ResNet的深度学习模型，实现非侵入性癫痫发作检测，在79名患者中达到95.65%的灵敏度和低误报率，适用于长期动态监测。
AI 中文摘要
癫痫发作的监测和检测对于降低与癫痫发作相关的发病率和死亡率至关重要。目前的癫痫护理通常涉及昂贵的视频脑电图（VEEG）监测，需要专业知识，且仅限于医院环境，并具有侵入性。另一方面，癫痫日记由于漏报而存在不可靠性，导致错误的治疗决策。可穿戴非侵入性癫痫发作检测可能为长期动态监测提供更可耐受和可行的解决方案。本研究探索了一种可穿戴远程监测系统，利用单个腕戴式加速度计设备，能够检测多种类型的癫痫发作，包括较短持续时间的事件。我们招募了79名接受视频脑电图监测的患者佩戴加速度计设备并收集数据。同步的VEEG记录由经委员会认证的癫痫专科医生审查，以产生注释，包括癫痫发作的起始、结束和发作类型。利用这些数据，我们构建了一个基于时间序列ResNet架构的深度神经网络，能够区分癫痫发作和非癫痫发作事件。我们提出的方法在评估期间（总记录时长为5576小时）实现了95.65%的癫痫发作检测灵敏度和0.15/24小时的总体误报率。此外，在经历46次惊厥性癫痫发作的20名患者中，受试者工作特征曲线下面积（AUC-ROC）为0.98，精确率-召回率曲线下面积（AUC-PRC）为0.67。这些有前景的结果表明，所提出的癫痫发作检测系统可有效用于长期动态癫痫发作监测。未来步骤包括在更大的数据集中验证我们的发现，并评估对其他癫痫发作类型的检测效用。
英文摘要
Seizure monitoring and detection are crucial for reducing the morbidity and mortality associated with seizures. Current epilepsy care, often involving expensive video-electroencephalography (VEEG) monitoring, requires specialized expertise and is limited to in-hospital settings, and intrusive in nature. Seizure diaries, on the other hand, suffer from unreliability due to under-reporting, leading to incorrect therapeutic decisions. Wearable non-invasive seizure detection may offer a more tolerable and feasible solution for long-term ambulatory monitoring. This study explores a wearable remote monitoring system utilizing a single wrist-worn accelerometer device and capable of detecting multiple types of seizures, including shorter duration events. We enrolled 79 patients under video-electroencephalography monitoring to wear accelerometer devices and collect data. Concurrent VEEG recordings were reviewed by board-certified epileptologists to produce annotations, including seizure onset, offset, and seizure type. Using this data, we constructed a deep neural network based on the time-series ResNet architecture, which could discriminate among seizure and non-seizure events. Our proposed approach achieved a seizure detection sensitivity of 95.65% and an overall false alarm rate of 0.15/24 hours during the evaluation, which spanned 5576 hours of total recording. Additionally, it resulted in an area under the receiver operating characteristic curve (AUC-ROC) of 0.98 and an area under the precision-re call curve (AUC-PRC) of 0.67 when averaged over 20 patients who experienced 46 convulsive seizures. These promising results suggest that the proposed seizure detection system can be effectively used for long-term ambulatory seizure monitoring. Future steps include validating our findings in larger datasets and assessing the utility of detection for additional seizure types.
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