IEEE Trans Biomed Eng. 2019 Feb;66(2):421-432. doi: 10.1109/TBME.2018.2845865. Epub 2018 Jun 11.
Epileptic seizure detection requires specialized approaches such as video/electroencephalography monitoring. However, these approaches are restricted mainly to hospital setting and requires video/EEG analysis by experts, which makes these approaches resource- and labor-intensive. In contrast, we aim to develop a wireless remote monitoring system based on a single wrist-worn accelerometer device, which is sensitive to multiple types of convulsive seizures and is capable of detecting seizures with short duration. Simple time domain features including a new set of Poincar´e plot based features were extracted from the active movement events recorded using a wrist-worn accelerometer device. The best features were then selected using the area under the ROC curve analysis. Kernelized support vector data description (SVDD) was then used to classify non-seizure and seizure events. The proposed algorithm was evaluated on 5;576h of recordings from 79 patients and detected 40 (86:95%) of 46 convulsive seizures (generalized tonic-clonic (GTCS), psychogenic non-epileptic (PNES), and complex partial seizures (CPS)) from twenty patients with a total of 270 false alarms (1:16=24h). Furthermore, the algorithm showed a comparable performance (sensitivity 95:23% and false alarm rate 0:64=24h) with respect to existing unimodal and multi-modal methods for GTCS detection. The promising results shows the potential to build an ambulatory monitoring convulsive seizure detection system. A wearable accelerometer based seizure detection system would aid in continuous assessment of convulsive seizures in a timely and non-invasive manner.
癫痫发作检测需要专门的方法，如视频/脑电图监测。然而，这些方法主要局限于医院环境，需要专家进行视频/脑电图分析，这使得这些方法需要大量的资源和劳动力。相比之下，我们旨在开发一种基于单个腕戴式加速度计设备的无线远程监测系统，该系统对多种类型的惊厥性发作敏感，能够检测到持续时间短的发作。从腕戴式加速度计设备记录的主动运动事件中提取了简单的时域特征，包括一组新的基于庞加莱图的特征。然后使用 ROC 曲线下面积分析选择最佳特征。核支持向量数据描述 (SVDD) 然后用于对非发作和发作事件进行分类。该算法在 79 名患者的 5576 小时记录上进行了评估，从 20 名患者的总共 270 次假警报（1:16=24 小时）中检测到 40 次（86:95%）46 次惊厥性发作（全面强直阵挛性发作（GTCS）、心因性非癫痫性发作（PNES）和部分性发作（CPS））。此外，该算法在 GTCS 检测方面的性能与现有的单模态和多模态方法相当（灵敏度为 95:23%，假警报率为 0:64=24 小时）。有前途的结果表明，有可能建立一个用于监测惊厥性发作的可移动监测系统。基于可穿戴加速度计的癫痫发作检测系统将有助于以及时和非侵入性的方式对惊厥性发作进行连续评估。
