Kusmakar Shitanshu, Karmakar Chandan K, Yan Bernard, O'Brien Terence J, Muthuganapathy Ramanathan, Palaniswami Marimuthu
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4566-4569. doi: 10.1109/EMBC.2017.8037872.
Epileptic seizures are characterized by the excessive and abrupt electrical discharge in the brain. This asynchronous firing of neurons causes unprovoked convulsions which can be a cause of sudden unexpected death in epilepsy (SUDEP). Remote monitoring of epileptic patients can help prevent SUDEP. Systems based on wearable accelerometer sensors have shown to be effective in ambulatory monitoring of epileptic patients. However, these systems have a trade-off between seizure duration and the false alarm rate (FAR). The FAR of the system decreases as we increase the seizure duration. Further, multiple sensors are used in conjugation to improve the overall performance of the detection system. In this study, we propose a system based on single wrist-worn accelerometer sensor capable of detecting seizures with short duration (≥ 10s). Seizure detection was performed by employing machine learning approach such as kernelized support vector data description (SVDD). The proposed approach is validated on data collected from 12 patients, corresponding to approximately 966h of recording under video-telemetry unit. The algorithm resulted in a seizure detection sensitivity of 95.23% with a mean FAR of 0.72=24h.
癫痫发作的特征是大脑中出现过度且突然的放电。神经元的这种异步放电会引发无端抽搐，这可能是癫痫患者突然意外死亡（SUDEP）的一个原因。对癫痫患者进行远程监测有助于预防SUDEP。基于可穿戴加速度计传感器的系统已被证明在对癫痫患者的动态监测中是有效的。然而，这些系统在癫痫发作持续时间和误报率（FAR）之间存在权衡。随着癫痫发作持续时间的增加，系统的误报率会降低。此外，还结合使用多个传感器来提高检测系统的整体性能。在本研究中，我们提出了一种基于单腕部佩戴加速度计传感器的系统，该系统能够检测短持续时间（≥10秒）的癫痫发作。通过采用机器学习方法，如核支持向量数据描述（SVDD）来进行癫痫发作检测。所提出的方法在从12名患者收集的数据上得到了验证，这些数据对应于在视频遥测单元下大约966小时的记录。该算法的癫痫发作检测灵敏度为95.23%，平均误报率为0.72/24小时。
