Detecting epileptic seizures with wearable devices

癫痫中的突然意外死亡(SUDEP)，在癫痫发作期间或之后立即发生，由患者呼吸暂停、抽搐使昏迷的人处于窒息位置、发展异常心律或这些因素的组合引起。对其他人来说，如家庭成员或照顾者，评估和帮助癫痫发作的个人是很重要的。Neutun实验室开发了一款应用程序，可以与你的智能手机或智能手表配合使用，检测潜在的癫痫发作，并通过短信、电话和/或急救警报通知选定的个人。他们目前的探测依赖于带有加速计的可穿戴设备。通过脑电(EEG)测量大脑活动的变化来检测癫痫发作是最好的。最近，多款消费级可穿戴EEG耳机相继进入市场。Neutun实验室希望将睡眠期间使用可穿戴EEG耳机收集的EEG数据与运动学数据相结合 以改进癫痫检测，减少误报。该项目专注于脑电信号处理和分析，结合运动学数据分析，开发基于APP的实时事件检测技术，产生准确的癫痫警报。从数学上计算一个事件是癫痫发作的可能性的能力刚刚开始转变为预测癫痫发作即将发生的可能性的能力。癫痫检测和预测可穿戴设备是一个新兴领域，目前专注于向智能手机应用程序提供脉搏、体温和皮肤电导测量。如果我们能够有效和高效地结合运动学和脑电信息，我们将在技术上产生重大飞跃。

Sudden unexpected death in epilepsy (SUDEP), which occurs during or immediately after a seizure, results from the person having pauses in breathing, a convulsion placing the unconscious person in a suffocating position, developing an abnormal heart rhythm, or a combination of these. It is important for someone else, like a family member or care-giver, to assess and assist the individual having a seizure. Neutun Labs have developed an app that works with your smartphone or smartwatch to detect potential seizures and notify chosen individuals via text, phone, and/or first-responder alerts. Their current detection relies on wearables with an accelerometer. Seizures are best detected via changes in brain activity measured with electroencephalography (EEG). Recently a number of consumer grade wearable EEG headsets have entered the market. Neutun Labs would like to integrate EEG data collected with a wearable EEG headset during sleep with the kinematic data to improve seizure detection and reduce the incidence of false alarms. This project focuses on EEG signal processing and analysis combined with kinematic data analysis to develop the technology for real-time app-based event detection that produces accurate seizure alerts. The ability to mathematically calculate the likelihood that an event was a seizure is just starting to be transferred into an ability to predict the likelihood of a seizure about to occur. Epilepsy detection and prediction wearables are a nascent field that is currently focused on pulse, temperature, and skin conductance measures feeding into a smartphone app. If we can effectively and efficiently combine kinematic and EEG information, we will produce a significant leap in technology.