Developing a mobile seizure alert device using non-invasive physiological measure

描述(申请人提供)：目前，照顾者干预是减轻癫痫发作相关不良事件的唯一方法，每天进行间接监测的选择寥寥无几。用于非临床环境的方法包括夜间床位传感器和基于加速度计的传感器，但这些传感器的灵敏度较低(例如，仅检测夜间或强直阵挛发作)和较高的误警率。RTI International和儿童国家医学中心(CNMC)的研究人员提议联合开发一种新的癫痫警报系统，用于日常监测和照顾者警报。该系统将针对癫痫发作期间自主神经系统(ANS)活动增加而产生的众所周知的生理效应，这些效应可以用不显眼、舒适的传感器来测量。将使用多传感器方法来提高对所有全面性发作和某些类型的部分性发作的检测的灵敏度和精确度，不包括缺失性发作和单纯部分性发作。总体性能目标是证明95%的时间可以识别重大的癫痫发作，而错误事件率低于10%。从16名受试者收集的初步数据表明，用多传感器方法成功检测癫痫发作的可能性很高。通过减少反应时间和消除持续观察的需要，这一系统可以通过减少与癫痫有关的伤害和死亡的数量、改善生活质量、增加患者和照顾者的独立性以及降低治疗成本，对癫痫社区产生重大和可衡量的影响。研究计划讨论了三个具体目标。目的1：利用临床数据开发并验证癫痫发作检测算法。据推测，使用ANS升级的多种生理指标，包括心率和变异性、呼吸、骨骼肌活动和出汗的变化，可以高灵敏度和精确度地检测到显著的癫痫发作。将开发并验证一种自动检测算法，该算法使用多传感器数据融合和模式识别方法来分类癫痫和非癫痫状态。目的2：研制集成样机癫痫报警装置。将开发一个完全集成的原型系统，包括一个紧凑的、可穿戴的监测设备和一个远程护理者警报单元，该系统基于稳健的方法，使用量化的临床结果、来自潜在最终用户的反馈和最先进的技术进步。目标3：在临床和住宅环境中验证原型。原型癫痫警报系统将在CNMC进行临床研究期间进行性能和总体可用性测试。45对患者-护理员将在临床测试中使用该系统来评估检测性能，然后在家中测试，以评估设备在现实环境中日常使用的有效性、实用性和舒适性。

DESCRIPTION (provided by applicant): Currently, caregiver intervention is the sole method of mitigating seizure-related adverse events, and there are few options for indirect monitoring on a daily basis. Approaches for non-clinical settings have included nocturnal bed sensors and accelerometry-based sensors, but these have suffered from low sensitivity (e.g., only detects nocturnal or tonic-clonic seizures) and high false-alarm rates. Investigators at RTI International and Children's National Medical Center (CNMC) propose a joint effort to develop a novel seizure alert system for daily monitoring and caregiver alert. This system will target the well-documented physiological effects due to elevated activity of the autonomic nervous system (ANS) during seizures that can be measured with unobtrusive, comfortable sensors. A multi-sensor approach will be used to increase sensitivity and precision for the detection of all generalized seizures and some types of partial seizures, excluding absence and simple partial. The overall performance objective is to demonstrate that significant seizures can be identified 95% of the time with a false event rate of less than 10%. Preliminary data collected from 16 subjects suggest that successful detection of seizures with a multi-sensor approach is highly probable. By decreasing response time and eliminating the need for constant observation, this system could have a substantial and measurable impact on the epilepsy community by decreasing the number of seizure-related injuries and deaths, improving quality of life, increasing independence for both patients and caregivers, and reducing the cost of treatment. The research plan discusses three specific aims. Aim 1: Develop and validate seizure detection algorithm with clinical data. It is hypothesized that significant seizures can be detected with hig sensitivity and precision using multiple physiological indicators of ANS escalation, including changes in heart rate and variability, respiration, skeletal muscle activity, and sweating. An automated detection algorithm will be developed and validated that uses a multi-sensor data fusion and pattern recognition approach to classify seizure and non-seizure states. Aim 2: Develop integrated prototype seizure alert device. A fully integrated prototype system will be developed, including a compact, wearable monitoring device and a remote caregiver alert unit based on robust methodology using quantitative clinical results, feedback from potential end users, and state-of-the-art technological advancements. Aim 3: Validate prototype in clinical and residential settings. The prototype seizure alert system will be tested for performance and overall usability during a clinical study at CNMC. Forty-five patient-caregiver pairs will use the system in clinical testing to assess detection performance, followed by at-home testing to assess device effectiveness, practicality, and comfort for daily use in a realistic environment.