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

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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.