Wearable Sensor System for Seizure Severity Assessment
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Solution Overview
Problem
Current methods for assessing and managing epilepsy, particularly in detecting the onset and severity of seizures, are inadequate in providing real-time, non-invasive, and comprehensive autonomic activity measurements, which are crucial for predicting seizure types and risks such as sudden unexpected death in epilepsy (SUDEP).
Innovation Solution
A wearable sensor system that measures electrodermal activity (EDA) and heart rate variability (HRV) before, during, and after seizures to assess sympathetic and parasympathetic activity, using processors to calculate the magnitude of post-ictal autonomic disturbances and determine seizure severity, type, and risk of SUDEP, with continuous monitoring capabilities and alert systems for caregivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seizure assessment methods are used, then the assessment process is simple, but the measurement precision and reliability of autonomic activity are insufficient
Solution Approach 1:
The patent combines multiple sensors (EDA sensors, HRV sensors, accelerometers, gyroscopes) into an integrated wearable monitoring system. This merging of multiple measurement functions into a single device enables comprehensive autonomic activity assessment while maintaining portability and ease of use, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The wearable device performs multiple functions including EDA measurement, HRV measurement, motion detection, and seizure classification. This multi-functionality allows a single device to provide comprehensive seizure assessment without requiring multiple separate devices, thereby improving measurement precision without proportionally increasing device complexity.
2Measurement precision
If invasive methods are used for seizure detection, then the measurement precision improves, but the ease of operation and patient comfort deteriorate
Solution Approach 1:
The patent replaces invasive mechanical/physical measurement methods with non-invasive physiological sensing technologies. EDA sensors measure skin conductance changes, HRV sensors measure heart rate variability, and motion sensors detect seizure-related movements. These non-invasive methods maintain adequate measurement precision while dramatically improving patient comfort and ease of operation.
Solution Approach 2:
The patent uses intermediate physiological markers (EDA, HRV, motion patterns) as mediators to indirectly assess seizure activity. Instead of directly measuring brain electrical activity through invasive means, the system measures autonomic nervous system responses and physical movements that correlate with seizure events, providing non-invasive yet accurate seizure detection.
3Reliability
If comprehensive autonomic monitoring is implemented, then the reliability of seizure assessment improves, but the loss of information and data processing complexity increase
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors EDA, HRV, and motion data, compares readings against baseline values and predefined thresholds, and provides real-time seizure detection alerts. This feedback loop enables reliable seizure assessment by systematically processing comprehensive autonomic data while filtering out normal variations through adaptive thresholding and pattern recognition.
Solution Approach 2:
The patent extracts and analyzes specific critical features from comprehensive autonomic data streams. Instead of processing all raw data, the system identifies and focuses on key indicators such as EDA surge patterns, HRV changes, and characteristic motion patterns associated with seizures. This extraction approach maintains assessment reliability while reducing data processing burden by concentrating on the most diagnostically relevant information.
Data Source
AI summary
In exemplary implementations of this invention, sensor measurements are taken before, during and after an epileptiform seizure of a human. The sensors measure electrodermal activity (EDA) and heart rate variability (HRV) of the human.The EDA and HRV measurements are used to assess sympathetic activity and parasympathetic activity, respectively. More particularly, in the case of HRV measurements, HF power is used to assess parasympathetic innervation of the heart. HF power is the power of the high frequency (e.g. 0.15 to 0.4 Hz) spectral component of the RRI signal.One or more processors analyze the sensor data to calculate the magnitude of a post-ictal autonomic disturbance. Based on that calculated magnitude, the processors assess the severity of the seizure.A wrist-worn sensor may take long-term, continuous EDA and motion measurements. The processors may analyze these measurements to detect the onset of a tonic-clonic seizure.