Seizure Prediction System Using Physiological Threshold Alerts
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Solution Overview
Problem
Current technologies lack an effective method to predict and alert for epileptic seizures in real-time, which can lead to unforeseen and potentially life-threatening events, especially in individuals with uncontrolled epilepsy.
Innovation Solution
A computer system that monitors physiological data such as heart rate, heart rate variability, oxygen saturation, and galvanic skin response, comparing these measurements to predetermined thresholds to generate alerts for users, family members, or medical practitioners when deviations exceed set values, allowing for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time physiological monitoring is implemented, then seizure prediction capability is improved, but device complexity increases
Solution Approach 1:
The system segments the monitoring function by using separate wearable devices for different physiological parameters (heart rate, oxygen saturation, galvanic skin response) that can independently collect and transmit data, rather than requiring a single complex integrated device. Each sensor focuses on one specific physiological measurement, making the overall system more manageable while maintaining comprehensive monitoring capability.
Solution Approach 2:
The system employs a universal data processing platform that can handle multiple types of physiological data from various sources. The server or mobile application serves multiple functions: collecting data from different sensors, storing historical records, performing real-time analysis, and generating alerts. This multi-functional approach reduces the need for separate dedicated systems for each monitoring task.
2Measurement precision
If continuous real-time monitoring is performed, then seizure detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of physiological parameters rather than truly continuous monitoring. The wearable devices collect data at predetermined intervals (e.g., every few seconds or minutes) rather than continuously, which significantly reduces energy consumption while still capturing the temporal patterns necessary for seizure detection. The analysis function processes these periodic samples to identify seizure precursors.
Solution Approach 2:
The system performs preliminary analysis of physiological data patterns in advance to identify trends that precede seizures. By analyzing historical data and establishing baseline patterns, the system can detect deviations that indicate an approaching seizure, allowing for early warning without requiring constant high-power monitoring during normal conditions.
Data Source
AI summary
The present invention provides for a computer system including at least one server having software stored on a non-transient computer readable medium; where, upon execution of the software, the at least one server is at least configured to: i) receiving physiological data representative of a first physiological measurement of at least one physiological characteristic of an epileptic user; ii) receiving physiological data representative of a second physiological measurement of at least one physiological characteristic of the epileptic user; iii) comparing the first physiological measurement of the epileptic user to the second physiological measurement of the epileptic user; iv) based on the comparing, determining that a difference between the first physiological measurement of the epileptic user and the second physiological measurement is: a) higher than a predetermined threshold value, or b) smaller than the predetermined threshold value; and v) generating at least one alert; vi) transmitting the at least one alert.