Seizure Detection Using Work Level Excursion and Confirmation Tests
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Solution Overview
Problem
Existing technologies face challenges in accurately detecting epileptic seizures, particularly distinguishing between convulsive and non-convulsive seizures, and differentiating them from normal physiological activities.
Innovation Solution
A medical device system that measures work levels by analyzing arterio-venous differences in oxygen saturation, pressure, or kinetic activity to detect extreme work levels exceeding thresholds, and performs confirmation tests to identify epileptic convulsive seizures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seizure detection methods are used, then detection capability is provided, but accuracy in distinguishing convulsive from non-convulsive seizures is insufficient
Solution Approach 1:
The patent segments the seizure detection process into multiple independent analysis components: work level determination module, activity level determination module, incommensurateness analysis module, and confirmation test module. Each module processes specific physiological parameters independently, then integrates results to improve detection accuracy without requiring a single complex detection system.
Solution Approach 2:
The patent introduces a new dimension of analysis by comparing work level (metabolic demand) with activity level (observable movement) to detect incommensurateness. This dual-dimensional approach adds a metabolic work level dimension to the traditional movement-based detection, enabling differentiation between convulsive and non-convulsive seizures through the mismatch between physiological work and observable activity.
2Measurement precision
If work level analysis is added to improve seizure detection, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent makes the physiological sensor system multi-functional by having it simultaneously measure both activity level (for traditional seizure detection) and work level (for metabolic analysis). The same sensor infrastructure serves dual purposes, reducing the need for separate dedicated sensors and minimizing overall device complexity while enabling enhanced detection capabilities.
Solution Approach 2:
The patent introduces an intermediary computational layer that processes physiological data to derive both activity level and work level metrics. This intermediary processing layer integrates multiple physiological parameters and compares them to detect incommensurateness, serving as a mediator between raw sensor data and seizure detection decisions, thereby improving accuracy without proportionally increasing hardware complexity.
3Reliability
If confirmation tests are implemented, then false positive reduction is achieved, but detection time increases
Solution Approach 1:
The patent performs preliminary analysis by continuously monitoring and comparing work level with activity level to detect incommensurateness before a full seizure confirmation is required. This preliminary detection of metabolic-activity mismatch triggers targeted confirmation tests only when suspicious patterns are identified, reducing unnecessary confirmation tests and minimizing detection time while maintaining high reliability.
Solution Approach 2:
The patent applies partial confirmation testing rather than requiring complete traditional confirmation procedures for all detected events. When work level and activity level show significant incommensurateness, the system performs a reduced confirmation protocol that is sufficient to rule out false positives without the full time investment of traditional comprehensive confirmation tests, thereby balancing reliability with reduced detection time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively distinguishes between convulsive and non-convulsive seizures by identifying extreme work levels and provides timely warnings or therapies, enhancing seizure detection accuracy.
Implementation Method 1
determining a work level of the patient's body or a part thereof, based on arterio-venous differences in oxygen saturation, pressure, or kinetic activity
Data Source
AI summary
We report a method of determining an occurrence of an epileptic convulsive seizure in a patient, comprising: receiving body data from a patient during a first time period, determining a work level relating to said first time period at least based partially upon said body data; determining whether said work level exceeds an extreme work level threshold; performing a responsive action, in response to a determination that said work level exceeds said extreme work level threshold. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.