RR-Interval Pattern Analysis for Epileptic and Psychogenic Seizures
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Solution Overview
Problem
Existing methods for detecting epileptic and psychogenic seizures rely on patient-dependent threshold values, leading to inaccurate identification and inadequate detection of focal epileptic seizures, necessitating medical surveillance.
Innovation Solution
A method that records and compares temporally sequential RR intervals to determine parameter values, using multiple parameter types and artificial neural networks to identify seizures based on characteristic time courses, reducing the reliance on patient-specific thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If patient-dependent threshold values are used for seizure detection, then the detection method is simple to implement, but the reliability of seizure identification deteriorates due to inaccurate detection and false positives
Solution Approach 1:
The patent transforms the detection approach from using fixed patient-dependent threshold values to analyzing temporal patterns and trends of parameter values over time. Instead of comparing individual parameter values against thresholds, the system evaluates changes in RR intervals, heart rate variability, and other cardiac parameters across multiple time points to identify seizure-related patterns, thereby improving reliability without requiring complex patient-specific calibration
Solution Approach 2:
The system performs preliminary recording and analysis of cardiac parameters during baseline periods to establish individual patient patterns before actual seizure detection. By pre-characterizing each patient's normal cardiac variability and rhythm patterns, the system can more accurately identify deviations indicating seizures, reducing false positives while maintaining simple implementation
2Device complexity
If traditional threshold-based methods are used, then the device complexity is low, but the detection precision deteriorates especially for focal epileptic seizures
Solution Approach 1:
The patent adds the temporal dimension to seizure detection by analyzing how cardiac parameters change over time rather than examining single-point measurements. The system records continuous RR intervals and derives multiple time-dependent parameters (heart rate variability, deceleration capacity, acceleration capacity) to create a temporal profile that reveals seizure patterns, particularly improving detection of focal seizures that produce subtle cardiac changes
Solution Approach 2:
The detection system segments the continuous cardiac signal into discrete time intervals and analyzes specific temporal features within each segment. By dividing the analysis into manageable temporal components (RR intervals, short-term variability, long-term trends), the system achieves high detection precision using relatively simple processing at each segment level
3Reliability
If medical surveillance is used for reliable seizure identification, then the detection reliability is high, but the loss of time and convenience deteriorates due to continuous hospital monitoring requirement
Solution Approach 1:
The patent enables the seizure detection system to operate autonomously using portable devices that patients can wear independently without requiring continuous medical surveillance. The system self-monitors cardiac parameters, automatically analyzes temporal patterns, and identifies seizures without physician intervention, allowing reliable detection in home or outpatient settings and eliminating the need for prolonged hospital stays
Data Source
AI summary
The invention relates to a method for detecting epileptic or psychogenic seizures, comprising the steps of:a. recording a large number of temporally sequential parameter values for a parameter type, wherein a parameter value is determined on the basis of a series of temporally sequential RR intervals, the series of temporally sequential parameter values preferably differing in that the series that served as the basis for determining the following parameter value includes, in place of the oldest RR interval of the series that served as the basis for determining the preceding parameter value (preceding series), the RR interval temporally subsequent to the most recent RR interval of the preceding series,b. comparing the time course of the parameter values with the time course of parameter values for the same parameter type that had been determined according to method step a, and the determination thereof is based on RR intervals that indicate a seizure (parameter reference values),c. identifying a seizure when the time course of the parameter values exhibits a characteristic of the time course of the parameter reference values (course characteristic) indicating a seizure.