HRV-Based Seizure Classification From Wearable ECG Signals
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Solution Overview
Problem
Existing diagnostic methods for distinguishing epileptic seizures (ES) from functional or dissociative seizures (FDS) are burdensome, costly, and often yield inconclusive results, particularly in settings where access to specialized healthcare facilities is limited, leading to misdiagnosis and inappropriate treatments.
Innovation Solution
A system utilizing heart rate variability (HRV)-based models through electrocardiogram (ECG) recordings and analytical algorithms to distinguish between ES and FDS, leveraging wearable devices for out-of-hospital diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video/EEG/ECG monitoring in epilepsy monitoring units is used to distinguish ES from FDS, then diagnostic accuracy is improved, but accessibility and cost are worsened
Solution Approach 1:
The patent extracts the essential diagnostic function from the complex EMU environment by isolating HRV analysis as the core diagnostic tool. By focusing solely on heart rate variability metrics from standard ECG recordings, the system removes the need for specialized EMU facilities, video monitoring, and expert interpretation, making the diagnostic capability accessible through widely available wearable ECG devices
Solution Approach 2:
The patent creates a simplified diagnostic model that copies the essential diagnostic information from the complex EMU setting. By using HRV metrics as a surrogate marker for seizure type classification, the system replicates the diagnostic function of full EMU monitoring through a much simpler, more accessible approach using standard ECG data
2Measurement precision
If comprehensive video/EEG/ECG monitoring is used in EMU, then diagnostic capability is improved, but cost and burden are worsened
Solution Approach 1:
The patent replaces expensive, resource-intensive EMU monitoring with a low-cost alternative using standard wearable ECG devices. The HRV analysis approach uses inexpensive computational methods applied to readily available ECG data, eliminating the need for costly specialized facilities and extended hospital admissions while maintaining diagnostic capability
3Measurement precision
If EMU admission is used for diagnosis, then diagnostic thoroughness is improved, but time efficiency is worsened
Solution Approach 1:
The patent enables preliminary diagnostic assessment using HRV analysis from routine ECG recordings before EMU admission is considered. By analyzing HRV metrics from standard monitoring, the system can provide preliminary seizure type classification that may eliminate the need for time-consuming EMU admissions, allowing for faster treatment decisions and reducing patient burden
Data Source
AI summary
A system and method for distinguishing the type of seizures in a human patient, such as an epileptic seizure (ES), or a functional or dissociative seizure (FDS). The system and method use a diagnostic analytical platform that gets heart rate variability (HRV) analytical metrics from a ECG and uses an analytical diagnostic algorithm to determine if an ES or FDS has occurred in the patient. The diagnostic analytical platform can create a model for distinguishing that a predetermined type of seizure has occurred from the HRV analytical metrics.