ECG Episode Classification Reducing False Positives
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Solution Overview
Problem
Conventional cardiac monitoring systems generate a high number of false positive alerts, leading to unnecessary strain on healthcare professionals and increased costs, and require multiple device-specific platforms for data analysis, which limits efficiency and interoperability.
Innovation Solution
A computer-implemented method and system that analyzes electrocardiographic episodes using machine learning algorithms to distinguish true positive episodes of abnormal heart rhythms from false positives by segmenting ECG signals, calculating features, and providing input to a machine learning algorithm to classify episodes, thereby reducing false positives and providing a unified platform for data analysis across different cardiac devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cardiac monitors output alerts for all abnormal rhythmic episodes, then sensitivity to detect abnormal rhythms is improved, but the number of false positive alerts increases significantly
Solution Approach 1:
The patent introduces an intermediary machine learning classification system between the cardiac monitor's initial detection and the final alert output. This intermediary layer analyzes multiple features (morphology, timing, rate) and applies trained algorithms to distinguish true abnormal rhythms from false positives, thereby maintaining high detection sensitivity while significantly reducing false positive alerts
Solution Approach 2:
The system changes multiple parameters simultaneously: it analyzes morphological parameters (wave shapes), temporal parameters (interval durations), and rate parameters (heart rate variations). By evaluating multiple parameters rather than relying on a single threshold, the system achieves better discrimination between true positives and false positives
2Measurement precision
If physicians manually review all alerts to evaluate episodes, then diagnostic accuracy is maintained, but physician workload and healthcare costs increase
Solution Approach 1:
The system implements self-service by enabling automated machine learning classification to perform the initial filtering and prioritization of episodes. The algorithm independently evaluates episodes based on trained criteria, automatically distinguishing likely true positives from false positives, thereby reducing the burden on physicians while maintaining diagnostic accuracy through targeted human review of only the most suspicious cases
3Adaptability or versatility
If multiple device-specific platforms are used for different cardiac devices, then device compatibility is achieved, but system complexity and training requirements increase
Solution Approach 1:
The patent implements a universal machine learning classification system that can process and analyze episodes from multiple different cardiac device manufacturers. The system is designed with multi-functionality to handle various device formats and protocols through a single unified platform, eliminating the need for separate device-specific analysis tools and reducing overall system complexity
4Loss of information
If conventional platforms output data to electronic medical records for physician review, then information delivery is achieved, but automated analysis capability is lost
Solution Approach 1:
The system replaces the manual mechanical process of physician review with automated machine learning algorithms. The ML system automatically performs feature extraction, episode classification, and alert generation, substituting the need for manual visual inspection while preserving complete information delivery to electronic medical records for documentation purposes
Data Source
AI summary
A method and a system for analyzing electrocardiographic segments previously derived from a cardiac device so as to help to discriminate true positives episodes, including abnormal heart rhythms, from false positives episodes, including normal heart rhythms. Each episode received includes at least one segment of electrocardiographic signal, and each segment is segmented into sub-segments. Score vectors are obtained for each sub-segment to classify the episode so as to discriminate true positive episodes from false positive episodes, and the classification results, which include at least the true positive episodes, are output.