Cardiac Episode Classification Algorithm Using Sinus Template Matching
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Solution Overview
Problem
Manual review of cardiac episode data by clinicians is time-consuming and challenging due to increased memory capacity and diagnostic complexity in implantable medical devices, leading to reduced quality of patient management.
Innovation Solution
An algorithm using a sinus template and template matching to classify cardiac episodes by comparing ventricular EGM signals, incorporating predetermined thresholds for R-R intervals and P-R intervals, and utilizing both near-field and far-field EGM channels for robust classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated algorithms are used for post-processing cardiac episodes, then the time required for review is reduced and productivity increases, but the complexity of the classification system increases
Solution Approach 1:
The classification algorithm is divided into multiple independent modules: rhythm detection module, morphology analysis module, template matching module, and classification decision module. Each module handles a specific aspect of episode analysis, making the overall complex system manageable and maintainable while achieving high automation.
Solution Approach 2:
The system performs preliminary rhythm classification and morphology assessment before final episode classification. By pre-processing and pre-categorizing episode characteristics using template matching and interval analysis, the system reduces the computational burden on the final classification stage, improving overall efficiency.
2Measurement precision
If manual review of episodes is performed, then classification accuracy can be maintained, but the time required for review increases significantly
Solution Approach 1:
The classification algorithm incorporates feedback mechanisms where classification results are continuously refined based on comparison with established templates and morphological patterns. The system provides confidence scores for each classification, allowing clinicians to review only uncertain cases, thereby maintaining high accuracy while reducing overall review time.
Solution Approach 2:
The patent replaces manual visual inspection and cognitive analysis with automated signal processing algorithms, template matching, and morphological analysis. This substitution of mechanical/computational methods for human manual review maintains classification accuracy while dramatically reducing the time required.
3Reliability
If more episode data is stored in the IMD, then diagnostic capability improves, but the time required to review the data increases
Solution Approach 1:
The algorithm extracts and analyzes only the most diagnostically relevant features from stored episode data, such as P-R intervals, R-R intervals, and ventricular EGM morphology. By selectively extracting key parameters rather than reviewing all raw data, the system maintains diagnostic capability while significantly reducing review time.
Solution Approach 2:
The system performs partial analysis of episode data by focusing on critical morphological features and intervals that are most indicative of arrhythmia type. Rather than comprehensively analyzing every aspect of the stored data, the algorithm applies targeted analysis to the most diagnostic elements, achieving effective diagnosis with reduced processing time.
Data Source
AI summary
The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data collected by an implantable medical device. The episode classification algorithm may classify may include a sinus template and a comparison of the electrogram signal to the sinus template. Possible classifications of the cardiac episode may include, for example, unknown, inappropriate, appropriate, supraventricular tachycardia, ventricular tachycardia, ventricular fibrillation or ventricular over-sensing.