Annotation Histograms for Automated Intracardiac Signal Mapping
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Solution Overview
Problem
Conventional cardiac mapping systems face challenges in efficiently processing large volumes of intracardiac electrograms (EGMs) due to the complex nature of the data, leading to misleading maps and increased examination time, and they often require manual inspection which is impractical and error-prone.
Innovation Solution
A system and method for processing cardiac electrical signals to generate activation waveforms and annotation histograms, utilizing machine-learning techniques to enhance accuracy and automate the mapping process, including signal conditioning, catheter registration, and 3D grid reconstruction to provide a more accurate and faithful reconstruction of physiological heart activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of captured electrograms is performed, then diagnostic accuracy may be improved, but examination time and cost increase significantly
Solution Approach 1:
The patent replaces manual visual inspection with an automated computer-based system that processes electrograms through digital signal processing, feature extraction, and classification algorithms. This substitution of mechanical human inspection with automated computational methods resolves the contradiction by providing both high diagnostic accuracy and reduced examination time.
Solution Approach 2:
The system enables self-service processing where the computer automatically performs diagnostic analysis of electrograms without requiring manual intervention. The automated classification and interpretation of cardiac signals allow the system to serve its own diagnostic function, eliminating the time-consuming manual review process while maintaining accuracy.
2Productivity
If automated processing of 6,000 to 20,000 intracardiac electrograms is implemented, then examination time is reduced, but diagnostic accuracy may deteriorate due to information condensation
Solution Approach 1:
The patent segments the complex electrogram data into distinct feature components through systematic feature extraction. By dividing the raw signals into measurable characteristics and organizing them into structured datasets, the system can process large volumes of electrograms efficiently while preserving diagnostic information through organized feature representation rather than lossy condensation.
Solution Approach 2:
The system transforms raw electrogram signals into different parameter representations through feature extraction and classification. By changing the parameters from raw time-domain signals to extracted features and class probabilities, the system enables efficient automated processing while maintaining diagnostic accuracy through meaningful parameter transformation rather than simple data compression.
3Ease of manufacture
If conventional mapping techniques are used, then basic electro-anatomical depiction is achieved, but the complex nature of the data makes accurate interpretation difficult
Solution Approach 1:
The patent introduces an intermediary automated classification system between the raw electrogram data and the final diagnostic interpretation. This intermediary layer processes the complex data through feature extraction and machine learning classifiers, translating complex electrical signals into interpretable diagnostic categories and confidence scores, thereby easing the interpretation process while maintaining manufacturing simplicity.
Data Source
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AI summary
Systems and methods for facilitating processing of cardiac information based on sensed electrical signals include a processing unit configured to receive a set of electrical signals; receive an indication of a measurement location corresponding to each electrical signal of the set of electrical signals; and generate, based on at least one of an annotation waveform corresponding to each electrical signal of the set of electrical signals and a set of annotation mapping values, an annotation histogram.