Automated His Bundle Detection From Intracardiac Electrograms
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Solution Overview
Problem
The His bundle, a critical pacing site in the heart, is difficult to identify from intracardiac electrograms (IEGMs) due to its small size and faint electrical signal, making it challenging for physicians to accurately detect during electrophysiological mapping.
Innovation Solution
An automated His bundle detection algorithm using a multi-electrode catheter, a defined window of interest, clustering, high-pass filtering, and cross-correlation to identify and tag His peaks on an EP map, ensuring accurate localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of His bundle from IEGM signals is used, then physician expertise and experience are required, but detection accuracy is low due to small size and faint electrical signal of the His bundle
Solution Approach 1:
The patent introduces an automated detection algorithm as an intermediary between the IEGM signals and the physician. This algorithm processes the faint electrical signals, applies filtering and cross-correlation techniques, and produces enhanced detection results that are much easier for physicians to interpret, thereby resolving the contradiction between detection difficulty and accuracy
Solution Approach 2:
The patent replaces the manual visual inspection mechanism with an automated computational mechanism. The automated algorithm performs signal processing, filtering, and pattern recognition tasks that are difficult for human physicians to perform accurately on faint IEGM signals, thereby improving measurement precision while reducing detection difficulty
2Measurement precision
If automated detection algorithm is applied, then detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the detection process into distinct stages: signal filtering to remove noise, peak detection to identify candidate His bundle signals, cross-correlation to verify and confirm His bundle identification, and visualization to present results. This segmentation manages processing complexity by breaking down the complex automated detection task into manageable, sequential steps while maintaining high detection accuracy
Data Source
AI summary
A method includes receiving intracardiac electrogram (IEGM) signals measured at a plurality of locations in a region of a heart that contains a His bundle of the heart. The IEGM signals are processed to find respective local activation time (LAT) values. A cluster of the locations is identified at which peaks associated with the LAT values occur later than a defined time. Respective time differences are calculated between times of occurrence of the associated peaks and a reference time. The time differences are compared to a threshold value to retain locations for which the time differences are below the threshold value. The respective IEGM signals are filtered to identify respective high-frequency peaks in the IEGM signals. The high-frequency peaks are cross-corelated to identify a subset of the locations whose high-frequency peaks meet a predefined cross-correlation level. The high-frequency peaks are tagged as His peaks and indicated on a cardiac map.