Electrogram Signal Analysis for Local Abnormal Ventricular Activity Detection
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Solution Overview
Problem
Current methods for detecting Local Abnormal Ventricular Activities (LAVAs) in electrogram data, particularly in Ventricular Tachycardia, often miss significant LAVAs, especially in the septum and early-to-activate regions, due to their reliance on delayed signal detection, which can be tedious and inefficient for high-density electrograms.
Innovation Solution
A method involving signal processing that transforms electrogram signals into the wavelet domain to compute a scalogram, detects peaks, and calculates LAVA probability and lateness parameters, including peak-to-peak amplitude analysis, to automatically identify and categorize LAVAs, reducing manual annotation efforts and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If substrate-based approaches target delayed signals relative to QRS-complex to detect LAVAs, then late-potentials can be identified, but significant LAVAs in septum and early-to-activate regions are missed
Solution Approach 1:
The patent segments the electrogram signal analysis into multiple time windows relative to the QRS-complex, including pre-QRS, early post-QRS, and late post-QRS periods. This allows detection of LAVAs across different activation times rather than only targeting delayed signals, thereby capturing early LAVAs in septum and other early-to-activate regions that would otherwise be missed.
2Measurement precision
If manual annotation of LAVAs is performed by EP physicians based on bipolar EGM characteristics, then LAVA identification can be achieved, but the process becomes tedious and difficult particularly for high density electrograms
Solution Approach 1:
The system implements automated LAVA detection algorithms that process bipolar EGM characteristics without requiring manual physician annotation. The algorithm automatically identifies LAVA signals by analyzing signal morphology, amplitude, and timing relative to QRS-complex, thereby eliminating the tedious manual annotation process while maintaining identification accuracy, especially for high density electrograms where manual analysis becomes impractical.
3Measurement precision
If wavelet transformation is applied to transform electrogram signal into wavelet domain, then scalogram can be computed for improved analysis, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential features from the wavelet transformation process by computing a scalogram that focuses on specific frequency bands relevant to LAVA detection. Rather than performing complete wavelet analysis across all frequencies, the system extracts and processes only the frequency components and time-localized information that are critical for identifying LAVA signals, thereby reducing computational complexity while preserving analysis precision.
Data Source
AI summary
Cardiac activity (e.g., a cardiac electrogram) is analyzed for local abnormal ventricular activity (LAVA), such as by using a LAVA detection and analysis module incorporated into an electroanatomical mapping system. The module transforms the electrogram signal into the wavelet domain to compute as scalogram; computes a one-dimensional LAVA function of the scalogram; detects one or more peaks in the LAVA function; and computes a peak-to-peak amplitude of the electrogram signal. If the peak-to-peak amplitude does not exceed a preset amplitude threshold, then the module can compute one or more of a LAVA lateness parameter for the electrogram signal using one of the one or more peaks detected in the LAVA function and a LAVA probability parameter for the electrogram signal.