Atrial fibrillation (AF) may not always be triggered by a fully random process. The stable and rapid re-entrant circuits resulting in fibrillatory conduction throughout the atria can persist for minutes even hours. Ablation at the center of stable rotating waves and focal sources resulted in a high rate of acute AF termination and a better long-term recurrence-free probability. However, during mapping the atrial substrate electrograms of AF, a frequently encountered difficulty is the identification of culprit sites and the analysis of the wave propagation particularly for the electrogram signals with wide temporal and spatial disparities. Localizing AF drivers by the conventional sequential temporal-spatial mapping in the persistent AF is even harder because of its lack of specificity of complex atrial electrograms, intermittent firing, and spatial meandering. Our newly developed system with the 64 channels amplifier frontend can be compatible with different types of catheters and achieve optimal computing efficiency in real-time ultra-high density mapping of the atrial substrates by implementing a heterogeneous computation. The system can be used to interpret the complicated wave propagation and identify the substrate that maintains AF through the multi-task sparse learning, which helps to locate the true AF driver hidden beneath the highly fragmented waves. In this project, to improve the efficiency of procedure, we also propose a novel strategy of SAFE-T (Simultaneous Amplitude Frequency Electrogram Transformation) and sinus rhythm (SR)- abnormal electrogram-guided ablation. We hypothesize that the novel real-time temporal frequency analysis can be used to identify and characterize the abnormal substrates even during SR, and the removal of potential redundant conducting channels in persistent AF can assure the long-term SR maintenance. In conclusion, we aim to develop the automated electrogram analysis using real-time ultra-high density substrate mapping which allows instantaneous and objective identification of abnormal potential that accurately indicates AF driver. Our new strategy aims not only to reduce the ablation area but also to improve the acute termination rate and the recurrence-free survival of AF.
|Effective start/end date||1/08/20 → 31/07/21|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):