Study of Signal Complexity algorithm for AF detection launched


CathVision has announced the investigation of the Signal Complexity algorithm designed to visualise and quantify atrial fibrillation (AF) complexity parameters in patients with persistent AF. Ten patients have been treated to date at NYU Langone Health (New York, USA).

CathVision’s Cardialytics suite of artificial-intelligence (AI) powered analytics, currently in development, will provide automated analysis designed to improve ablation outcomes utilising high-quality signal data from the ECGenius system, the company said in a press release.

The NYU Langone study evaluating the Signal Complexity analytic tool is the second study initiated to assess Cardialytics algorithms. Last year the PVISION study evaluated the PVI Analyzer algorithm developed to automate the assessment of pulmonary vein isolation (PVI).

“Current AF treatment requires multiple procedural ablation steps. We are at a time when advanced signal analyses, AI algorithms, and high signal quality combined can deliver valuable visualisation allowing the opportunity to analyse progress throughout the ablation procedure,” said Larry Chinitz (NYU Langone Health, New York, USA).

“The results achieved after applying the Signal Complexity algorithm to our case data support this notion, so we are excited to see what happens in the future for the treatment of AF.”


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