CathVision this week announced it completed patient enrollment in the CathVision ECGenius System clinical study.

The company anticipates FDA 510(k) clearance for the ECGenius System within the next two weeks, a company representative said.

The clinical evaluation study is being conducted at the University of Vermont Medical Center and will evaluate the safety and technical performance of the company’s ECGenius electrophysiology (EP) recording system and benchmark electrogram signal quality.

Denmark-based CathVision designed ECGenius to guide and enhance ablation therapy through the acquisition of low-noise, high-fidelity EP signals. It can be seamlessly integrated into modern hospital environments and has a 12-lead ECG, 128 intracardiac channels and four blood pressure channels. It is compatible with existing catheters and 3D mapping systems.

“What we’ve seen so far clinically with ECGenius are sharper, higher frequency signals compared to the systems that have been in our labs for years, and without a notch filter,” said Dr. Nathaniel Thompson, principal investigator of the study and doctor at the University of Vermont Medical Center. “The noise level with ECGenius is quite low and has allowed our team to clearly visualize very precise cardiac signals – including His – that are normally blurred or rendered completely undetectable because of the baseline noise associated with using more conventional recording systems.”

Gold standard EP recording systems usually acquire suboptimal quality electrogram signals that can prevent the accurate analysis and interpretation of those signals and severely limits the ability to correctly diagnose and devise ablation strategies for complex arrhythmias.

“EP recording systems have seen almost no meaningful evolution in decades. This lack of innovation has created an unacceptable status quo that curbs the advanced diagnosis and treatment of complex cardiac arrhythmias. ECGenius is changing that,” CEO Mads Matthiesen said in a news release. “ECGenius delivers a modern approach built on improved visualization and higher quality raw data that paves the way for more informed and accurate decision-making processes in the EP lab. This is necessary, foundational change and will become the basis for artificial intelligence-driven therapy support and machine learning software tools.”