Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 3:39 AM
Ignite Modification Date:
2025-12-25 @ 3:39 AM
NCT ID:
NCT05450302
Brief Summary:
Prior multicenter study demonstrated superiority of PURE EP to conventional mapping. This superiority was seen when comparing small, fractionated signals of interest, near-field vs far-field distinction, and general signal quality (2). Hence, investigators propose a retrospective study to assess the predictive value of high frequency algorithm (HFA) for identifying local physiologic signal under 0.3 mV distinctive of noise.
Detailed Description:
Accurate interpretation of intracardiac EGMs remains essential to the field of electrophysiology (EP). Conventional Electro-anatomical mapping systems (EAMs) have made significant improvements in EGM quality and noise reduction since their emergence, however their use of variable gain amplifiers and high/low pass filters, often provide visualization of low amplitude signals that lead to saturation artifact or necessitate signal clipping.
PURE EPTM (BioSig Technologies, Inc., Westport, CT, USA), in contrast does provide the ability to record this unaltered signal with more customizable display options. Their proprietary amplifier and high-resolution analog-digital converters with Radiofrequency (RF) suppression eliminates the need for gain switching, optimizing resolution of the full range of input signals (1).
Prior multicenter study demonstrated superiority of PURE EP to conventional mapping. This superiority was seen when comparing small, fractionated signals of interest, near-field vs far-field distinction, and general signal quality (2). Hence, investigators propose a retrospective study to assess the predictive value of high frequency algorithm (HFA) for identifying local physiologic signal under 0.3 mV distinctive of noise.
Study:
NCT05450302
Study Brief:
Protocol Section:
NCT05450302