Slope Entropy as a Complexity Metric for the Characterization of Electrograms in Post-Ischemic Ventricular Tachycardia

Slope Entropy (SlopEn) has been recently introduced as a robust estimator of the complexity degree in physiological time series.Post-ischemic ventricular tachycardia (VT) patients exhibit highly fragmented and unstructured intracardiac electrograms (EGMs), called abnormal ventricular potentials (AVPs), referable to arrhythmogenic areas.This study aims to characterize VT EGMs, specifically physiological EGMs and AVPs, in terms of SlopEn metric, to support AVPs identification.A dataset of 344 EGMs, (65% AVPs, 35% physiological EGM), was used to assess the impact of each parameter of SlopEn (i.e., the embedded dimension m, the vertical increment threshold , and the proximityto-zero difference threshold ) in terms of significant differences, by non-parametric statistical tests, in SlopEn values between physiological EGMs and AVPs.This analysis allowed us to identify a good set of SlopEn parameters (i.e., m=3, =0.0003, and =0.0055) able to achieve a statistically significant difference (p<0.0001).According to these results, SlopEn effectively characterizes physiological and pathological EGMs in post-ischemic VT, and can be considered to support the identification of arrhythmogenic areas in VT electrophysiological studies.

Authors

Publication Details

Journal
Computing in cardiology
Published
2024-12-01
DOI
https://doi.org/10.22489/cinc.2024.421
Primary Topic
Cardiac electrophysiology and arrhythmias
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Slope Entropy as a Complexity Metric for the Characterization of Electrograms in Post-Ischemic Ventricular Tachycardia

Danilo Pani, Marco Orrù, Graziana Viola, Nicla Mandas et al.
Computing in cardiology
Cardiac electrophysiology and arrhythmias
article

Slope Entropy as a Complexity Metric for the Characterization of Electrograms in Post-Ischemic Ventricular Tachycardia

Danilo Pani, Marco Orrù, Graziana Viola, Nicla Mandas, Giulia Baldazzi
article en

Abstract

Slope Entropy (SlopEn) has been recently introduced as a robust estimator of the complexity degree in physiological time series.Post-ischemic ventricular tachycardia (VT) patients exhibit highly fragmented and unstructured intracardiac electrograms (EGMs), called abnormal ventricular potentials (AVPs), referable to arrhythmogenic areas.This study aims to characterize VT EGMs, specifically physiological EGMs and AVPs, in terms of SlopEn metric, to support AVPs identification.A dataset of 344 EGMs, (65% AVPs, 35% physiological EGM), was used to assess the impact of each parameter of SlopEn (i.e., the embedded dimension m, the vertical increment threshold , and the proximityto-zero difference threshold ) in terms of significant differences, by non-parametric statistical tests, in SlopEn values between physiological EGMs and AVPs.This analysis allowed us to identify a good set of SlopEn parameters (i.e., m=3, =0.0003, and =0.0055) able to achieve a statistically significant difference (p<0.0001).According to these results, SlopEn effectively characterizes physiological and pathological EGMs in post-ischemic VT, and can be considered to support the identification of arrhythmogenic areas in VT electrophysiological studies.

Computing in cardiologyVol. 51
European Commission
Openalex Percentile: Top 58%
Cardiac electrophysiology and arrhythmias
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Slope Entropy as a Complexity Metric for the Characterization of Electrograms in Post-Ischemic Ventricular Tachycardia — Danilo Pani, Marco Orrù, et al. · Computing in cardiology (2024) | TGRS Research Map | TGRS