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An ECG-based model to distinguish cardiac sarcoidosis from arrhythmogenic right ventricular cardiomyopathy

Iacono, Francesca Lo ; Slattman, Anna LU ; Chaudhry, Uzma LU ; Platonov, Pyotr P. LU and Corino, Valentina D.A. (2024) 13th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2024
Abstract

Arrhythmogenic right ventricular cardiomyopathy (ARVC) and cardiac sarcoidosis (CS) can show significant overlap in their clinical presentation. Differentiation between both is crucial for optimal patient management. Aim of this study is to develop an ECG-based machine learning model to distinguish between ARCV and CS patients. The study population included 100 patients (50 with ARCV and 50 with CS) whose 10-s 12-lead ECG were recorded. A total of 630 ECG standard measurements were extracted and submitted to two features selection steps assessing their redundancy and relevance. Sixteen features satisfied the selection criteria and were used as input of a gradient boosting classifier. A 70-30% stratified split was performed. Model... (More)

Arrhythmogenic right ventricular cardiomyopathy (ARVC) and cardiac sarcoidosis (CS) can show significant overlap in their clinical presentation. Differentiation between both is crucial for optimal patient management. Aim of this study is to develop an ECG-based machine learning model to distinguish between ARCV and CS patients. The study population included 100 patients (50 with ARCV and 50 with CS) whose 10-s 12-lead ECG were recorded. A total of 630 ECG standard measurements were extracted and submitted to two features selection steps assessing their redundancy and relevance. Sixteen features satisfied the selection criteria and were used as input of a gradient boosting classifier. A 70-30% stratified split was performed. Model performances reached an accuracy, sensitivity and specificity of 0.867 on the test set, correctly classifying 13/15 patients of both ARVC and CS group. These preliminary results show the possibility of using an ECGbased machine learning model to distinguish ARVC and CS.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
arrhythmogenic right ventricular cardiomyopathy, cardiac sarcoidosis, ECG measurements, machine learning
host publication
2024 13th Conference of the European Study Group on Cardiovascular Oscillations : Physiologically-Guided Signal Processing of Cardiovascular and Cerebrovascular Oscillations, ESGCO 2024 - Physiologically-Guided Signal Processing of Cardiovascular and Cerebrovascular Oscillations, ESGCO 2024
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
13th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2024
conference location
Zaragoza, Spain
conference dates
2024-10-23 - 2024-10-25
external identifiers
  • scopus:85213531575
ISBN
9798350392050
DOI
10.1109/ESGCO63003.2024.10767069
language
English
LU publication?
yes
id
f64b0ff2-b5b0-47c3-b0a1-e5540090b48e
date added to LUP
2025-02-26 11:31:04
date last changed
2025-04-04 14:06:45
@inproceedings{f64b0ff2-b5b0-47c3-b0a1-e5540090b48e,
  abstract     = {{<p>Arrhythmogenic right ventricular cardiomyopathy (ARVC) and cardiac sarcoidosis (CS) can show significant overlap in their clinical presentation. Differentiation between both is crucial for optimal patient management. Aim of this study is to develop an ECG-based machine learning model to distinguish between ARCV and CS patients. The study population included 100 patients (50 with ARCV and 50 with CS) whose 10-s 12-lead ECG were recorded. A total of 630 ECG standard measurements were extracted and submitted to two features selection steps assessing their redundancy and relevance. Sixteen features satisfied the selection criteria and were used as input of a gradient boosting classifier. A 70-30% stratified split was performed. Model performances reached an accuracy, sensitivity and specificity of 0.867 on the test set, correctly classifying 13/15 patients of both ARVC and CS group. These preliminary results show the possibility of using an ECGbased machine learning model to distinguish ARVC and CS.</p>}},
  author       = {{Iacono, Francesca Lo and Slattman, Anna and Chaudhry, Uzma and Platonov, Pyotr P. and Corino, Valentina D.A.}},
  booktitle    = {{2024 13th Conference of the European Study Group on Cardiovascular Oscillations : Physiologically-Guided Signal Processing of Cardiovascular and Cerebrovascular Oscillations, ESGCO 2024}},
  isbn         = {{9798350392050}},
  keywords     = {{arrhythmogenic right ventricular cardiomyopathy; cardiac sarcoidosis; ECG measurements; machine learning}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{An ECG-based model to distinguish cardiac sarcoidosis from arrhythmogenic right ventricular cardiomyopathy}},
  url          = {{http://dx.doi.org/10.1109/ESGCO63003.2024.10767069}},
  doi          = {{10.1109/ESGCO63003.2024.10767069}},
  year         = {{2024}},
}