Background: Artificial intelligence (AI) has shown promise in the early detection of various cardiac conditions from a standard 12-lead electrocardiogram (ECG). However, the ability of AI to identify abnormalities from single-lead recordings across a range of pathological conditions remains to be systematically investigated. This study aims to assess the performance of a convolutional neural network (CNN) using a single-lead (D1) rather than a standard 12-lead setup for accurate identification of ECG abnormalities. Methods: We designed and trained a lightweight CNN to identify 20 different cardiac abnormalities on ECGs, using data from the PTB-XL dataset. With a relatively simple architecture, the network was designed to accommodate different combinations of leads as input (<100,000 learnable parameters). We compared various lead setups such as the standard 12-lead, D1 alone, and D1 paired with an additional lead. Results: The CNN based on single-lead ECG (D1) outperformed the one based on the standard 12-lead framework [with an average percentage difference of the area under the curve (AUC) of -8.7%]. Notably, for certain diagnostic classes, there was no difference in the diagnostic AUC between the single-lead and the standard 12-lead setups. When a second lead was detected in the CNN in addition to D1, the AUC gap was further reduced to an average percentage difference of -2.8% compared with that of the standard 12-lead setup. Conclusions: A relatively lightweight CNN can predict different classes of cardiac abnormalities from D1 alone and the standard 12-lead ECG. Considering the growing availability of wearable devices capable of recording a D1-like single-lead ECG, we discuss how our findings contribute to the foundation of a large-scale screening of cardiac abnormalities.

Convolutional neural network (CNN)-enabled electrocardiogram (ECG) analysis: a comparison between standard twelve-lead and single-lead setups

Saglietto, Andrea
Co-first
;
Baccega, Daniele
Co-first
;
Esposito, Roberto;Anselmino, Matteo;Dusi, Veronica;Fiandrotti, Attilio;De Ferrari, Gaetano Maria
Last
2024-01-01

Abstract

Background: Artificial intelligence (AI) has shown promise in the early detection of various cardiac conditions from a standard 12-lead electrocardiogram (ECG). However, the ability of AI to identify abnormalities from single-lead recordings across a range of pathological conditions remains to be systematically investigated. This study aims to assess the performance of a convolutional neural network (CNN) using a single-lead (D1) rather than a standard 12-lead setup for accurate identification of ECG abnormalities. Methods: We designed and trained a lightweight CNN to identify 20 different cardiac abnormalities on ECGs, using data from the PTB-XL dataset. With a relatively simple architecture, the network was designed to accommodate different combinations of leads as input (<100,000 learnable parameters). We compared various lead setups such as the standard 12-lead, D1 alone, and D1 paired with an additional lead. Results: The CNN based on single-lead ECG (D1) outperformed the one based on the standard 12-lead framework [with an average percentage difference of the area under the curve (AUC) of -8.7%]. Notably, for certain diagnostic classes, there was no difference in the diagnostic AUC between the single-lead and the standard 12-lead setups. When a second lead was detected in the CNN in addition to D1, the AUC gap was further reduced to an average percentage difference of -2.8% compared with that of the standard 12-lead setup. Conclusions: A relatively lightweight CNN can predict different classes of cardiac abnormalities from D1 alone and the standard 12-lead ECG. Considering the growing availability of wearable devices capable of recording a D1-like single-lead ECG, we discuss how our findings contribute to the foundation of a large-scale screening of cardiac abnormalities.
2024
11
1
9
artificial intelligence; deep learning; electrocardiogram; screening; single-lead
Saglietto, Andrea; Baccega, Daniele; Esposito, Roberto; Anselmino, Matteo; Dusi, Veronica; Fiandrotti, Attilio; De Ferrari, Gaetano Maria
File in questo prodotto:
File Dimensione Formato  
Convolutional-neural-network-CNNenabled-electrocardiogram-ECG-analysis-a-comparison-between-standard-twelvelead-and-singlelead-setupsFrontiers-in-Cardiovascular-Medicine.pdf

Accesso aperto

Dimensione 4.21 MB
Formato Adobe PDF
4.21 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1967592
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact