A deep learning model for QRS delineation in organized rhythms during in-hospital cardiac arrest

Abstract

Background

Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in loss of consciousness and cessation of pulse and breathing. The electrocardiogram (ECG) stands as an essential tool extensively utilized by clinicians, during CA treatment. Within the ECG, the QRS complex reflects the depolarization of the ventricles, yielding valuable perspectives on cardiac health and potential irregularities. The delineation of QRS complexes is crucial for obtaining that information, but classical algorithms have not been tested with CA rhythms.

Objective

This research aims to introduce a new deep learning-based model for accurately delineating QRS complexes in patients experiencing organized rhythms during in-hospital CA.

Material and Methods

Two databases have been employed, one comprising 332 episodes of in-hospital CA (151815 QRS complexes) and another consisting of 105 hemodynamically stable patients (112497 QRS complexes). The method comprises three stages: signal preprocessing for noise removal, windowing and sample classification with a U-Net model, and finally, the segmented windows are merged to complete the process.

Results

The proposed method exhibited mean (standard deviation) F1 score/Sensitivity/Specificity/intersection over union values of 97.03(8.28)/ 97.69(11.38)/96.47(9.92)/79.09(15.78), and a 8.56(11.62) 

error for $\mathrm{QR}{S}_{\mathrm{on}}$, and 25.11(25.86) 

for $\mathrm{QR}{S}_{\mathrm{off}}$ instant delineation.

Conclusions

A precise delineator like this could support clinical practice by quantifying QRS features to enhance diagnostic accuracy and optimize treatment strategies.