Advanced ECG feature extraction and SVM classification for predicting defibrillation success in OHCA.

ABSTRACT: Out-of-hospital cardiac arrest (OHCA) represents a critical challenge for emergency medical services, with the necessity for rapid and accurate prediction of defibrillation outcomes to enhance patient survival. This study leverages a dataset of 251 ECG signals from OHCA patients, consisting of 195 unsuccessful and 56 successful resuscitation attempts as categorized by expert cardiologists. We extracted six crucial features from each ECG signal: heart rate, QRS complex amplitude, QRS complex duration, total power, low-frequency power (0.04-0.15 Hz), and high-frequency power (0.15-0.4 Hz). These features were derived using standard temporal and frequency domain methods. Subsequent analysis focused on selecting the most predictive features, with QRS complex amplitude, total power, and low-fre

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12308557 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature