ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Cardiac Rhythmology
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1550422
Advanced ECG Feature Extraction and SVM Classification for Predicting Defibrillation Success in OHCA
Provisionally accepted
- 1杭州电子科技大学, 浙江省杭州市, China
- 2Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
- 3Department of Gerontology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huai'an, China
- 4Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
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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-frequency power showing the highest discriminative ability based on their Area Under the Curve (AUC) values. A Support Vector Machine (SVM) classifier, trained on these selected features, demonstrated a prediction accuracy of 95.6%, highlighting the efficacy of combining targeted ECG signal features with machine learning techniques to forecast defibrillation success accurately. This approach provides a non-invasive, rapid, and reliable method to support clinical decisions during OHCA emergencies. Future research aims to expand the dataset, refine feature extraction techniques, and explore additional machine learning models to further enhance prediction accuracy. This study underscores the potential of ECG-based feature analysis and targeted machine learning in improving resuscitation strategies, ultimately contributing to higher survival rates in OHCA patients.
Keywords: out-of-hospital cardiac arrest (OHCA), electrocardiogram (ECG), Defibrillation Outcome Prediction, machine learning, support vector machine (SVM), Feature Selection
Received: 23 Dec 2024; Accepted: 28 Jun 2025.
Copyright: © 2025 Zhang, Pan, Zhou, Zhang, Chen and Pan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Haqi Zhang, 杭州电子科技大学, 浙江省杭州市, China
Xiaotian Pan, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang Province, China
Weiwei Zhang, Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
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