ArticleMedical & biological engineering & computing2019
ECG-based pulse detection during cardiac arrest using random forest classifier.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 37 citations in OpenAlex.
- Development and preliminary validation of an explainable machine learning model for early ROSC detection during chest compression in a swine model of cardiac arrest.Resuscitation plus · 2026Article
- Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest.Resuscitation plus · 2024Article
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring.Micromachines · 2022Article
- Machine learning and feature engineering for predicting pulse presence during chest compressions.Royal Society open science · 2021Article
- Atrial Fibrillation Prediction from Critically Ill Sepsis Patients.Biosensors · 2021Article
- A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest.Entropy (Basel, Switzerland) · 2021Article
- Random forest-based prediction of stroke outcome.Scientific reports · 2021Article
- Novel Density Poincaré Plot Based Machine Learning Method to Detect Atrial Fibrillation From Premature Atrial/Ventricular Contractions.IEEE transactions on bio-medical engineering · 2021Article
- A Random Forest-Based Accuracy Prediction Model for Augmented Biofeedback in a Precision Shooting Training System.Sensors (Basel, Switzerland) · 2020Article
- Article
- Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest.Entropy (Basel, Switzerland) · 2019Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sudden cardiac arrest is one of the leading causes of death in the industrialized world. Pulse detection is essential for the recognition of the arrest and the recognition of return of spontaneous circulation during therapy, and it is therefore crucial for the survival of the patient. This paper introduces the first method based exclusively on the ECG for the automatic detection of pulse during cardiopulmonary resuscitation. Random forest classifier is used to efficiently combine up to nine features from the time, frequency, slope, and regularity analysis of the ECG. Data from 191 cardiac arrest patients was used, and 1177 ECG segments were processed, 796 with pulse and 381 without pulse. A leave-one-patient out cross validation approach was used to train and test the algorithm. The statistical distributions of sensitivity (SE) and specificity (SP) for pulse detection were estimated using 500 patient-wise bootstrap partitions. The mean (std) SE/SP for nine-feature classifier was 88.4 (1.8) %/89.7 (1.4) %, respectively. The designed algorithm only requires 4-s-long ECG segments and could be integrated in any commercial automated external defibrillator. The method permits to detect the presence of pulse accurately, minimizing interruptions in cardiopulmonary resuscitation therapy, and could contribute to improve survival from cardiac arrest.
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