Anticipation of clinical decompensation is essential for effective emergency and critical care.
In this study, we develop a multimodal machine learning approach to predict the onset of …

Background Artificial intelligence (AI) has demonstrated significant potential in supporting
emergency medical services personnel during out-of-hospital cardiac arrest (OHCA) care; …

Our scoping review provides a comprehensive analysis of the landscape of artificial
intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field …

In this paper, the ventricular fibrillation (VF) rhythm is detected by using a new approach
involving the support vector machine (SVM), adaptive boosting (AdaBoost) and differential …

Background Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of
death. Quantitative analysis of the VF electrocardiogram (ECG) can predict patient outcomes …

In this paper, we propose a prediction system of the effect of electrical defibrillation for
shockable arrhythmias. In order to develop the proposed prediction system, ECGs are firstly …

This paper proposes a prediction system of the effect of electrical defibrillation based on the
wavelet transform with pseudo-differential operators. To construct this system first, we …

In this proposed work, an electrocardiogram (ECG) signal is classified using multi-class
kernel function based support vector machine (SVM) approach with optmized …

The provided database of 260 ECG signals was collected from patients with out-of-hospital
cardiac arrest while treated by the emergency medical services. Each ECG signal contains a …

Out-of-hospital ventricular fibrillation (VF) cardiac arrest results in approximately 50,000
deaths per year in the United States. Treatment includes defibrillation shock supported by …