Ivanović, Marija D. (57038326200)Marija D. (57038326200)IvanovićHannink, Julius (56352302600)Julius (56352302600)HanninkRing, Matthias (55546847500)Matthias (55546847500)RingBaronio, Fabio (6603509435)Fabio (6603509435)BaronioVukčević, Vladan (15741934700)Vladan (15741934700)VukčevićHadžievski, Ljupco (6602497159)Ljupco (6602497159)HadžievskiEskofier, Bjoern (26428080900)Bjoern (26428080900)Eskofier2025-07-022025-07-022020https://doi.org/10.1016/j.artmed.2020.101963https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092661204&doi=10.1016%2fj.artmed.2020.101963&partnerID=40&md5=f4749a6480d71dbe87489d971461e42bhttps://remedy.med.bg.ac.rs/handle/123456789/12423Objective: Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned. Methods: A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class. Results: The obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators. Conclusions: The learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community. © 2020 Elsevier B.V.Convolutional neural networks (CNN)Deep learningDefibrillationShock outcomeVentricular fibrillation (VF)