TY  - JOUR
AU  - Ivanović, Marija D.
AU  - Hannink, Julius
AU  - Ring, Matthias
AU  - Baronio, Fabio
AU  - Vukčević, Vladan
AU  - Hadžievski, Ljupčo
AU  - Eskofier, Bjoern
PY  - 2020
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/9678
AB  - 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.MethodsA 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.ResultsThe 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.ConclusionsThe 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.
T2  - Artificial Intelligence in Medicine
T1  - Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design
VL  - 110
SP  - 101963
DO  - 10.1016/j.artmed.2020.101963
ER  - 

@article{
author = "Ivanović, Marija D. and Hannink, Julius and Ring, Matthias and Baronio, Fabio and Vukčević, Vladan and Hadžievski, Ljupčo and Eskofier, Bjoern",
year = "2020",
abstract = "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.MethodsA 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.ResultsThe 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.ConclusionsThe 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.",
journal = "Artificial Intelligence in Medicine",
title = "Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design",
volume = "110",
pages = "101963",
doi = "10.1016/j.artmed.2020.101963"
}

Ivanović, M. D., Hannink, J., Ring, M., Baronio, F., Vukčević, V., Hadžievski, L.,& Eskofier, B.. (2020). Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design. in Artificial Intelligence in Medicine, 110, 101963.
https://doi.org/10.1016/j.artmed.2020.101963

Ivanović MD, Hannink J, Ring M, Baronio F, Vukčević V, Hadžievski L, Eskofier B. Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design. in Artificial Intelligence in Medicine. 2020;110:101963.
doi:10.1016/j.artmed.2020.101963 .

Ivanović, Marija D., Hannink, Julius, Ring, Matthias, Baronio, Fabio, Vukčević, Vladan, Hadžievski, Ljupčo, Eskofier, Bjoern, "Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design" in Artificial Intelligence in Medicine, 110 (2020):101963,
https://doi.org/10.1016/j.artmed.2020.101963 . .