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Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design

Ivanović MD, Hannink J, Ring M, Baronio F, Vukčević V, Hadžievski L, Eskofier B (2020)

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Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 110

Article Number: 101963

DOI: 10.1016/j.artmed.2020.101963

Abstract

Objective: 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.

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How to cite

APA:

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. Artificial Intelligence in Medicine, 110. https://doi.org/10.1016/j.artmed.2020.101963

MLA:

Ivanović, Marija D., et al. "Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design." Artificial Intelligence in Medicine 110 (2020).

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