Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Figures

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  Fig. 1. Architecture of deep-learning-based algorithm for predicting cardiac arrest. BN denotes batch normalization, Conv convolutional layer, ECG electrocardiography, and FC fully connected layer

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  Fig. 2. Study flowchart

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  Fig. 3. Performances of artificial intelligence algorithms for predicting cardiac arrest. AUC denotes area under the receiver operating characteristic curve, CI confidence interval, DLA deep-learning based artificial intelligence algorithm, NPV negative predictive value, PPV positive predictive value, and ROC receiver operating characteristic curve

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  Fig. 4. Cumulative hazard of deterioration event in patients who had no cardiac arrest within 24 h. DLA denotes deep-learning based artificial intelligence algorithm, ECG electrocardiography, and ICU intensive care unit. The cutoff point used for dividing the risk groups was selected when the overall sensitivity was 90% in the development dataset. Cox proportional hazards regression was used to estimate the hazard for the delayed cardiac arrest and the deterioration events

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  Fig. 5. Electrocardiography and sensitivity map of patient with cardiac arrest. This is electrocardiography of patient who was 62 years old and was occurred cardiac arrest in external validation hospital. The cardiac arrest occurred 18 min after acquiring electrocardiography. The deep learning based artificial intelligence algorithm predicted cardiac arrest in this patient with a value of 0.685897, which was 32.7 times the cut-off value of sensitivity 90% in development dataset