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Abstract 268: A Convolutional Neural Network for Real-Time Discrimination of Shock-Indicated Rhythms During CPR


 Introduction:
 An interruption of chest compressions during CPR adversely affects patient outcome. Currently, however, periodical interruptions are unavoidable to assess the ECG rhythms and to give shocks for defibrillation if indicated. Evidence suggests a 5-second interruption immediately before shocks may translate into ~15% reduction of the chance of survival. The objective of this study was to build, train and validate a convolutional neural network (artificial intelligence) for detecting shock-indicated rhythms out of ECG signals corrupted with chest compression artifacts during CPR.
 
 
 Methods:
 Our convolutional neural network consisted of 7 convolutional layers, 3 pooling layers and 3 fully-connected layers for binary classification (shock-indicated vs non-shock-indicated). The input data set was a spectrogram consisting of 56 frequency-bins by 80 time-segments transformed from a 12.16-seconds ECG signal. From AEDs used for 236 patients with out-of-hospital cardiac arrest, 1,223 annotated ECG strips were extracted. Ventricular fibrillation and wide-QRS ventricular tachycardia with HR>180 beats/min were annotated as shock-indicated, and the others as non-shock-indicated. The total length of the strips was 8:49:57 (hr:min:sec) and 8:02:07 respectively for shock-indicated and non-shock-indicated rhythms. Those strips were converted into 465,102 spectrograms allowing partial overlaps and were fed into the neural network for training. The validation data set was obtained from a separate group of 225 patients, from which annotated ECG strips (total duration of 62:11:28) were extracted, yielding 43,800 spectrograms.
 
 
 Results:
 After the training, both the sensitivity and specificity of detecting shock-indicated rhythms over the training data set were 99.7% - 100% (varying with training instances). The sensitivity and specificity over the validation data set were 99.3% - 99.7% and 99.3% - 99.5%, respectively.
 
 
 Conclusions:
 The convolutional neural network has accurately and continuously evaluated the ECG rhythms during CPR, potentially obviating the need for rhythm checks for defibrillation during CPR.
