Abstract 12792: Enhancing the Accuracy of Cardiac Rhythm Analysis in Automated External Defibrillators During Ongoing Cardiopulmonary Resuscitation by Applying a Deep Encoder-Decoder Filtering Model

Abstract

Survival from out-of-hospital cardiac arrests depends on an accurate defibrillatory shock decision during cardiopulmonary resuscitation (CPR). Since chest compressions induce severe motion artifact in the electrocardiogram (ECG), current automatic external defibrillators (AEDs) do not perform CPR during the rhythm analysis period. However, performing continuous CPR is vital and dramatically increases the chance of survival. Hence, we demonstrate a novel application of a deep convolutional neural network encoder-decoder (CNNED) method in suppressing CPR artifact in near real-time using only ECG data. The encoder portion of the CNNED uses the frequency and phase contents derived via time-varying spectral analysis to learn distinct features that are representative of both the ECG signal and CPR artifact. The decoder portion takes the results from the encoder and reconstructs what is perceived as the motion artifact removed ECG data. These procedures are done via multitude of training of CNNED using many different arrhythmia contaminated with CPR. In this study, CPR-contaminated ECGs were generated by combining clean ECG with CPR artifacts from 52 different performers. ECG data from CUDB, VFDB, and SDDB datasets which belong to the Physionet’s Physiobank archive were used to create the training set containing 89,984 14-second ECG segments. The performance of the proposed CNNED was evaluated on a separate test set comprising of 23,816 CPR-contaminated 14-second ECG segments from 458 subjects. The results were evaluated by two metrics: signal-to-noise ratio (SNR), and Defibtech’s AED rhythm analysis algorithm. CNNED resulted in the increase of the mean SNR value from -3 dB to 5.63 dB and 6.3 dB for shockable and non-shockable rhythms, respectively. Comparing Defibtech’s AED rhythm classifier before and after applying CNNED on the CPR-contaminated ECG, the specificity improved from 96.57% to 99.31% for normal sinus rhythm, and from 91.5% to 96.57% for other non-shockable rhythms. The sensitivity of shockable detection also increased from 67.68% to 87.76% for ventricular fibrillation, and from 62.71% to 81.27% for ventricular tachycardia. These results indicate continuous and accurate AED rhythm analysis without stoppage of CPR using only ECG data.