Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a deep convolutional Encoder-Decoder filtering model

• Published in: Expert systems with applications Vol. 203; p. 117499
• Main Authors: Hajeb-M, Shirin, Cascella, Alicia, Valentine, Matt, Chon, K.H.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2022
• Subjects: Autoencoder denoising; Automated external defibrillation (AED); Cardiopulmonary resuscitation (CPR); Deep convolutional neural network encoder-decoder (CNNED); OHCA; Autoencoder denoising; OHCA; Cardiopulmonary resuscitation (CPR); Deep convolutional neural network encoder-decoder (CNNED); Automated external defibrillation (AED)
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.117499

•This is a new filtering technique for AEDs to suppress CPR artifacts from ECG signal.•We demonstrated an application of a deep convolutional encoder-decoder method in AEDs.•Results indicate continuous and accurate AED rhythm analysis without stoppage of CPR.•The observed result of the proposed model exceeds the defined performance goals of the AHA.•Unlike most of the previous approaches, no additional reference signal is required. Survival from out-of-hospital cardiac arrests (OHCA) 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) instruct the user not to 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 to suppress CPR artifact in near real-time, using only ECG data. The encoder portion of the CNNED uses the magnitude 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 a multitude of training of the CNNED using many different arrhythmias contaminated with CPR. In this study, CPR-contaminated ECGs were generated by combining clean ECGs with 52 different CPR artifacts. ECG data from CUDB, VFDB, SDDB, and AFDB datasets which belong to the Physionet’s Physiobank archive were used to create the training set containing 14-second ECG segments. The performance of the proposed CNNED was evaluated on a separate test set comprised of 23,816 CPR-contaminated 14-second ECG segments from 458 subjects. The results were evaluated by three metrics: signal-to-noise ratio (SNR), correlation coefficient, and accuracy of Defibtech’s AED shock advisory algorithm (SAA). CNNED resulted in an increase of the mean SNR value from −3 dB to 7.5 dB and 7.1 dB for shockable and non-shockable rhythms, respectively. 80.85% of the filtered shockable and 74.13% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; these values were only 13.30% and 12.71% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. Comparing results of Defibtech’s AED SAA before and after applying CNNED on the CPR-contaminated ECG, the specificity improved from 96.21% to 99.14% for normal sinus rhythm, and from 88.5% to 96.45% for other non-shockable rhythms. The sensitivity of shockable detection also increased from 67.68% to 90.90% for ventricular fibrillation, and from 62.71% to 82.26% for ventricular tachycardia. These results indicate continuous and accurate AED rhythm analysis without stoppage of CPR, using only ECG data.