A Hybrid GAN-Based DL Approach for the Automatic Detection of Shockable Rhythms in AED for Solving Imbalanced Data Problems

Abstract

Sudden cardiac arrest (SCA) is one of the global health issues causing high mortality. Hence, timely and agile detection of such arrests and immediate defibrillation support to SCA victims is of the utmost importance. An automated external defibrillator (AED) is a medical device used to treat patients suffering from SCA by delivering an electric shock. An AED implements the machine learning (ML)- or deep learning (DL)-based approach to detect whether the patient needs an electric shock and then automates the shock if needed. However, the effectiveness of these models has relied on the availability of well-balanced data in class distribution. Due to privacy concerns, collecting sufficient data is more challenging in the medical domain. Generative adversarial networks (GAN) have been successfully used to create synthetic data and are far better than standard oversampling techniques in maintaining the original data’s probability distribution. We, therefore, proposed a GAN-based DL approach, external classifier–Wasserstein conditional generative adversarial network (EC–WCGAN), to detect the shockable rhythms in an AED on an imbalanced ECG dataset. Our experiments demonstrate that the classifier trained with real and generated data via the EC–WCGAN significantly improves the performance metrics on the imbalanced dataset. Additionally, the WCGAN for generating synthetic data outperformed the standard oversampling technique, such as adaptive synthetic (ADASYN). In addition, our model achieved a high sensitivity, specificity, and F1-score (more than 99%) and a low balanced error rate (0.005) on the balanced 4-s segmented public Holter databases, meeting the American Health Association criteria for AEDs.