The electrocardiogram (ECG) is a noninvasive test used extensively to monitor and diagnose cardiac arrhythmia. Existing automated arrhythmia classification methods hardly achieve acceptable performance in detecting different heart conditions, especially under imbalanced datasets. This paper presents a novel method of heartbeat classification from ECG using deep learning. An automated system named ‘CardioNet’ is devised that employs the principle of transfer learning for faster and robust classification of heartbeats for arrhythmia detection. It uses pre-trained architecture of DenseNet that is trained on ImageNet dataset of millions images. The weights obtained during training of DenseNet are used to fine-tune CardioNet learning on the ECG dataset, resulting a unique system providing faster training and testing. The ECG dataset is prepared using augmentation process to provide a comprehensive learning of heartbeat morphology in the presence of intraclass variations. Two benchmark datasets of ECG recordings e.g., MIT-BIH arrhythmia and PTB are used to classify 29 types of heartbeats for arrhythmia classification. The proposed CardioNet system achieves higher classification accuracy of 98.92% outperforming other methods and shows robustness to different irregular heartbeats or arrhythmias.

心电图 (ECG) 是一种无创检查，广泛用于监测和诊断心律失常。现有的自动心律失常分类方法很难在检测不同的心脏状况方面达到可接受的性能，尤其是在不平衡的数据集下。本文提出了一种使用深度学习从心电图进行心跳分类的新方法。设计了一种名为“CardioNet”的自动化系统，该系统采用迁移学习原理，可更快、更稳健地对心律失常检测进行心跳分类。它使用 DenseNet 的预训练架构，该架构在数百万张图像的 ImageNet 数据集上进行训练。在 DenseNet 训练期间获得的权重用于在 ECG 数据集上微调 CardioNet 学习，从而形成一个独特的系统，可提供更快的训练和测试。ECG 数据集是使用增强过程准备的，以在存在类内变化的情况下提供对心跳形态的全面学习。心电图记录的两个基准数据集例如，MIT-BIH 心律失常和 PTB 用于对 29 种心跳进行分类，用于心律失常分类。所提出的 CardioNet 系统实现了 98.92% 的更高分类准确率，优于其他方法，并显示出对不同不规则心跳或心律失常的鲁棒性。