Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1288-1291. doi: 10.1109/EMBC48229.2022.9871124.
Atrial fibrillation (AF) is a common supraventricular arrhythmia. Its automatic identification by standard 12-lead electrocardiography (ECG) is still challenging. Recently, deep learning provided new instruments able to mimic the diagnostic ability of clinicians but only in case of binary classification (AF vs. normal sinus rhythm-NSR). However, binary classification is far from the real scenarios, where AF has to be discriminated also from several other physiological and pathological conditions. The aim of this work is to present a new AF multiclass classifier based on a convolutional neural network (CNN), able to discriminate AF from NSR, premature atrial contraction (PAC) and premature ventricular contraction (PVC). Overall, 2796 12-lead ECG recordings were selected from the open-source "PhysioNet/Computing in Cardiology Challenge 2021" database, to construct a dataset constituted by four balanced classes, namely AF class, PAC class, PVC class, and NSR class. Each lead of each ECG recording was decomposed into spectrogram by continuous wavelet transform and saved as 2D grayscale images, used to feed a 6-layers CNN. Considering the same CNN architecture, a multiclass classifiers (all classes) and three binary classifiers (AF class, PAC class, and PVC class vs. NSR class) were created and validated by a stratified shuffle split cross-validation of 10 splits. Performance was quantified in terms of area under the curve (AUC) of the receiver operating characteristic. Multiclass classifier performance was high (AF class: 96.6%; PAC class: 95.3%; PVC class: 92.8%; NSR class: 97.4%) and preferable to binary classifiers. Thus, our CNN AF multiclass classifier proved to be an efficient tool for AF discrimination from physiological and pathological confounders. Clinical Relevance-Our CNN AF multiclass classifier proved to be suitable for AF discrimination in real scenarios.
心房颤动（AF）是一种常见的室上性心律失常。其通过标准 12 导联心电图（ECG）的自动识别仍然具有挑战性。最近，深度学习提供了新的工具，能够模拟临床医生的诊断能力，但仅在二进制分类（AF 与正常窦性节律-NSR）的情况下。然而，二进制分类远非真实场景，在真实场景中，AF 还必须与其他几种生理和病理情况区分开来。本工作的目的是提出一种新的基于卷积神经网络（CNN）的 AF 多类分类器，能够将 AF 与 NSR、房性早搏（PAC）和室性早搏（PVC）区分开来。总体而言，从开源“PhysioNet/Computing in Cardiology Challenge 2021”数据库中选择了 2796 个 12 导联 ECG 记录，构建了一个由四个平衡类组成的数据集，即 AF 类、PAC 类、PVC 类和 NSR 类。每个 ECG 记录的每个导联都通过连续小波变换分解为声谱图，并保存为 2D 灰度图像，用于馈送 6 层 CNN。考虑到相同的 CNN 架构，创建了一个多类分类器（所有类）和三个二进制分类器（AF 类、PAC 类和 PVC 类与 NSR 类），并通过 10 个分裂的分层洗牌交叉验证进行验证。性能以接收者操作特征的曲线下面积（AUC）来量化。多类分类器的性能很高（AF 类：96.6%；PAC 类：95.3%；PVC 类：92.8%；NSR 类：97.4%），优于二进制分类器。因此，我们的 CNN AF 多类分类器被证明是一种从生理和病理混杂因素中区分 AF 的有效工具。临床相关性-我们的 CNN AF 多类分类器被证明适用于真实场景中的 AF 区分。
