Romdhane Taissir Fekih, Alhichri Haikel, Ouni Ridha, Atri Mohamed
National Engineering School of Sousse, Electrical Engineering Department, University of Sousse, Sousse, Tunisia.
ALISR, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
Comput Biol Med. 2020 Aug;123:103866. doi: 10.1016/j.compbiomed.2020.103866. Epub 2020 Jul 5.
The electrocardiogram (ECG) is an effective tool for cardiovascular disease diagnosis and arrhythmia detection. Most methods proposed in the literature include the following steps: 1) denoizing, 2) segmentation into heartbeats, 3) feature extraction, and 4) classification. In this paper, we present a deep learning method based on a convolutional neural network (CNN) model. CNN models can perform feature extraction automatically and jointly with the classification step. In other words, our proposed method does not require a feature extraction step with hand-crafted techniques. Our proposed method is also based on an algorithm for heartbeat segmentation that is different from most existing methods. In particular, the segmentation algorithm defines each ECG heartbeat to start at an R-peak and end after 1.2 times the median RR time interval in a 10-s window. This method is simple and effective, as it does not use any form of filtering or processing that requires assumptions about the signal morphology or spectrum. Although enhanced ECG heartbeat classification algorithms have been proposed in the literature, they failed to achieve high performance in detecting some heartbeat categories, especially for imbalanced datasets. To overcome this challenge, we propose an optimization step for the deep CNN model using a novel loss function called focal loss. This function focuses on minority heartbeat classes by increasing their importance. We trained and evaluated our proposed model with the MIT-BIH and INCART datasets to identify five arrhythmia categories (N, S, V, Q, and F) based on the Association for Advancement of Medical Instrumentation (AAMI) standard. The evaluation results revealed that the focal loss function improved the classification accuracy for the minority classes as well as the overall metrics. Our proposed method achieved 98.41% overall accuracy, 98.38% overall F1-score, 98.37% overall precision, and 98.41% overall recall. In addition, our method achieved better performance than that of existing state-of-the-art methods.
心电图（ECG）是用于心血管疾病诊断和心律失常检测的有效工具。文献中提出的大多数方法包括以下步骤：1）去噪，2）分割为心跳，3）特征提取，以及4）分类。在本文中，我们提出了一种基于卷积神经网络（CNN）模型的深度学习方法。CNN模型可以自动执行特征提取，并与分类步骤相结合。换句话说，我们提出的方法不需要使用手工技术进行特征提取步骤。我们提出的方法还基于一种与大多数现有方法不同的心跳分割算法。具体而言，该分割算法将每个ECG心跳定义为从R波峰开始，并在10秒窗口内的RR时间间隔中位数的1.2倍之后结束。该方法简单有效，因为它不使用任何形式的滤波或处理，而这些滤波或处理需要对信号形态或频谱进行假设。尽管文献中已经提出了增强的ECG心跳分类算法，但它们在检测某些心跳类别时未能实现高性能，特别是对于不平衡数据集。为了克服这一挑战，我们提出了一种使用称为焦点损失的新型损失函数对深度CNN模型进行优化的步骤。该函数通过增加少数心跳类别的重要性来关注它们。我们使用MIT-BIH和INCART数据集对我们提出的模型进行了训练和评估，以根据医疗仪器促进协会（AAMI）标准识别五种心律失常类别（N、S、V、Q和F）。评估结果表明，焦点损失函数提高了少数类别的分类准确率以及整体指标。我们提出的方法实现了98.41%的整体准确率、98.38%的整体F1分数、98.37%的整体精度和98.41%的整体召回率。此外，我们的方法比现有最先进的方法表现更好。
