Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan.
Faculty of Science, Northern Border University, Arar 1321, Saudi Arabia.
Sensors (Basel). 2022 Jul 27;22(15):5606. doi: 10.3390/s22155606.
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.
心律失常对人类生命构成重大威胁；因此，能够及时有效地诊断这些心律失常至关重要。有许多检测心律失常的技术；然而，最广泛采用的方法是使用心电图 (ECG)。医学专家对 ECG 的手动分析往往效率低下。因此，通过机器学习技术检测和识别 ECG 特征已变得流行。现有的机器学习方法有两个主要缺点：(a) 它们需要大量的训练时间；和 (b) 它们需要手动特征选择。为了解决这些问题，本文提出了一种新颖的深度学习框架，该框架通过在每个网络中堆叠相似的层来集成各种网络，从而生成单个稳健模型。所提出的框架已经在两个公开可用的数据集上进行了测试，用于识别五种微类心律失常。所提出方法的总体分类灵敏度、特异性、阳性预测值和准确性分别为 98.37%、99.59%、98.41%和 99.35%。将结果与最先进的方法进行了比较。所提出的方法在灵敏度、特异性、阳性预测值、准确性和计算成本方面均优于现有方法。
