Abbaszadeh Amin, Bazargani Mahdi
Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran.
Heliyon. 2024 Nov 19;10(23):e40537. doi: 10.1016/j.heliyon.2024.e40537. eCollection 2024 Dec 15.
Annually, the proportion of individuals suffering from cardiovascular disease rises significantly. Heart attacks are the most prevalent and unpleasant illness among them. Heart disease (HD) diagnosis can be complicated when there are multiple symptoms. The growing popularity of wearable smart devices has increased the likelihood of providing the Internet of Things (IoT). However, one of the biggest obstacles to overcome in implementing the system under IoT is developing a lightweight model for cardiac diagnosis and categorization. In this paper, we have presented a two-step heart disease classification method. This method includes demarcation of classes with the help of optimized non-linear support vector machine technique in the first step and determining the modified fuzzy class in the second step. Initially, pre-processing is accomplished using the ECG signals to eliminate noise and improve signal smoothness. Subsequently, features such as PQRS wave, linear characteristics, and reciprocal information are extracted from pre-processed signals. At the classification stage, the two-stage learning system is used to classify cardiac arrhythmias. First, using the wild horse optimization (WHO) technique (WHO-sigmoid-TH-NL-demarcation), each class is subjected to a binary classification based on feature demarcation, thresholding, and weighting of the sigmoid function. The information from the first stage will be transferred into the subsequent stage for an equal number of heart disease classifications. In the second step, a TS fuzzy logic system optimized by the Giza Pyramids Construction (GPC) approach (GPC-TS-Fuzzy) is utilized to classify each signal. The MIT-BIH arrhythmia dataset is used to assess the suggested approach. In a comprehensive evaluation of the suggested method, performance metrics including "accuracy, sensitivity, and specificity" yielded average values of 98.58 %, 98.13 %, and 96.47 %, respectively. The MATLAB platform is utilized to accomplish the proposed methodology.
每年，患心血管疾病的人数比例都显著上升。心脏病发作是其中最常见且令人不适的疾病。当出现多种症状时，心脏病（HD）的诊断可能会很复杂。可穿戴智能设备的日益普及增加了实现物联网（IoT）的可能性。然而，在物联网环境下实施该系统需要克服的最大障碍之一是开发一种用于心脏诊断和分类的轻量级模型。在本文中，我们提出了一种两步心脏病分类方法。该方法第一步借助优化的非线性支持向量机技术划分类别，第二步确定修正的模糊类别。首先，使用心电图信号进行预处理以消除噪声并提高信号平滑度。随后，从预处理后的信号中提取诸如PQRS波、线性特征和互信息等特征。在分类阶段，使用两阶段学习系统对心律失常进行分类。首先，使用野马优化（WHO）技术（WHO - sigmoid - TH - NL - 划分），基于特征划分、阈值处理和Sigmoid函数加权对每个类别进行二元分类。第一阶段的信息将被传递到后续阶段进行相同数量的心脏病分类。在第二步中，使用由吉萨金字塔构造（GPC）方法优化的TS模糊逻辑系统（GPC - TS - Fuzzy）对每个信号进行分类。使用麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）心律失常数据集来评估所提出的方法。在对所提出方法的综合评估中，包括“准确率、灵敏度和特异性”在内的性能指标分别产生了98.58%、98.13%和96.47%的平均值。利用MATLAB平台完成所提出的方法。
