Heart diseases are a major cause of human casualties each year. An accurate and efficient diagnosis is essential to minimize their risk. This paper presents a system for the classification of multiple cardiac disorders based on pulse plethysmographic (PuPG) signal analysis. In particular, the work focuses on the detection and classification of ischemic and rheumatic heart diseases using proposed 1D local ternary patterns of PuPG signals. The proposed methodology is applied on a self‐collected dataset consisting of 250 PuPG signals from 50 normal/healthy subjects, 140 signals from 28 ischemic patients, and 180 signals from 36 rheumatic patients. The effectiveness of proposed feature descriptors to precisely represent different classes of data is verified by several classifiers namely K‐nearest neighbours (KNN), support vector machines (SVM), and decision tree (DT) with 10‐fold cross‐validation. The proposed methodology achieves the best detection performance with 99% accuracy, 100% sensitivity, and 98% specificity using an SVM classifier with cubic kernel. The comparative analysis demonstrates that the proposed method is more accurate and reliable as compared to several existing works on cardiac disorders classification.

中文翻译：

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使用基于脉搏体积描记器信号的一维局部三元模式对心脏疾病进行分类

心脏病是每年造成人员伤亡的主要原因。准确有效的诊断对于最大程度地降低其风险至关重要。本文提出了一种基于脉冲体积描记（PuPG）信号分析的多发性心脏病分类系统。尤其是，这项工作着重于使用拟议的PuPG信号一维局部三元模式对缺血性和风湿性心脏病进行检测和分类。所提出的方法应用于自收集的数据集，该数据集包含来自50个正常/健康受试者的250 PuPG信号，来自28个缺血患者的140个信号和来自36个风湿病患者的180个信号。几个分类器，即K最近邻（KNN），支持向量机（SVM），以及具有10倍交叉验证的决策树（DT）。所提出的方法使用具有立方核的SVM分类器以99％的准确度，100％的灵敏度和98％的特异性实现了最佳检测性能。对比分析表明，与几种有关心脏疾病分类的现有工作相比，该方法更准确，更可靠。