Background

Heart valve diseases are commonly seen ailments, and many people suffer from these diseases. Therefore, early diagnosis and accurate treatment are crucial for these disorders. This research aims to diagnose heart valve diseases automatically by employing a new stable feature generation method.

Materials and method

This research presents a stable feature generator-based automated heart diseases diagnosis model. This model uses three primary sections. They are stable feature generation using the improved one-dimensional binary pattern (IBP), selecting the most discriminative feature with neighborhood component analysis (NCA), and classification employing the conventional classifiers. IBP uses three kernels, and they are named signum, left signed, and right signed kernels. By applying these kernels, 768 features are generated. NCA aims to choose the most discriminative ones, and 64 features are chosen to employ NCA. The k nearest neighbor (kNN) and support vector machine (SVM) classifier are employed in the classification phase. Open access (public published) Phonocardiogram signal (PCG) sound dataset is used to calculate this model's measurements. This dataset contains 1000 PCGs with five categories.

Results

The presented IBP and NCA-based heart valve disorders classification model tested using kNN and SVM classifier and attained 99.5% and 98.30% accuracies, respectively.

Conclusions

Per the results, the presented IBP and NCA-based PCG sound classification is a successful method. Moreover, this model is basic and high accurate. Therefore, it is ready for the development of real-time implementations.

中文翻译：

---

基于新型三核二进制模式特征提取的自动PCG声音分类方法

背景

心脏瓣膜疾病是常见疾病，许多人患有这些疾病。因此，早期诊断和准确治疗对于这些疾病至关重要。本研究旨在通过采用一种新的稳定特征生成方法来自动诊断心脏瓣膜疾病。

材料与方法

这项研究提出了一个稳定的基于特征生成器的自动心脏病诊断模型。该模型使用三个主要部分。它们使用改进的一维二进制模式（IBP）进行稳定的特征生成，通过邻域分量分析（NCA）选择最具区分性的特征，并使用常规分类器进行分类。IBP使用三个内核，它们分别称为signum，左签名和右签名内核。通过应用这些内核，可以生成768个功能。NCA旨在选择最具区别性的功能，并选择了64个功能来采用NCA。在分类阶段，采用了k最近邻（kNN）和支持向量机（SVM）分类器。开放获取（公开出版）心电图信号（PCG）声音数据集用于计算该模型的测量值。

结果

提出的基于IBP和NCA的心脏瓣膜疾病分类模型使用kNN和SVM分类器进行了测试，分别达到了99.5％和98.30％的准确度。

结论

根据结果​​，提出的基于IBP和NCA的PCG声音分类是一种成功的方法。而且，该模型是基本且高精度的。因此，它为实时实现的开发做好了准备。