Cardiovascular diseases have become one of the world’s leading causes of death today. Several decision-making systems have been developed with computer-aided support to help the cardiologists in detecting heart disease and thereby minimizing the mortality rate. This paper uses an unexplored sub-domain related to textural features for classifying phonocardiogram (PCG) as normal or abnormal using Grey Level Co-occurrence Matrix (GLCM). The matrix has been applied to extract features from spectrogram of the PCG signals taken from the Physionet 2016 benchmark dataset. Random Forest, Support Vector Machine, Neural Network, and XGBoost have been applied to assess the status of the human heart using PCG signal spectrogram. The result of GLCM is compared with the two other textural feature extraction methods, viz. structural co-occurrence matrix (SCM), and local binary patterns (LBP). Experimental results have proved that applying machine learning model to classify PCG signal on the dataset where GLCM has extracted the feature-set, the accuracy attained is greater as compared to its peer approaches. Thus, this methodology can go a long way to help the medical specialists in precisely and accurately assessing the heart condition of a patient.

中文翻译：

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使用共现矩阵和频谱图对人类心脏声音信号进行分类的健康模型

心血管疾病已成为当今世界主要的死亡原因之一。在计算机辅助支持下开发了几种决策系统，以帮助心脏病专家检测心脏病，从而最大限度地降低死亡率。本文使用与纹理特征相关的未开发子域，使用灰度共现矩阵 (GLCM) 将心音图 (PCG) 分类为正常或异常。该矩阵已用于从从 Physionet 2016 基准数据集中获取的 PCG 信号的频谱图中提取特征。随机森林、支持向量机、神经网络和 XGBoost 已被应用于使用 PCG 信号频谱图评估人类心脏的状态。将 GLCM 的结果与其他两种纹理特征提取方法进行比较，即。结构共现矩阵（SCM），和本地二进制模式（LBP）。实验结果证明，在 GLCM 提取特征集的数据集上应用机器学习模型对 PCG 信号进行分类，与同类方法相比，获得的准确度更高。因此，这种方法可以在很大程度上帮助医学专家准确和准确地评估患者的心脏状况。