Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classification. The main hypothesis is that the SH coefficients of the parameterized endocardial shapes in MI patients are recognizable and distinguishable from healthy subjects. The SH parameterization, expansion, and registration of the LV endocardial shapes were performed, then parametric-based features were extracted. The proposed method performance was investigated by varying considered phases (i.e., the end-systole (ES) or the end-diastole (ED) frames), the spatial alignment procedures based on three modes (i.e., the center of the apical (CoA), the center of mass (CoM), and the center of the basal (CoB)), and considered orders of SH coefficients. After applying principal component analysis (PCA) on the feature vectors, support vector machine (SVM), K-nearest neighbors (K-NN), and random forest (RF) were trained and tested using the leave-one-out cross-validation (LOOCV). The proposed method validation was performed via a dataset containing healthy and MI subjects selected from the automated cardiac diagnosis challenge (ACDC) database. The promising results show the effectiveness of the proposed classification model. SVM reached the best performance with accuracy, sensitivity, specificity, and F-score of 97.50%, 95.00%, 100.00%, and 97.56%, respectively, using the introduced optimum feature set. This study demonstrates the robustness of combining the SH coefficients and machine learning techniques. We also quantify and notably highlight the contribution of different parameters in the classification and finally introduce an optimal feature set with maximum discriminant strength for the MI classification task. Moreover, the obtained results confirm that the proposed method performs more accurately than conventional point-based methods and also the current start-of-the-art, i.e., clinical measures. We showed our method’s generalizability using employing it in dilated cardiomyopathy (DCM) detection and achieving promising results too.

Graphical abstract

Parametric-based feature selection via spherical harmonics coefficients for the left ventricle myocardial infarction screening

中文翻译：

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基于参数的左心室心肌梗死筛查球谐系数特征选择

使用机器学习技术对心脏病进行计算机辅助诊断 (CAD) 最近受到了很多关注。在这项研究中，我们提出了一种新的基于参数的特征选择方法，使用左心室 (LV) 的三维球谐 (SH) 形状描述符进行智能心肌梗塞 (MI) 分类。主要假设是 MI 患者参数化心内膜形状的 SH 系数是可识别的，并可与健康受试者区分开来。执行左心室心内膜形状的 SH 参数化、扩展和配准，然后提取基于参数的特征。通过不同的考虑阶段（即收缩末期 (ES) 或舒张末期 (ED) 帧）、基于三种模式的空间对齐程序（即。例如，心尖 (CoA)、质心 (CoM) 和基底中心 (CoB)），并考虑 SH 系数的顺序。在对特征向量应用主成分分析 (PCA) 后，使用留一法交叉验证训练和测试支持向量机 (SVM)、K-最近邻 (K-NN) 和随机森林 (RF) (LOOCV)。所提出的方法验证是通过包含从自动心脏诊断挑战 (ACDC) 数据库中选择的健康和 MI 受试者的数据集进行的。有希望的结果表明了所提出的分类模型的有效性。使用引入的最优特征集，SVM 达到了最佳性能，准确度、灵敏度、特异性和 F-score 分别为 97.50%、95.00%、100.00% 和 97.56%。这项研究证明了结合 SH 系数和机器学习技术的鲁棒性。我们还量化并特别突出了不同参数在分类中的贡献，并最终为 MI 分类任务引入了具有最大判别强度的最佳特征集。此外，所获得的结果证实，所提出的方法比传统的基于点的方法以及当前最先进的方法（即临床测量）执行得更准确。我们将其用于扩张型心肌病 (DCM) 检测并显示出我们的方法的普遍性，并且也取得了有希望的结果。我们还量化并特别突出了不同参数在分类中的贡献，并最终为 MI 分类任务引入了具有最大判别强度的最佳特征集。此外，所获得的结果证实，所提出的方法比传统的基于点的方法以及当前最先进的方法（即临床测量）执行得更准确。我们将其用于扩张型心肌病 (DCM) 检测并显示出我们的方法的普遍性，并且也取得了有希望的结果。我们还量化并特别突出了不同参数在分类中的贡献，并最终为 MI 分类任务引入了具有最大判别强度的最佳特征集。此外，所获得的结果证实，所提出的方法比传统的基于点的方法以及当前最先进的方法（即临床测量）执行得更准确。我们将其用于扩张型心肌病 (DCM) 检测并显示出我们的方法的普遍性，并且也取得了有希望的结果。

图形概要

基于参数的左心室心肌梗死筛查球谐系数特征选择