Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, People's Republic of China.
Physiol Meas. 2022 Jun 28;43(6). doi: 10.1088/1361-6579/ac6d40.
Heart sounds can reflect detrimental changes in cardiac mechanical activity that are common pathological characteristics of chronic heart failure (CHF). The ACC/AHA heart failure (HF) stage classification is essential for clinical decision-making and the management of CHF. Herein, a machine learning model that makes use of multi-scale and multi-domain heart sound features was proposed to provide an objective aid for ACC/AHA HF stage classification.A dataset containing phonocardiogram (PCG) signals from 275 subjects was obtained from two medical institutions and used in this study. Complementary ensemble empirical mode decomposition and tunable-Q wavelet transform were used to construct self-adaptive sub-sequences and multi-level sub-band signals for PCG signals. Time-domain, frequency-domain and nonlinear feature extraction were then applied to the original PCG signal, heart sound sub-sequences and sub-band signals to construct multi-scale and multi-domain heart sound features. The features selected via the least absolute shrinkage and selection operator were fed into a machine learning classifier for ACC/AHA HF stage classification. Finally, mainstream machine learning classifiers, including least-squares support vector machine (LS-SVM), deep belief network (DBN) and random forest (RF), were compared to determine the optimal model.. The results showed that the LS-SVM, which utilized a combination of multi-scale and multi-domain features, achieved better classification performance than the DBN and RF using multi-scale or/and multi-domain features alone or together, with average sensitivity, specificity, and accuracy of 0.821, 0.955 and 0.820 on the testing set, respectively.PCG signal analysis provides efficient measurement information regarding CHF severity and is a promising noninvasive method for ACC/AHA HF stage classification.
心音可以反映心脏机械活动的有害变化，这些变化是慢性心力衰竭（CHF）的常见病理特征。ACC/AHA 心力衰竭（HF）分期分类对于临床决策和 CHF 的管理至关重要。在此，提出了一种利用多尺度和多域心音特征的机器学习模型，为 ACC/AHA HF 分期分类提供客观辅助。本研究使用了来自两个医疗机构的 275 名受试者的心音图（PCG）信号数据集。互补集合经验模态分解和可调 Q 子波变换用于构建 PCG 信号的自适应子序列和多电平子带信号。然后对原始 PCG 信号、心音子序列和子带信号进行时域、频域和非线性特征提取，构建多尺度和多域心音特征。通过最小绝对值收缩和选择算子选择的特征被输入到机器学习分类器中进行 ACC/AHA HF 分期分类。最后，将主流机器学习分类器（包括最小二乘支持向量机（LS-SVM）、深度置信网络（DBN）和随机森林（RF））进行比较，以确定最佳模型。结果表明，LS-SVM 利用多尺度和多域特征的组合，比 DBN 和 RF 单独或组合使用多尺度或/和多域特征具有更好的分类性能，在测试集上的平均灵敏度、特异性和准确度分别为 0.821、0.955 和 0.820。PCG 信号分析为 CHF 严重程度提供了有效的测量信息，是一种有前途的 ACC/AHA HF 分期分类的非侵入性方法。
