Robust classification of heart valve sound based on adaptive EMD and feature fusion

• Published in: PloS one Vol. 17; no. 12; p. e0276264
• Main Authors: Wang, Weibo, Yuan, Jin, Wang, Bingrong, Fang, Yu, Zheng, Yongkang, Hu, Xingping
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.12.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Cardiovascular disease; Cardiovascular diseases; Classification; Computer and Information Sciences; Correlation coefficient; Correlation coefficients; Decision trees; Empirical analysis; Engineering and Technology; Evaluation; Feature extraction; Feature selection; Heart; Heart diseases; Heart Sounds; Heart Valves; Medicine and Health Sciences; Methods; Metric space; Neural networks; Physical Sciences; Processing modes; Research and Analysis Methods; Rheumatic heart disease; Root-mean-square errors; Sound; Sounds; Waveforms; Wavelet transforms; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0276264

Cardiovascular disease (CVD) is considered one of the leading causes of death worldwide. In recent years, this research area has attracted researchers' attention to investigate heart sounds to diagnose the disease. To effectively distinguish heart valve defects from normal heart sounds, adaptive empirical mode decomposition (EMD) and feature fusion techniques were used to analyze the classification of heart sounds. Based on the correlation coefficient and Root Mean Square Error (RMSE) method, adaptive EMD was proposed under the condition of screening the intrinsic mode function (IMF) components. Adaptive thresholds based on Hausdorff Distance were used to choose the IMF components used for reconstruction. The multidimensional features extracted from the reconstructed signal were ranked and selected. The features of waveform transformation, energy and heart sound signal can indicate the state of heart activity corresponding to various heart sounds. Here, a set of ordinary features were extracted from the time, frequency and nonlinear domains. To extract more compelling features and achieve better classification results, another four cardiac reserve time features were fused. The fusion features were sorted using six different feature selection algorithms. Three classifiers, random forest, decision tree, and K-nearest neighbor, were trained on open source and our databases. Compared to the previous work, our extensive experimental evaluations show that the proposed method can achieve the best results and have the highest accuracy of 99.3% (1.9% improvement in classification accuracy). The excellent results verified the robustness and effectiveness of the fusion features and proposed method.