IEEE J Biomed Health Inform. 2014 Jul;18(4):1138-52. doi: 10.1109/JBHI.2013.2294399.
An efficient heart sound segmentation (HSS) method that automatically detects the location of first ( S1) and second ( S2) heart sound and extracts them from heart auscultatory raw data is presented here. The heart phonocardiogram is analyzed by employing ensemble empirical mode decomposition (EEMD) combined with kurtosis features to locate the presence of S1, S2, and extract them from the recorded data, forming the proposed HSS scheme, namely HSS-EEMD/K. Its performance is evaluated on an experimental dataset of 43 heart sound recordings performed in a real clinical environment, drawn from 11 normal subjects, 16 patients with aortic stenosis, and 16 ones with mitral regurgitation of different degrees of severity, producing 2608 S1 and S2 sequences without and with murmurs, respectively. Experimental results have shown that, overall, the HSS-EEMD/K approach determines the heart sound locations in a percentage of 94.56% and segments heart cycles correctly for the 83.05% of the cases. Moreover, results from a noise stress test with additive Gaussian noise and respiratory noises justify the noise robustness of the HSS-EEMD/K. When compared with four other efficient methods that mainly employ wavelet transform, energy, simplicity, and frequency measures, respectively, using the same experimental database, the HSS-EEMD/K scheme exhibits increased accuracy and prediction power over all others at the level of 7-19% and 4-9%, respectively, both in controls and pathological cases. The promising performance of the HSS-EEMD/K paves the way for further exploitation of the diagnostic value of heart sounds in everyday clinical practice.
本文提出了一种高效的心音分段（HSS）方法，可自动检测第一心音（S1）和第二心音（S2）的位置，并从心音听诊原始数据中提取出来。通过使用集合经验模态分解（EEMD）结合峰度特征来分析心音心音图，以定位 S1、S2 的存在，并从记录的数据中提取出来，形成了所提出的 HSS 方案，即 HSS-EEMD/K。在一个真实临床环境中进行的 43 次心音记录的实验数据集上评估了其性能，该数据集来自 11 个正常受试者、16 个主动脉瓣狭窄患者和 16 个不同严重程度的二尖瓣反流患者，分别产生了 2608 个无杂音和有杂音的 S1 和 S2 序列。实验结果表明，总体而言，HSS-EEMD/K 方法在 94.56%的情况下确定心音位置，在 83.05%的情况下正确分段心周期。此外，通过添加高斯噪声和呼吸噪声的噪声压力测试的结果证明了 HSS-EEMD/K 的噪声鲁棒性。与另外四个主要分别采用小波变换、能量、简单性和频率测量的高效方法相比，使用相同的实验数据库，HSS-EEMD/K 方案在控制和病理情况下的准确性和预测能力分别提高了 7-19%和 4-9%。HSS-EEMD/K 的有前景的性能为进一步挖掘心音在日常临床实践中的诊断价值铺平了道路。
