School of Electrical and Computer Engineering, Tarbiat Modares University, P.O. Box 14115-143, Tehran, Iran.
Comput Biol Med. 2011 Sep;41(9):802-11. doi: 10.1016/j.compbiomed.2011.06.016. Epub 2011 Jul 8.
Heart murmurs are pathological sounds produced by turbulent blood flow due to certain cardiac defects such as valves disorders. Detection of murmurs via auscultation is a task that depends on the proficiency of physician. There are many cases in which the accuracy of detection is questionable. The purpose of this study is development of a new mathematical model of systolic murmurs to extract their crucial features for identifying the heart diseases. A high resolution algorithm, multivariate matching pursuit, was used to model the murmurs by decomposing them into a series of parametric time-frequency atoms. Then, a novel model-based feature extraction method which uses the model parameters was performed to identify the cardiac sound signals. The proposed framework was applied to a database of 70 heart sound signals containing 35 normal and 35 abnormal samples. We achieved 92.5% accuracy in distinguishing subjects with valvular diseases using a MLP classifier, as compared to the matching pursuit-based features with an accuracy of 77.5%.
心脏杂音是由于某些心脏缺陷（如瓣膜疾病）导致血流湍流产生的病理性声音。通过听诊检测杂音依赖于医生的熟练程度。在许多情况下，检测的准确性值得怀疑。本研究的目的是开发一种新的收缩期杂音的数学模型，以提取其关键特征，用于识别心脏疾病。使用高分辨率算法，多元匹配追踪，通过将它们分解为一系列参数时频原子来对杂音进行建模。然后，使用模型参数执行一种新的基于模型的特征提取方法来识别心音信号。该框架应用于包含 35 个正常和 35 个异常样本的 70 个心音信号数据库。与基于匹配追踪的特征（准确率为 77.5%）相比，使用 MLP 分类器在区分瓣膜疾病患者方面达到了 92.5%的准确率。
