Using time-frequency features to recognize abnormal heart sounds
Using time-frequency features to recognize abnormal heart sounds
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DOI：
10.22489/cinc.2016.327-210
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发表时间：
2016-09
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影响因子：
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通讯作者：
Hsuan-Lin Her;Hung-Wen Chiu
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文献类型：
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作者：
Hsuan-Lin Her;Hung-Wen Chiu
心脏病占所有死亡人数的6%。心音是临床体检中的常规检查，对心脏病的检测具有一定的敏感性。在目前的研究中，我们建立了一个模型来对心音进行分类，用于临床前筛查。心音是在不受控制的环境下录制的，每个样本的范围从5秒到120秒不等。这是由年度PhysioNet/CinC挑战赛提出的一项工作。由于心脏事件的时间性和紧张性，在本研究中，我们使用时频特征来对心音进行分类。首先，使用施普林格改进版的施密特方法将每个听到的声音记录分割成几个周期。每个心动周期被切割成10个分区，每个分区通过补零得到数据点。使用快速傅立叶变换(FFT)从每个分区提取光谱特征，从而生成3500个特征矩阵。采用滤波法，选取40个特征作为最终的分类器。然后将每个周期的平均特征矩阵应用于采用2-均值聚类和人工神经网络(ANN)的分类系统。通过聚类来识别不确定类。利用训练好的人工神经网络模型对正常心音和异常心音进行识别。结果表明，该方法的准确率为86.5%，灵敏度为84.4%，特异度为86.9%。研究表明，由于异常事件的异质性和样本内偏差，对异常心音进行分类是一项非常困难的任务。
Disease of the heart accounts for 6% of all death. Heart sound is a routine in physical examination clinically, and is sensitive in detecting a subset of heart diseases. In the current study, we build up a model to classify heart sounds for preclinical screening. Heart sounds are recorded under uncontrolled environment, and each sample can range from 5 to 120 seconds. This is a work raised by annual PhysioNet/CinC Challenge. Because of timing and tonic natures of heart events, we used time-frequency features to classify heart sounds in this study. Firstly, each heard sound recording was segmented into cycles using Springer's improved version of Schmidt's method. Each cardiac cycle was cut into 10 partitions and data points were obtained by zero-padding in each partition. Spectral features were extracted from each partition using fast-Fourier Transform (FFT) thus a 3,500 feature matrix was created. Using filter method, 40 features were selected for the final classifier. The average feature matrix of each cycle was then applied to a classification system using 2-means clustering and artificial neural network (ANN). By clustering the unsure class was recognized. The discrimination of normal and abnormal heart sound were performed by a well-trained ANN model. The results showed that our proposed method got a performance with an accuracy 86.5%, a sensitivity 84.4%, a specificity 86.9%. Here we show that classifying abnormal heart sound is a really difficult task due to the heterogeneity of “abnormal events” and intra-sample deviation.