PCA-based heart sound feature generation for a ventricular septal defect discrimination
作者： Shuping Sun , Haibin Wang , Chi Cheng , Zhenhui Chang , Dayong Huang
DOI: 10.1109/ICCWAMTIP.2017.8301464
关键词: Principal component analysis 、 Computer science 、 Pattern recognition 、 Stethoscope 、 Heart sounds 、 Artificial intelligence 、 Audio signal 、 Feature extraction 、 Time domain 、 Sound (medical instrument) 、 Frequency domain
摘要: In this study, a simple system based on PCA-based heart sound feature extraction is proposed for discriminating ventricular septal defects (VSDs), which are generally divided into three types: small VSDs (SVSDs), moderate (MVSDs) and large (LVSDs). The stages corresponding to the discrimination implementation summarized as follows. stage 1, collected by stethoscope preprocessed using wavelet decomposition (WD). 2, time domain features [T12, T11] first extracted from envelope Et, signal Xt filtered WD method, frequency [Fg, Fw] subsequently Ef one period sound, automatically segmented sounds short modified Hilbert transform (STMHT). Finally, time-frequency matrix (TFFM), expressed TFFM = T11, Fg, Fw], generated. 3, diagnostic y y2 SVSD, MVSD, LVSD normal mean standard deviation [−2.41±0.49, 2.16±0.45], [−1.87±0.35, 0.22±0.33], [−1.63 ± 0.56, −2.11 0.68] [1.11 0.43, 0.09 0.43], respectively. Therefore, given y1 generated through discriminate different types of VSD other sounds. Moreover, validate usefulness system, mitral stenosis (MS) aortic regurgitation (AR) used examples detection analysis scatter diagram [y1, y2].
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