Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.     

Computerized heart sounds analysis

This paper is concerned with a synthesis study of the fast Fourier transform (FFT), the short-time Fourier transform (STFT), the Wigner distribution (WD) and the wavelet transform (WT) in analysing the phonocardiogram signal (PCG). It is shown that these transforms provide enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency (TF) PCG signal characteristics and consequently aid diagnosis. Similarly, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties. The studied techniques (FT, STFT, WD, CWT, DWT and PWT) of analysis can thus be regarded as complementary in the TF analysis of the PCG signal; each will relate to a part distinct from the analysis in question.     

基于联合时频分析的X射线管旋转阳极振动特征分析研究

本文研究了一种利用联合时频分析（Joint Time Frequency Analysis）分析X射线管振动信号特征的方法。该方法利用短时傅里叶变换（Short Time Fourier Transform）的基本原理,并采用LabView中的信号处理工具编写了振动信号短时傅里叶变换分析程序,通过实验证明了该方法适用于监测X射线管在工作过程中振动信号频率随时间变化的情况,为X射线管工作状态监测提供了有效手段。     

基于时频分析的T波交替检测算法的研究

心电的T波交替现象与室性心律失常有关,已成为预测心脏猝死的一个无创的临床指标.当前T波交替分析常用谱分析方法,为弥补其不具备时间分辨率的不足,本研究提出一种鲁棒的基于时频分析的微伏级T波交替检测算法:通过心电信号的短时傅里叶变换,提取时域T波序列,计算其在选定时频区域的能量谱；然后运用Wilcoxon秩和检验统计分析方法,检测微伏级T波交替现象.经仿真实验、欧洲ST-T数据及临床检测实验,本算法对T波交替检测的平均灵敏性达90.4％,正确预测率达92.0％,在30 dB及以上信噪比情况下,实现了100％的正确检测.仿真实验还表明本算法也支持短时心电数据的TWA准确检测.     

雷达式生命探测仪中人体与动物识别技术的研究

雷达式生命探测仪通过检测生命体的呼吸信号来确定废墟、建筑物内等一些用肉眼无法观测到的生命体时,不仅关心生命体是否存在,而且需要知道生命体为人体还是动物。本文利用短时傅利叶变换对生命体的呼吸信号进行变换,并通过奇异值分解有效地提取特征矢量进行模式识别,能够成功地识别人体和动物。实验结果表明,基于短时傅利叶变换的奇异值分解法能够稳定、有效地提取特征矢量,从而便雷达式生命探测仪对生命体进行较准确地识别。     

应用MATLAB获取超声多普勒音频信号频谱的方法研究

超声多普勒血流检测信号的频谱以及最大频率曲线包含着重要的血流生理参数和动力学参数,能反映血流状况以及血管痰病信息.本文提出了应用matlab获取超声多普勒音频信号频谱的方法,从而进一步获得最大频率曲线等信息.     

The effects of levodopa and ongoing deep brain stimulation on subthalamic beta oscillations in Parkinson's disease

Local field potentials (LFPs) recorded through electrodes implanted in the subthalamic nucleus (STN) for deep brain stimulation (DBS) in patients with Parkinson's disease (PD) show that oscillations in the beta frequency range (8-20 Hz) decrease after levodopa intake. Whether and how DBS influences the beta oscillations and whether levodopa- and DBS-induced changes interact remains unclear. We examined the combined effect of levodopa and DBS on subthalamic beta LFP oscillations, recorded in nine patients with PD under four experimental conditions: without levodopa with DBS turned off; without levodopa with DBS turned on; with levodopa with DBS turned on; and with levodopa with DBS turned off. The analysis of STN-LFP oscillations showed that whereas levodopa abolished beta STN oscillations in all the patients (p = 0.026), DBS significantly decreased the beta oscillation only in five of the nine patients studied (p = 0.043). Another difference was that whereas levodopa completely suppressed beta oscillations, DBS merely decreased them. When we combined levodopa and DBS, the levodopa-induced beta disruption prevailed and combining levodopa and DBS induced no significant additive effect (p = 0.500). Our observations suggest that levodopa and DBS both modulate LFP beta oscillations.     

Software Development for the Analysis of Heartbeat Sounds with LabVIEW in Diagnosis of Cardiovascular Disease

In this paper, a time-frequency spectral analysis software (Heart Sound Analyzer) for the computer-aided analysis of cardiac sounds has been developed with LabVIEW. Software modules reveal important information for cardiovascular disorders, it can also assist to general physicians to come up with more accurate and reliable diagnosis at early stages. Heart sound analyzer (HSA) software can overcome the deficiency of expert doctors and help them in rural as well as urban clinics and hospitals. HSA has two main blocks: data acquisition and pre-processing, time–frequency spectral analyses. The heart sounds are first acquired using a modified stethoscope which has an electret microphone in it. Then, the signals are analysed using the time–frequency/scale spectral analysis techniques such as STFT, Wigner–Ville distribution and wavelet transforms. HSA modules have been tested with real heart sounds from 35 volunteers and proved to be quite efficient and robust while dealing with a large variety of pathological conditions.     

Determination of carotid disease with the application of STFT and CWT methods

In this study, Doppler signals were recorded from the output of carotid arteries of 40 subjects and transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the subjects, and then analyzed by using short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) methods to obtain their sonograms. These sonograms were then used to determine the relationships of applied methods with medical conditions. The sonograms that were obtained by CWT method gave better results for spectral resolution than the STFT method. The sonograms of CWT method offer net envelope and better imaging, so that the measurement of blood flow and brain pressure can be made more accurately. Simultaneously, receiver operating characteristic (ROC) analysis has been conducted for this study and the estimation performance of the spectral resolution for the STFT and CTW has been obtained. The STFT has shown a 80.45% success for the spectral resolution while CTW has shown a 89.90% success.     

体表胃电信号的时-频域分析

把短时傅里叶变换用于体表胃电时-频域分析,结果表明,利用短时傅里叶变换等时-频域分析,可以同时得到体表胃电信号的时域幅度变化和能量分布特征.