生物医学信号的广义时域分析

广义时频表示方法在处理非平稳信号中发挥了越来越重要的作用，并在众多领域中有广泛的应用前景。本文在介绍时频分析的基本方法之后，综述了该方法在生物医学信号（如：超声多普勒血流信号、心音、心血管音、肌音、脑电及诱发电位、心电、晚电位等）处理中的应用。     

肺音信号检测处理及其临床应用研究

近年来,随着传感器技术、电子技术、信号处理技术等的迅速发展,国外医学界对肺音发生与传播、肺音检测与处理、肺音临床应用等方面进行了许多研究,取得了较大进展。本文主要综述了肺音信号检测处理与肺音临床应用等方面的新进展。在肺音检测方面,涉及肺音检测用传感器及肺音中噪声的滤波技术等。在信号处理方面,涉及肺音信号特征与肺音分析的振幅分析、时域分析、频域分析等方法。并指出,开发适用于临床的肺音计,将有助于对肺音作全面分析,有助于肺音的广泛应用。最后,介绍了肺音在临床呼吸监护方面的应用。     

基于变分模态分解的肺音去噪算法

目的：为有效提高肺音信号质量,提出一种基于变分模态分解的肺音去噪方法。方法：首先利用经验模态分解对带噪肺音信号进行分解,根据本征模态函数特征确定最佳分解层数,然后根据分解层数对原始带噪肺音进行变分模态分解处理,接着根据皮尔逊系数选取有用模态,最后采用阈值方法对各模态函数去噪,重构后得到没有噪声干扰的肺音信号。结果：通过与维纳滤波和FIR滤波进行对比,本文方法的语音质量感知评价、短时间客观可读性和源信号失真比均更优。结论：本文方法能有效对肺音信号进行去噪处理。     

基于小波变换的吞水音信号分析算法的研究

目的:吞水音信号的处理和特征提取。方法:本论文利用小波阈值算法对吞水音信号进行处理;并且通过带通滤波,利用小波变换的多分辨率分析对信号进行分析。结果:通过信号处理前后对比和分析,小波变换对吞水音信号的处理是有效的;并提出用咽部到贲门后音节的时间代替吞程来作为诊断的一个参数。结论:吞水音的分析在贲门癌的诊断中具有重要价值,利用小波变换,可以有效地对信号进行处理和分析,有助于贲门癌的诊断。     

肺音信号的自回归谱谱阵分析

本文应用自回归模型方法研究了各种肺音信号的ＡＲ谱谱阵，并与傅里叶功率谱谱阵作了对比，探讨了两种谱阵的特点与差异，实验结果表明：肺泡呼吸音ＡＲ模型阶次为６阶，气管音ＡＲ模型阶次为１４阶，哮鸣音与喘鸣音的ＡＲ模型阶次分别为１２阶与６阶，正常呼吸音（肺泡呼吸音与气管音）功率谱为连续谱，异常连续性肺音（哮鸣音与喘鸣音）功率谱为离散线状谱，音肺信号谱阵方法可显示出肺音的时变特性，有利于对肺音作动态分析。     

基于MyDAQ和LabVIEW的胎心音实验系统

目的：指出胎儿监护的重要性和通过检测胎心音实现胎儿监护的重要价值，设计一个胎心音实验平台，实现胎心音采集和性质分析。方法：首先依据WHO的调查，世界每年有数百万胎儿死亡，若孕妇在怀孕期间按周期做胎儿健康监护，则可以明显降低婴儿的死亡率，简述了胎儿健康监护的重要意义；然后比较多种胎儿监护方法，指出胎心音图法具有更重要的价值。最后论文分析了胎心音的产生和基本特点，基于MyDAQ和LabVIEW建立了一个胎心音实验系统。结果：该实验系统实现了胎心音的采集、WAV格式存储和读取。时变滤波器和小波除噪的预处理，以及基于TSA的胎心音性质分析，得出胎心音的性质与文献研究一致。结论：该系统廉价、快速原型化、灵活的优势，以及能够开放的特点，不仅能用于胎心音的相关研究，还适用于语音等信号的采集和分析。     

基于coif5小波的多普勒胎心音信号提取算法的研究

胎心率监护是围产期胎儿监护的关键技术指标，超声多普勒测量胎心率是最常用的无创方法。由于胎心多普勒信号具有信噪比低、非平稳的随机性特点，提出基于coif5小波，结合双重阈值方法的胎心音信号提取算法。实验表明，该算法有效地解决了由于胎心率加倍、减半所引起的胎心率曲线翻转问题，提高了多普勒胎心音信号提取的准确性。     

间质性肺炎患者肺音的小波分析

目的：探索一种间质性肺炎患者肺音的的定量化评估分析方法。方法：采集第三军医大学第三附属医院呼吸科2名间质性肺炎患者的肺音,对肺音信号进行小波分析,首先进行归一化处理,然后采用db3小波进行6尺度小波分解,计算肺音小波分解各分量的能量。结果：间质性肺炎患者的爆烈音主要集中在小波分解的D3,D4和D5分量中。患者A治疗前肺音及治疗半年后的D3、D4和D5的能量和的平均值分别为0．25和0．057。患者B是在同一时期记录不同部位的肺音,患者的右上背与右下背记录的肺音D3＋D4＋D5的能量和均值分别为0．085和0．128,右下肺较右上肺的病情更严重。结论：肺音属于非平稳的随机信号,在时域和频域都有特征信息,适合采用小波时频分析方法来进行信号处理。肺音小波分量D3,D4和D5的能量和可以在一定程度反映爆烈音的多少,从而反映间质性肺炎患者的病情严重程度的不同。肺音检测安全,方法简单,成本低,便于重复检测,适合于监测间质性肺炎患者的病情程度。     

远程会诊系统中生理信号实时采集传输系统的设计与实现

目的:研究远程会诊系统中人体生理信号的实时采集与传输,用于对患者进行实时远程物理检查,辅助远程诊断。方法:应用传感器获取生理信号,用LabWindows/CVI编程,实现远程会诊中心音、呼吸音和脉搏的实时采集和传输。结果:远程会诊专家能够实时对远程患者进行心音、呼吸音和脉搏等物理检查,并将诊断结果实时反馈给患者。结论:在现有的网络条件下,系统可以很好的实现生理信号的实时采集与传输,具有较强的实用性。     

一种用于喉部疾病语声诊断的新的声带模型

本文提出了一种新的声带模型－非对称四质量块声带模型，给出了它的数学表达式和等效电路，说明了在ＩＢＭ－ＰＣ微机上利用已知的语音信号拟合声带参数的方法，改进了语音嘶哑度的衡量方法，应用该模型对各种正常音和１６种典型喉部疾病患者手术前后的嘶哑语音进行分析比较，结果表明此模型的分析方法可作为喉部疾病诊断和嘶音分析的有效手段。     

心音信号的非高斯AR模型双谱分析

本文利用高阶谱分析方法 ,提出以非高斯参数模型对心音信号进行双谱分析与分类。采用具有非高斯白噪声激励的参数模型对心音 (PCG)信号进行建模 ,由导出的基于累积量的三阶递推方程估计模型参数 ,并对心音信号进行参数化双谱估计。其次 ,对心音信号的非高斯AR模型的定阶进行讨论 ,提出采用双谱互相关法估计心音信号的模型阶次。另外 ,利用非高斯AR模型参数构成特征向量 ,对正常心音和病理性心音进行二类模式分类。最后 ,对实际心音信号进行分析 ,结果表明利用双谱技术分析心音信号是一种新的有效方法 ,有助于心音信号的定量分析并为某些心脏疾病的早期诊断提供新的辅助信息。     

A review of signal processing techniques for heart sound analysis in clinical diagnosis

This paper presents an overview of approaches to analysis of heart sound signals. The paper reviews the milestones in the development of phonocardiogram (PCG) signal analysis. It describes the various stages involved in the analysis of heart sounds and discrete wavelet transform as a preferred method for bio-signal processing. In addition, the gaps that still exist between contemporary methods of signal analysis of heart sounds and their applications for clinical diagnosis is reviewed. A lot of progress has been made but crucial gaps still exist. The findings of this review paper are as follows: there is a lack of consensus in research outputs; inter-patient adaptability of signal processing algorithm is still problematic; the process of clinical validation of analysis techniques was not sufficiently rigorous in most of the reviewed literature; and as such data integrity and measurement are still in doubt, which most of the time led to inaccurate interpretation of results. In addition, the existing diagnostic systems are too complex and expensive. The paper concluded that the ability to correctly acquire, analyse and interpret heart sound signals for improved clinical diagnostic processes has become a priority.     

A review of signal processing techniques for heart sound analysis in clinical diagnosis

This paper presents an overview of approaches to analysis of heart sound signals. The paper reviews the milestones in the development of phonocardiogram (PCG) signal analysis. It describes the various stages involved in the analysis of heart sounds and discrete wavelet transform as a preferred method for bio-signal processing. In addition, the gaps that still exist between contemporary methods of signal analysis of heart sounds and their applications for clinical diagnosis is reviewed. A lot of progress has been made but crucial gaps still exist. The findings of this review paper are as follows: there is a lack of consensus in research outputs; inter-patient adaptability of signal processing algorithm is still problematic; the process of clinical validation of analysis techniques was not sufficiently rigorous in most of the reviewed literature; and as such data integrity and measurement are still in doubt, which most of the time led to inaccurate interpretation of results. In addition, the existing diagnostic systems are too complex and expensive. The paper concluded that the ability to correctly acquire, analyse and interpret heart sound signals for improved clinical diagnostic processes has become a priority.     

环境声信号库的建立

利用微机对１３８种环境声信号进行了采集、数据存储入库、声图分析，绘制了彩色三维声图及频谱图，并对频谱分析参数进行了统计处理。本数据库的建立为进行环境声信号研究分析和聋人的感觉代偿研究，提供了基础资料和手段。     

一种新呼吸音信号特征提取方法与应用

目的:通过研究呼吸音信号与呼吸系统疾病内在联系,对呼吸音特征进行提取与分类,为研制便携家用型呼吸音远程监测移动装置进行技术准备。 方法:对采集到的呼吸音进行预处理分析,从处理后的呼吸音原始数据中提取短时能量与短时过零率的特征值。 结果:通过不同时间段呼吸音信号能量的高低来显示特征差异,并且呼吸音的高低频异常信号对特征提取方法影响较小。 结论:本文方法可简单有效地提取特征值,不仅简化了特征识别数据处理过程,而且提取到的特征参数满足了差异性、统一性及相关性等基本特征。为构建神经网络的输入提供了理论依据与实际数据支撑。     

Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique

During the recording time of lung sound (LS) signals from the chest wall of a subject, there is always heart sound (HS) signal interfering with it. This obscures the features of lung sound signals and creates confusion on pathological states, if any, of the lungs. A novel method based on empirical mode decomposition (EMD) technique is proposed in this paper for reducing the undesired heart sound interference from the desired lung sound signals. In this, the mixed signal is split into several components. Some of these components contain larger proportions of interfering signals like heart sound, environmental noise etc. and are filtered out. Experiments have been conducted on simulated and real-time recorded mixed signals of heart sound and lung sound. The proposed method is found to be superior in terms of time domain, frequency domain, and time-frequency domain representations and also in listening test performed by pulmonologist.     

Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique

During the recording time of lung sound (LS) signals from the chest wall of a subject, there is always heart sound (HS) signal interfering with it. This obscures the features of lung sound signals and creates confusion on pathological states, if any, of the lungs. A novel method based on empirical mode decomposition (EMD) technique is proposed in this paper for reducing the undesired heart sound interference from the desired lung sound signals. In this, the mixed signal is split into several components. Some of these components contain larger proportions of interfering signals like heart sound, environmental noise etc. and are filtered out. Experiments have been conducted on simulated and real-time recorded mixed signals of heart sound and lung sound. The proposed method is found to be superior in terms of time domain, frequency domain, and time–frequency domain representations and also in listening test performed by pulmonologist.     

心音信号分段规则的探讨

目的 如何有效提取心音信号的有效成分（第一心音S1、第二心音S2）是分析心音信号的关键。为提取心音信号的有效成分,必须明确心音信号的分段规则。方法 首先对目前心音研究领域中常用的两类心音分段方法进行分析和比较。根据现有文献资料,结合作者对实际采集的829例心音实例的研究,提取心音的各时域特征并进行统计分析,最后对心音信号分段规则进行了探讨。结果 心音信号的S1、S2及收缩期、舒张期等时域特征呈现一定规律性。结论 可按照上述时域特征对心音信号进行自动分段,并借助心音分段规则进行进一步识别和分析。     

Directional dual-tree complex wavelet packet transforms for processing quadrature signals

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time–frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time–frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.