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。     

小波变换去除心电信号中呼吸信号干扰

目的 研究用小波变换去除心电图信号中呼吸信号的方法。方法 采用db4小波对采样频率为200Hz的心电图信号作离散小波变换的多层分解，并与呼吸信号的频率成分比较，发现呼吸信号分布在心电图信号分解后第8、9、10层细节中，去除这些成分和高频干扰，对剩下的分量重构。结果 比较成功地纠正了心电信号的基线，去除了低频呼吸信号的干扰。结论 小波变换的方法能够去除心电信号中的呼吸信号干扰。     

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。     

心音信号的聚类分析

通过对ＡＲＴ２神经网络进行改进，构造了适合于对信号的时变谱进行分类的二维ＡＲＴ神经网络，而后根据心音信号的特点，我们选择了心音的小波变换谱作为网络的输入特征。调整网络的参数，使之能够将输入的心音信号特征聚类成为两类，实验结果表明我们所设计的分类系统能够从连续３２个正常人的心音信号中区分出两个正常分裂的心音。这个结果在基础生理研究和临床诊断上都有一定的应用价值。     

ECG信号的小波变换检测方法

本文反小波变换应用于ＥＣＧ信号的ＱＲＳ波检测。利用二进样条小波对信号按Ｍａｌｌａｔ算法进行变换：从二进小波变换的等效滤波器的角度，分析了信号奇异点（Ｒ峰点）与其小波变换模极大值对的零交叉点的关系。在检测中运用了一系列策略以增强算法的抗干扰能力、提高ＱＲＳ波的正确检测率。经ＭＩＴ／ＢＩＨ标准心电数据库检测验证，ＱＲＳ波正确检测率高达９９．８％。     

生物医学信号的广义时频分析

广义时颁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。     

心电信号的小波变换滤波算法的改进

对心电信号的滤波算法进行了改进。在利用小波变换实现心电图信号滤波算法的基础上，增加了对2^3尺度下小波分解所得细节信号的模极大值对的检测功能，以修复因滤波受损的心电信号的QRS波。经MIT／BIH标准心电数据库验证，试验表明，该方法行之有效。     

基于混合小波变换的瞬态信号检测方法

探讨了信号的小波变换与匹配滤波的关系,指出小波变换（WT）实际上就是可变检测模板的匹配滤液过程。根据这一思想,提出了基于混合小波的信号检测方法。本文中,“混合小波变换”是指在小波分解和重构中分别使用不同的基本小波。其中分解小波用于实现可变模板的信号检测,重构小波则用以增强被检测信号的特征。我们用该方法对实测脑电信号（EEG）中瞬态脉冲干扰进行检测。实验结果表明该方法能有效地检测出EEG中的瞬态脉冲。     

心音信号检测的一种新方法

本文为实现第一心音（Ｓ１）和第二心音（Ｓ２）的定位的一种新方法，对心音图（ＰＣＧ）信号进行预处理后，再利用小波变换原理检测信号的奇异点，达到准确定位的目的。这一检测方法已在心功能仪上成功地应用于测试Ｑ－Ｓ１和Ｑ－Ｓ２间期。     

生物医学信号的小波分析方法

本比较了傅立叶变换、加窗傅立叶变换和小波变换等处理信号的方法优缺点,并在此基础上着重介绍了小波分析的基本概念及其在心电信号处理中的应用和实现方法。     

基于傅立叶变换的心磁信号减噪处理和心磁图分析

利用单通道直流高温超导磁强计(de-SQUID),在磁屏蔽室内测量心脏跳动产生的磁场信号.改变无磁床的位置得到胸前平面6×6的正方格子上各点的心磁信号,以同时记录的心电信号为时间基准,对心磁信号作平均处理.通过二维双线性插值,得到时域心磁分布图,可观察一个心动周期内的心磁分布变化.为了提高心磁信号的信噪比,我们在信号平均处理的基础上通过傅立叶滤波,消除电网干扰和高频噪声.快速傅立叶变换算法简单,效果显著.我们在此基础上观察到心动周期内心磁图QRS和T波段的反向现象.     

肺音信号的时变谱分析

肺音信号谱特性是肺音学研究的重要课题，本文利用ＦＦＴ谱分析技术，对正常肺音（肺泡呼吸音，气管音）与异常肺音（哮呜音，喘呜音）进行了时变谱研究，获得了上述各种肺音信号的谱特性，实验结果表明；时变谱分析是肺音分析的有效方法。     

正常人与冠心病患者的第三心音分析

研究第三心音 (S3)在临床上的意义。在心前区采集心音数据 ,并采集一导心电信号来定位心音。采样频率为10 3 Hz。用小波变换时频分析方法 ,研究了 135例 (正常少儿 34例、正常成年人 31例、正常老年人 30例和冠心病老年人 40例 )心音图中的S3。表明所有正常人均有S3;正常老年人与老年一般冠心病患者的S3存在明显差异 (p =0 .0 16 4) ,后者S3的频谱主峰频率明显上移。S3的检测有重要的临床诊断意义     

The Role of Phase Changes in Sound Signals in Localization of Sound Sources

The auditory system in humans and animals makes virtually no discrimination of phase changes in the structure of monaurally presented sound signals. However, electrophysiological studies have demonstrated marked changes in the responses of the central parts of the auditory system when the phase structure of the signal changes during presentation of the same type of stimulation. We have suggested that this inconsistency is due to the preparative role of phase effects during monaural stimulation for subsequent operations in the auditory system involved in determining the location of a sound source in space. This report presents experimental data on defined changes (increases in amplitude) in the electrical responses of the midbrain center of the auditory system (inferior colliculus) in antiphase binaural presentation of series of sound impulses (comparison with synphase presentation). These changes may be part of the mechanism underlying the interference resistance of the auditory system during determination of the location of a sound source (binaural release from masking). Neuronal cortical activity is sensitive and selective to dynamic interaural changes in the phase spectrum of the signal, which may provide the basis of the mechanism for locating a moving sound source. Auditory evoked potentials in humans demonstrate memorizing of the direction of movement of a sound image, as shown by the changes in parameters on presentation of stimuli of different locations (deviant stimuli) differing from the standard parameters of mismatch negativity.     

正常青年人短纯音诱发的耳声发射及幅值频谱分析

用1KHz短纯音测试正常青年42人耳诱发性耳声发射(EOAE)获得100%检出率。其阈值为3.18±8.54dB(SL),耳间阈差为5.71±4.27dB。本文用长延迟时间及短扫描时间窗观察EOAE的持续时间。正常青年人的EOAE持续时间>20ms者占78.71％。用快速傅里叶变换对8例EOAE行幅值频谱分析,发现正常EOAE的频谱主要分布在1～3 KHz范围内,其主峰频率均值为1362.50±229.92Hz。     

基于笔记本计算机的心音分析仪

基于笔记本计算机的心音分析仪由心音传感器、心音信号预处理盒,笔记本计算机、打印,音箱和心音信号处理软件组成。它的开发目的是充分利用笔记本计算机的便携特点和强大功能。该系统在Ｗｉｎｄｏｗｓ９５操作系统下用ＶＩＳＵＡＬＢＡＳＩＣ编程。     

基于非线性混沌的心律失常心音信号分析

本文基于非线性混沌理论,对正常及心律失常心音信号的关联维数及最大Lyapunov指数进行了计算和混沌特性分析,从而提出一种新的心律失常分析方法。对30例健康人和30例心律失常患者的分析结果显示,正常心音和心律失常心音的关联维数和最大Lyapunov指数具有显著性差异。由于心律失常心音信号时序上的不规则性,导致其可预测性下降,与正常心音信号相比,具有较高的复杂度,从而具有比正常心音更大的关联维数和最大Lyapunov指数值。故关联维数和最大Lyapunov指数可用于分析心律失常心音信号及其特征提取。     

Computer-Based Detection and Analysis of Heart Sound and Murmur

To develop a digital algorithm that detects first and second heart sounds, defines the systole and diastole, and characterises the systolic murmur. Heart sounds were recorded in 300 children with a cardiac murmur, using an electronic stethoscope. A Digital algorithm was developed for detection of first and second heart sounds. R-waves and T-waves in the electrocardiography were used as references for detection. The sound signal analysis was carried out using the short-time Fourier transform. The first heart sound detection rate, with reference to the R-wave, was 100% within 0.05–0.2R-R interval. The second heart sound detection rate between the end of the T-wave and the 0.6R-R interval was 97%. The systolic and diastolic phases of the cardiac cycle could be identified. Because of the overlap between heart sounds and murmur a systolic segment between the first and second heart sounds (20–70%) was selected for murmur analysis. The maximum intensity of the systolic murmur, its average frequency, and the mean spectral power were quantified. The frequency at the point with the highest sound intensity in the spectrum and its time from the first heart sound, the highest frequency, and frequency range were also determined. This method will serve as the foundation for computer-based detection of heart sounds and the characterisation of cardiac murmurs.     

Cough Sound Analysis Can Rapidly Diagnose Childhood Pneumonia

Pneumonia annually kills over 1,800,000 children throughout the world. The vast majority of these deaths occur in resource poor regions such as the sub-Saharan Africa and remote Asia. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths. The reliable diagnosis of childhood pneumonia in remote regions is fraught with difficulties arising from the lack of field-deployable imaging and laboratory facilities as well as the scarcity of trained community healthcare workers. In this paper, we present a pioneering class of technology addressing both of these problems. Our approach is centred on the automated analysis of cough and respiratory sounds, collected via microphones that do not require physical contact with subjects. Cough is a cardinal symptom of pneumonia but the current clinical routines used in remote settings do not make use of coughs beyond noting its existence as a screening-in criterion. We hypothesized that cough carries vital information to diagnose pneumonia, and developed mathematical features and a pattern classifier system suited for the task. We collected cough sounds from 91 patients suspected of acute respiratory illness such as pneumonia, bronchiolitis and asthma. Non-contact microphones kept by the patient’s bedside were used for data acquisition. We extracted features such as non-Gaussianity and Mel Cepstra from cough sounds and used them to train a Logistic Regression classifier. We used the clinical diagnosis provided by the paediatric respiratory clinician as the gold standard to train and validate our classifier. The methods proposed in this paper could separate pneumonia from other diseases at a sensitivity and specificity of 94 and 75% respectively, based on parameters extracted from cough sounds alone. The inclusion of other simple measurements such as the presence of fever further increased the performance. These results show that cough sounds indeed carry critical information on the lower respiratory tract, and can be used to diagnose pneumonia. The performance of our method is far superior to those of existing WHO clinical algorithms for resource-poor regions. To the best of our knowledge, this is the first attempt in the world to diagnose pneumonia in humans using cough sound analysis. Our method has the potential to revolutionize the management of childhood pneumonia in remote regions of the world.