短纯音诱发耳声发射的指数方法时频分析

目的在于使用指数分布方法计算来自于正常人耳短纯音诱发耳声发射（Ｔｏｎｅ－ｂｕｒｓｔＥｖｏｋｅｄＯｔｏａｃｏｕｓｔｉｃＥｍｉｓｓｉｏｎｓ,ＴＢＯＡＥｓ）的时频分布。对耳声发射的定量分析依赖于谱方法,而ＴＢＯＡＥｓ是非平稳信号,因此传统的谱分析方法已不能满足要求,指数分布能很好地给出ＴＢＯＡＥｓ的时频表示。我们根据对仿真信号及实测ＴＢＯＡＥｓ的计算结果,分析了其时频分布的特点,并对不同的频率成份与潜伏期的关系进行了描述。     

耳声发射的听觉外周系统主动同态模型

给出了听觉外周系统的主动性模型。该模型是建立在声电等效理论基础上的同态模型。它共分成三个部分：耳道、中耳及内耳耳蜗。整体模型中主动性特征的引入是通过耳蜗部分的受控源来体现的。计算结果表明,该模型能够较好地仿真两种重要的耳声发射－ＴＥＯＡＥ及ＤＰＯＡＥ,且由此模型得到的ＴＥＯＡＥ及ＤＰＯＡＥ在波形的形态及数量关系上都与临床测量结果有较好的一致性     

瞬态刺激诱发耳声发射非线性特性的研究

耳声发射（ＯｔｏａｃｏｕｓｔｉｃＥｍｉｓｓｉｏｎｓ,ＯＡＥｓ）是目前对外周听觉系统是否完好无损评价的客观指标。瞬态刺激诱发的耳声发射（ＴｒａｎｓｉｅｎｔＥｖｏｋｅｄＯＡＥｓ,ＴＥＯＡＥｓ）在初始部分通常混杂有伪迹成份（主要指耳道对刺激声直接反射的回声）,这就使得ＯＡＥｓ的测量在临床使用中受到了很多的限制。因此,为了消除伪迹,一种称作“导出的非线性响应＂（ＤｅｒｉｖｅｄＮｏｎｌｉｎｅａｒｉｔｙＲｅｓｐｏｎｓｅ,ＤＮＬＲ）的非线性处理技术取代了传统的线性同步平均技术。本文的主要目的首先是根据听觉系统结构的特点建立了一个简单的模型,通过该模型解释了ＤＮＬＲ方法成立的第一个基本假设,然后根据对正常听力成年人耳朵的实测结果,计算了ＴＥＯＡＥｓ的输入／输出关系曲线,并证明了ＤＮＬＲ方法成立的第二个基本假设,最后对ＤＮＬＲ技术和同步平均技术进行了比较和分析,并且得到了一些有益的结论。     

豚鼠急性缺氧窒息后畸变产物耳声发射的改变

豚鼠急性缺氧窒息后畸变产物耳声发射的改变＊姜伟张敬人江平汪磊（海军总医院,北京１０００３７）畸变产物耳声发射（ＤｉｓｔｏｒｔｉｏｎＰｒｏｄｕｃｔＯｔｏａｃｏｕｓｔｉｃＥｍｉｓｉｏｎ,ＤＰＯＡＥ）是诱发性耳声发射中的一种,其来源于耳蜗已被研究者证实,为...     

耳声发射信号的小波分析及应用

为了利用瞬态诱发耳声发射信号,对耳蜗性和蜗后性两种感音神经性耳聋进行诊断和定位,提出了一种基于小波变换的方法,用于对感音神经性耳聋病变进行诊断和定位。首先,获得听力正常耳信号的小波变换的对数均方根－频率曲线范围,及在对侧刺激声作用下,信号受抑制的小波变换的对数均方根－频率曲线范围;然后根据某一受试耳有无对侧刺激声作用下的瞬态诱发耳声发射信号,得出其小波变换的对数均方根曲线及受抑制的对数均方根曲线。最后,检测受试耳的两条曲线是否分别在正常值范围内,由此对受试耳的耳蜗及内侧橄榄耳蜗系统功能进行检测,同时根据每一条曲线将蜗性及蜗后病变定位到某一细致的频带。此方法与目前临床上采用的耳声发射结合ABR和纯音测听的诊断方法能很好地吻合。     

瞬态诱发耳声发射分形特性研究

耳声发射的产生是耳蜗非线性动力学机制作用的结果 ,是外毛细胞的非线性生物机构放大过程的一种能量泄露。我们将分形理论用于对耳声发射信号波形形态的研究 ,并通过基于数学形态学的分形维数计算方法对数例瞬态诱发耳声发射信号进行了分析。实验结果表明该信号具有较好的分形特性 ,为耳声发射进一步的深入研究和理解提供了一定的帮助。     

正常足月儿畸变产物耳声发射(DPOAEs)特性分析

研究提出正常足月儿畸变产物耳声发射（ＤＰＯＡＥｓ） 特性分析如下：１ ． ＤＰＯＡＥｓ 反应强度曲线,ＤＰ 图显示两个反应峰（ｆ２ ＝ １ ．６ 和５ ．０ ｋＨｚ） 和一个反应谷（ｆ２ ＝ ３ ．１ ～４ ．０ ｋＨｚ） ;２． ＤＰＯＡＥ 本底噪声及其特性,ｆ２ ＝ １．０ ｋＨｚ 其测试频率点（２ｆ１ －ｆ２） 本底噪声最高（Ｐ＜ ０ ．０５） ,ｆ２ ＝ ３ ．１ ,４．０ 和５ ．０ｋＨｚ ,测试频率点（２ｆ１ －ｆ２） 本底噪声较低（Ｐ＜ ０．０５） ;除ｆ２ ＝ ４．０ ｋＨｚ 外,ＤＰＯＡＥ 本底噪声与其反应强度均未呈现直线相关特性;３ ． ＤＰＯＡＥ ＳＮＲ 特性,ｆ２ ＝ １．０ ｋＨｚ 其ＳＮＲ 最小（Ｐ＝ ０．０００） ,ｆ２ ＝２ ．０ ｋＨｚ 其ＳＮＲ 最大（Ｐ０ ．００３） ;４． ＤＰＯＡＥ ＳＮＲ 和ＴＥＯＡＥ ＳＮＲ 相关特性,除１．０ ｋＨｚ 频段外,其余频段其二者间均有着非常显著的直线相关特性。     

诱发性耳声发射的掩蔽

３４例听觉正常受试者（共４８耳）进行疏波短声诱发性耳声发射（ＥＯＡＥ）掩蔽实验,项目包括同侧同时掩蔽、同侧后掩蔽和对倒后掩蔽。同时掩蔽的掩蔽声是稳态白噪声,后掩蔽的掩蔽声是宽带噪声。同侧同时掩蔽强度达３０ｄＢＳＬ时,未观察到对ＥＯＡＥ的掩蔽效应,但对主观听觉感受有掩蔽作用,表明ＥＯＡＥ的客观属性反映听觉行为有其局限性、同侧及对侧后掩蔽出现掩蔽效应时的掩蔽强度分别为３０和５０ｄＢＳＬ,掩蔽阈约分别为５９和６８ｄＢＳＬ。耳蜗的机械特性－非线性或耳蜗内存在的某种功能性的反馈调节系统可能是同侧后掩蔽的作用机理。下行的对侧橄榄耳蜗内侧束对外毛细胞主动收缩的抑制性作用,可有效解释对倒后掩蔽的ＥＯＡＥ变化。     

烫伤对大鼠下丘脑视上核内皮素-1基因转录和蛋白含量的影响

用原位杂交和免疫组织化学方法观察了烫伤后下丘脑视上核（ＳＯＮ）内皮素－１（ＥＴ－１）基因转录和蛋白含量的变化,并用通用图像颗粒分析法估计ＥＴ－１ ｍＲＮＡ阳性杂交信号的强度和ＥＴ－１样免疫反应物（ＥＴ－１－ｉｒ）的免疫反应强度。与对照组相比,烫伤后１５ｍｉｎ,ＳＯＮ神经元胞浆内ＥＴ－１ ｍＲＮＡ阳性杂交信号未见明显变化;而在烫伤后６０和１８０ｍｉｎ,ＥＴ－１ ｍＲＮＡ阳性杂交信号强度分别较比照组增     

浮游生物生物量指数作为水库营养状态和鱼产力评估指标的作用

浮游生物生物量指数作为水库营养状态和鱼产力评估指标的作用卢敬让,李德尚（青岛海洋大学,青岛,２６６００３）ＴＨＥＵＳＥＳＯＦＰＬＡＮＫＴＯＮＢＩＯＭＡＳＳＩＮＤＥＸ（ＢＩ）ＡＳＡＮＩＮＤＩＤＡＴＯＲＩＮＡＳＳＥＳＳＭＥＮＴＯＦＲＥＳＥＲＶＯＩＲＴＲＯ...     

Respiratory sound analysis in healthy and pathological subjects: A wavelet approach

In this paper we describe the application of a wavelet analysis-based method, to characterize the frequency power distribution of the unsteady respiratory sound signals in order to better discriminate the healthy state of a given subject. To evaluate the methodology, both normal tracheal sounds as well as adventitious respiratory sounds were investigated. In particular, our analysis shows the possibility to extract useful statistical information on the energy content and its mean frequency distribution giving us a quantitative characteristic hallmark of the respiratory pattern. The presence of sound anomalies can be pointed out through some specific patterns of the wavelet mean power spectra and thus the localization of the related quartiles which can be used as simple and efficient diagnostic indices. In this study the method has been applied in healthy subjects and patients with different respiratory diseases. Results show that different power spectra patterns characterize health from disease. Some preliminary results indicate also that pathological patterns can change as result of therapeutical interventions like mechanical ventilation.     

Mobile telephone use effects on peripheral audiovestibular function: a case-control study

Low level radio-frequency (RF) signals may produce disorientation, headache and nausea. This double blind study tested nine case-subjects, who complained of various symptoms after prolonged mobile telephone use and 21 control subjects. Each subject underwent a series of trials, in which a dummy mobile telephone exposure system was held to each ear for 30 min in (a) pulsed, (b) continuous RF emission or, (c) no emission test modes. In the active pulsed and continuous modes the same mean power as the output of a typical handset was delivered at a carrier frequency of 882 MHz and at a maximum specific absorption rate (SAR) value of 1.3 W kg(-1) (+/- 30%). In Experiment I (auditory), transient evoked otoacoustic emissions (TEOAE), which assess the outer hair cells in the inner ear, were conducted. In Experiment II (vestibular) the vestibulo-ocular reflex was recorded by video-oculography (VOG), at baseline and immediately post exposure. There were no significant TEOAE changes from baseline to post-exposure recording for any of the exposures and no significant differences in the TEOAEs' change from baseline to post exposure between cases and controls. The VOG did not identify any effect of the exposure on the vestibular end organ in either cases or controls. In conclusion, 30 min exposure to mobile phone RF did not show any immediate effects on vestibulocochlear function as measured by TEOAE and the VOR.     

Non-invasive diagnosis methods of coronary disease based on wavelet denoising and sound analyzing

The heart sound is the characteristic signal of cardiovascular health status. The objective of this project is to explore the correlation between Wavelet Transform and noise performance of heart sound and the adaptability of classifying heart sound using bispectrum estimation. Since the wavelet has multi-scale and multi-resolution characteristics, in this paper, the heart sound signal with different frequency ranges is decomposed through wavelet and displayed on different scales of the resolving wavelet result. According to distribution features of frequency of heart sound signals, the interference components in heart sound signal can be eliminated by selecting reconstruction coefficients. Comparing de-noising effects of four wavelets which are haar, db6, sym8 and coif6, the db6 wavelet has achieved an optimal denoising effect to heart sound signals. The de-noising result of contrasting different layers in the db6 wavelet shows that decomposing with five layers in db6 provide the optimal performance. In practice, the db6 wavelet also shows commendable denoising effects when applying to 51 clinical heart signals. Furthermore, through the clinic analyses of 29 normal signals from healthy people and 22 abnormal heart signals from coronary heart disease patients, this method can fairly distinguish abnormal signals from normal signals by applying bispectrum estimation to denoised signals via ARMA coefficients model.     

Energy-Based Wavelet De-Noising of Hydrologic Time Series

De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series'' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed.     

Dynamics of local maxima chains in spectra of human electroencephalogram

The continuous wavelet transform was applied to the human EEG signals recorded in different states of brain activity. The dynamics of local maxima chains in the matrices of the continuous wavelet transform coefficients was studied. The typologization method was developed for local maxima chains to separate by their drift in the frequency space as well as by dynamics of their signal “energy.” The method proved to be highly informative. It was shown that it was highly sensitive to a selection of one of two responses to the test question. It is determined that local maxima chains in most cases are gradually increasing and decreasing in the frequency space and by changes in the values of their continuous wavelet transform coefficients. The functional asymmetry in local maxima chains types’ distribution is determined. The results obtained allow us to consider the types of the local maxima chains dynamics as a new phenomenon of EEG activity.     

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.     

Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcrafi Unmanned Aerial Vehicle （RUAV） sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.     

飞行状态下四种菊头蝠回声定位声波的小波包识别方法

研究了飞行状态下的四种菊头蝠回声定位声波的识别方法.通过小波包分解得到各个频带能量作为识一别特征向量,用主成分分析法优化特征空间.提取少数几个主成分,这些主成分彼此不相关,符合特征优化的要求,以主成分向量作为BP神经网络的输入对蝙蝠的种类进行识别.个体识别正确率达到了80%以上,表明基于小渡包分解和神经网络识别的方法对蝙蝠回声定位声波进行识别是可行的.     

Comparison of transient otoacoustic emission responses from neonatal and adult ears.

Transient otoacoustic emission (TEOAE) responses from neonatal (age: 48 h) and adult subjects (age: 26.6 +/- 10.0 yr) were analyzed by the combined use of recurrence quantification analysis and singular value decomposition. The data from the two age groups showed significant differences and similarities. The neonatal responses presented less deterministic structures than those of the adults in terms of recurrent dynamic features. In both data sets, the same high level of individual specific dynamic features was observed. The results from the singular value decomposition analysis suggest that a large percentage of variability in all of the analyzed responses can be explained by four to five essential modes. This number is lower than that observed in simulated TEOAE responses generated by a five-component gammatone model. A possible explanation is presented, based on simple instrumental and morphoanatomic considerations.