肺骨形成性气管病1例

患者男,62岁,以反复咳嗽、咳痰6年,症状加剧伴畏冷、发热7天住院。体检:体温385℃,血压16/8ｋＰａ,全身浅表淋巴结未触及肿大,右肺呼吸音减弱。Ｘ线片见右下肺片状影,右胸心膈角及膈面上见一肿块影,大小约5ｃｍ×7ｃｍ,拟诊:右下肺癌并感染。ＭＲＩ:右肺下叶及肺门均见不规则块影,提示右肺中心型肺癌并肺门转移。支气管镜检查:右下叶背段管口粘膜充血、水肿,管腔狭窄。刷片见炎症细胞,未找到癌细胞。于1997年7月18日行剖胸探查。术中见右胸腔内少量淡黄色渗出液,右上肺表面充血、水肿,见散在性肺大小泡,上叶外侧面见3ｃｍ×4ｃｍ纤维素渗出…     

小鼠急性心肌缺血ECG的小波分析

结扎小鼠冠状动脉前降支造成心肌缺血10min，采集小鼠在正常和急性心肌缺血下的ECG信号。采样频率选为500Hz，连续采集20s的数据。用具有对数频率分辨率的小波变换技术来分析ECG信号。选Mexican Hat小波为母函数，尺度因子α在区间[0．00125，2．5]取值，则相应的带通滤波器中心频率为200～0．1Hz。计算出ECG信号在相应尺度下的小波分解系数，然后求出在频率f处带宽为△f内的信号能量，得出了小鼠急性心肌缺血前后ECG信号能量随频率f的变化规律。结果表明：小鼠急性心肌缺血时ECG信号平均能量在0．1～1．0Hz和1～10Hz段是增加的，在10～200Hz段减少。     

原发性呼吸道淀粉样变二例

例 1男 ,5 6岁。因咳嗽、咳痰 2 0余天 ,加重伴发热 4ｄ于1999年 11月 2 9日入山东医科大学附属医院就诊。胸部ＣＴ示右肺下叶片状阴影 ,边缘不清 ,密度不均 ,考虑右肺下叶炎症。体检 :两肺呼吸音低 ,右下肺肩胛部深吸气时可闻及少许湿罗音。入院后行抗感染及对症治疗 2周后患者症状消失。但复查胸部ＣＴ仍显示两肺下叶炎症。为查明病因行纤维支气管镜活检术。病理检查 :术中发现右上支气管尖后段充血水肿明显 ,呈缝隙样狭窄 ,下叶粘膜苍白、变皱 ,呈放射状 ;左主气管开口轻度充血 ,舌叶支气管开口呈鸭嘴样狭窄 ,色泽正常 ,光滑 ,下叶背段关…     

皮肌炎患者外周血BDNF水平及受体TrkB在T淋巴细胞表达的研究

目的检测皮肌炎(DM)患者血清中脑源性神经营养因子(BDNF)水平及T淋巴细胞表达酪氨酸激酶B(TrkB)的情况。方法选取25例DM患者,健康对照28例,采用酶联免疫吸附方法检测DM组及健康对照组血清BDNF水平,流式方法检测两组外周血CD3~+CD4~+和CD3~+C D8~+T淋巴细胞表面TrkB的表达情况。根据肺受累情况将DM组分为合并间质性肺炎与不合并间质性肺炎两组。采用魏氏法收集DM患者血沉,免疫比浊法测定C反应蛋白、D二聚体与免疫球蛋白IgG、IgM、IgA,鞘流电阻抗法测定血淋巴细胞数与血小板数,磁珠凝固法测定纤维蛋白原,分析实验室指标与血清BDNF水平的相关性。结果 DM患者血清BDNF水平明显降低,合并间质性肺炎的DM组血清BDNF水平显著低于无间质性肺炎组。DM患者外周血CD3~+CD4~+和CD3~+C D8~+T淋巴细胞表面TrkB表达比例显著增加,DM患者血清BDNF水平与D-二聚体呈负相关,与淋巴细胞计数和血小板计数均呈正相关。结论血清BDNF水平和T淋巴细胞表面TrkB的表达比例可能成为DM患者疾病活动的血清学标记物,尤其是血清BDNF水平严重下降可能预示肺受累。     

原发性孤立性肺纤维瘤1例

患者男性 ,48岁 ,体检发现右上肺阴影 6年。近期反复出现右胸背部隐痛 ,于 2 0 0 0年 10月 30日入院。体检 :双肺呼吸音正常 ,胸片和ＣＴ示 :右上肺类圆形阴影 ,密度较高 ,轮廓清晰 ,有分叶 ,与 6年前胸片及ＣＴ片比较 ,右上肺阴影有增大趋势 ,于 2 0 0 0年 11月 12日做右上叶切除术。病理检查　送检右上叶肺组织 ,胸膜增厚、粘连 ,肿块位于右上叶前后段之间支气管旁肺实质内 ,2 5ｃｍ× 2 5ｃｍ×3ｃｍ大小 ,椭圆形 ,边界清 ,似有假包膜 ,切面灰黄色 ,质韧 ,肿块与支气管无明显关联。镜检 :肿瘤由梭形细胞组成 ,梭形细胞与不同比例的胶原…     

一种同时消除脉搏波信号中呼吸基线漂移和高频噪声的方法

目的：基于光电容积脉搏波可以实现血氧饱和度等人体生理参数的无创检测。基于光电容积脉搏波测量时,由于信号采集过程中存在人体呼吸和仪器本身热噪声等干扰,脉搏波信号中存在着呼吸基线漂移和高频噪声,影响最终的人体生理参数测量精度。方法：因此提出一种在经验模式分解的过程中结合小波变换的方法,来同时消除呼吸基线漂移和高频噪声的影响。首先通过经验模态分解将脉搏波信号分解为若干内在模式分量,并分别判断出含有呼吸基线漂移和代表高频噪声的分量,对于代表高频噪声的分量采用类似小波变换的方法进行滤波,利用小波变换将含有呼吸基线漂移的分量分解,将代表呼吸基线漂移的小波细节置零,信号重构后就达到了同时消除呼吸基线和高频噪声的目的。利用自行研制的测量装置采集的脉搏波信号进行实验验证,并采用信号交直流比R和信号的频谱进行效果评价。结果：有效地同时消除了呼吸基线漂移和高频噪声。结论：该方法将有利于血氧饱和度等人体生理参数无创检测精度的提高。     

基于功率谱特征的心电数据小波压缩方法

利用心电功率谱特征,探索心电数据压缩新方法。用小波分解心电信号为高频与低频分量,对低频分量继续分解达到要求的级数,对高频分量则根据其所在频段的能量,对临床诊断的价值加以取舍。对MIT生理信号数据库心电数据的压缩与还原分析表明,该方法平衡了压缩比与还原精度之间的矛盾,既具有较高的压缩比,又具有较高的还原精度,而且对信号的适应性也明显增强。另外,该压缩方法还具有一定的去噪作用。说明结合心电功率谱特征与小波变换方法压缩心电有其优势。     

脑卒中伴慢性阻塞性肺疾病患者应用ACEI对并发吸入性肺炎的影响

目的观察合并高血压的脑卒中并发稳定期慢性阻塞性肺疾病患者应用血管紧张素转换酶抑制剂（ACEI）类降压药对其预防吸入性肺炎的影响。方法将120例合并高血压非昏迷且年龄≥60岁的脑卒中伴稳定期慢性阻塞性肺疾病患者分为ACEI组和对照组,ACEI组患者应用ACEI类降压药（卡托普利）治疗,对照组应用其它类型的降压药,观察两组在住院期间吸入性肺炎等并发症的发生情况。结果在住院期间ACEI组有4例患者（6．7％）并发吸入性肺炎,而对照组有13例患者（21．7％）合并吸入性肺炎,两组相比差异有统计学意义（P〈0．05）。ACEI组有11例（18．3％）患者出现不同程度咳嗽,其中4例因持续性干咳而停药。ACEI组住院期间患者死亡率为3．3％,而对照组患者死亡率为5％,两组差异无统计学意义。结论预防性应用ACEI类降压药．可降低合并高血压非昏迷老年脑卒中伴稳定期慢性阻塞性肺疾病患者并发吸入性肺炎的发生率。     

支气管肺隔离症1例

患者男性 ,6 7岁 ,因刺激性咳嗽 1个月、痰中带血 1周入院 ,起病缓慢 ,逐渐加重 ,查体未见其它阳性体征。X线示 :右下肺外野高密度块影 ,与周围边界模糊 ,4 2cm× 2 5cm ,主动脉迂曲增厚 ,右侧膈面毛糙 ,肋膈角模糊。CT示 :右上肺前外侧段结节影 ,形态不规则 ,边缘不光滑 ,密度均匀 ,周围可见小毛刺 ,向外侧与侧胸壁粘连 ,纵隔稍向左移位 ,考虑为周围性肺癌。术中见病变位于右肺下叶基底段 ,4 0cm× 1 8cm× 1 5cm ,肺门及纵隔未见肿大淋巴结 ,右肺表面可见多个直径 0 5～ 0 8cm的肺大泡 ,包块上端有部分组织与右上肺相连 ,右侧缘有…     

基于能量估计的小波阈值与经验模态分解相结合的滤波方法研究

本文针对基于经验模态分解(EMD)的时空滤波器存在的固有模态函数分量中频率混叠交叉,导致有用信号与噪声一起被滤除的问题,结合小波在时间、尺度两域表征信号局部特征的特性,提出了一种基于能量估计实现EMD分解层数确定,小波变换阈值处理与EMD相结合的时空滤波方法。该方法既利用小波变换多分辨率的特性,又结合EMD的自适应分解与希尔伯特(Hilbert)谱分析中瞬时频率与能量意义的关系,从而解决了有用信号在滤波时被削弱的问题。以MIT/BIH标准心电数据库数据为对象的实验结果表明,该方法对于生理信号这一类强噪声下的微弱信号是一种有效的数据处理方法。     

Neural classification of lung sounds using wavelet coefficients

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.     

Heart sound cancellation from lung sound recordings using time-frequency filtering

During lung sound recordings, heart sounds (HS) interfere with clinical interpretation of lung sounds over the low frequency components which is significant especially at low flow rates. Hence, it is desirable to cancel the effect of HS on lung sound records. In this paper, a novel HS cancellation method is presented. This method first localizes HS segments using multiresolution decomposition of the wavelet transform coefficients, then removes those segments from the original lung sound record and estimates the missing data via a 2D interpolation in the time-frequency (TF) domain. Finally, the signal is reconstructed into the time domain. To evaluate the efficiency of the TF filtering, the average power spectral density (PSD) of the original lung sound segments with and without HS over four frequency bands from 20 to 300 Hz were calculated and compared with the average PSD of the filtered signals. Statistical tests show that there is no significant difference between the average PSD of the HS-free original lung sounds and the TF-filtered signal for all frequency bands at both low and medium flow rates. It was found that the proposed method successfully removes HS from lung sound signals while preserving the original fundamental components of the lung sounds.     

Automatic breath and snore sounds classification from tracheal and ambient sounds recordings

In this study respiratory sound signals were recorded from 23 patients suspect of obstructive sleep apnea, who were referred for the full-night sleep lab study. The sounds were recorded with two microphones simultaneously: one placed over trachea and one hung in the air in the vicinity of the patient. During recording the sound signals, patients’ Polysomnography (PSG) data were also recorded simultaneously. An automatic method was developed to classify breath and snore sound segments based on their energy, zero crossing rate and formants of the sound signals. For every sound segment, the number of zero crossings, logarithm of the signal's energy and the first formant were calculated. Fischer Linear Discriminant was implemented to transform the 3-dimensional (3D) feature set to a 1-dimensional (1D) space and the Bayesian threshold was applied on the transformed features to classify the sound segments into either snore or breath classes. Three sets of experiments were implemented to investigate the method's performance for different training and test data sets extracted from different neck positions. The overall accuracy of all experiments for tracheal recordings were found to be more than 90% in classifying breath and snore sounds segments regardless of the neck position. This implies the method's accuracy is insensitive to patient's position; hence, simplifying data analysis for an entire night recording. The classification was also performed on sounds signals recorded simultaneously with an ambient microphone and the results were compared with those of the tracheal recording.     

Pulmonary Crackle Detection Based on Fractional Hilbert Transform

Crackles are an important kind of abnormal and discontinuous lung sounds, which have been found to be correlated to types of pulmonary diseases. The purpose of this work is to show a new perspective to solve the problem of crackle detection, based on an emerging theory of fractional Hilbert transform. By applying fractional Hilbert transform to lung sound signals, a two-dimension texture image can be generated. The texture features corresponding to crackles are quite easy to be extracted.Experiments illustrate the effectiveness of our method.     

Heart sound and lung sound separation algorithms: a review

Breath and cardiac sounds are two major bio sound signals. In this, heart sounds are produced by movement of some body parts such as heart valve, leaflets and the blood flow through the vessels, whereas lung sounds generates due to the air in and out flow through airways during breathing cycle. These two signals are recorded from chest region. These two signals have very high clinical importance for the patient who is critically ill. The lung functions and the cardiac cycles are continuously monitored for such patients with the help of the bio sound signal captured using suitable sensing mechanism or with auscultation techniques. But these two signals mostly superimpose with each other, so the separation of these heart sound signals (HSS) and the lung sound signals (LSS) is of great research interest. There are so many different techniques proposed for this purpose. In this paper, a study is carried out on different algorithms used for the separation of HSS from LSS, and also the results of major four separation algorithms are compared.     

Filtering and classification of phonocardiogram signals using wavelet transform

Auscultation is a technique in which a stethoscope is used to listen to the sounds of the heart. Structural defects of the heart are often reflected in the sounds the heart produces, and auscultation provides clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as clinical tool, it is difficult to analyse heart sound signals in the time or frequency domain. Thus phonocardiogram (PCG), recording of heart sounds has many advantages over traditional auscultation, in that they may be replayed and analysed for time and frequency information. Using discrete wavelet transform, the signal is decomposed and reconstructed without significant loss of information in the signal content. The error of rebuilding can be considered as an important parameter in the classification of the pathological severity of the phonocardiogram signals. Variation of this parameter is very sensitive to the murmur importance in PCG signals.     

Filtering and classification of phonocardiogram signals using wavelet transform

Auscultation is a technique in which a stethoscope is used to listen to the sounds of the heart. Structural defects of the heart are often reflected in the sounds the heart produces, and auscultation provides clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as clinical tool, it is difficult to analyse heart sound signals in the time or frequency domain. Thus phonocardiogram (PCG), recording of heart sounds has many advantages over traditional auscultation, in that they may be replayed and analysed for time and frequency information. Using discrete wavelet transform, the signal is decomposed and reconstructed without significant loss of information in the signal content. The error of rebuilding can be considered as an important parameter in the classification of the pathological severity of the phonocardiogram signals. Variation of this parameter is very sensitive to the murmur importance in PCG signals.     

基于功率谱信息熵的异常心音识别

目的：异常心音识别是心血管疾病检测的一种重要手段,为了探究异常心音频域的有用信息,提出了将不同频段的功率谱作为一个独立信源计算其信息熵,从而对房室瓣和动脉瓣异常信号进行判别的一种新方法。方法：实验先将心音信号进行小波包分解,然后利用改进的Welch方法计算信号的功率谱,进而求各频段的功率谱信息熵,再建立支持向量机预测模型来对两种异常心音进行识别。结果：选取二尖瓣狭窄、二尖瓣关闭不全、主动脉瓣狭窄、主动脉瓣关闭不全共27例心音信号进行算法仿真,其中房室瓣异常能够全部检测出来,动脉异常有3例被误判,正确率达到77％;在原有27例信号的基础上,增加3例房室瓣异常和3例动脉异常信号进行算法验证,房室瓣异常信号仍然能够全部被检测出来．动脉异常信号2例被误判。结论：从仿真结果可以看出,该算法对房室瓣异常和动脉异常两种心音信号有较高的识别率。尤其对房室瓣杂音能够完全识别,也表明功率谱信息熵在异常心音的识别中具有重要意义。     

The effectiveness of the wavelet transforms method in the heart sounds analysis

Heart sounds can be used more efficiently by medical doctors when they are displayed visually, rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and non-stationary that they are very difficult to analyse in time or frequency domains. We have studied the extraction of features from heart sounds in the time-frequency domain for recognition of heart sounds through time-frequency analysis. The application of wavelet transform for the heart sounds is thus described. The performance of continuous wavelet transform, discrete wavelet transform and packet wavelet transform is discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify clinical usefulness of our extraction methods for recognition of heart sounds.     

多通道肺音采集系统设计

目的：设计一种多通道肺音采集系统。方法：系统采用增强型的32位基于ARM核心的微控制器STM32F103ZET6作为核心控制芯片。电容传声器将采集到的肺音信号转换为电信号,设计多路放大滤波电路对肺音电信号进行处理,微控制器通过控制4通道串行输出AD转换芯片ADS8341实现多通道数据采集,采用SD卡作为存储介质用以存储所采集的肺音信号,采用液晶显示器作为人机界面,显示操作提示信息或系统工作状态。结果：可以同时采集1-4通道的肺音数据,采样频率为8789 Hz,数据采样精度为16位,单次可连续存储30s-60s的肺音数据。结论：系统操作简便,体积较小,肺音数据以WAV格式的音频文件存储于SD卡中。计算机可以方便地读取SD卡中肺音数据,进行肺音音频播放,还可以对肺音数据进行进一步的分析处理。系统能准确地采集多通道肺音数据,数据存储快速、可靠,数据读取方便,能满足临床医生采集肺音数据的需求。