小波变换在心电图ST段识别中的应用

目的:探讨基于小波变换的心电图ST段形态的识别算法.方法:首先利用二次样条小波对心电信号进行分解,并根据信号奇异点与其小波变换模极大值的对应关系,提出了在不同尺度下进行心电信号中关键特征点的提取策略;然后对ST段进行直线拟合,识别出ST段的形态;最后采用MIT/BIH标准心电数据库的数据进行检验.结果:利用作者所提算法编制的自动诊断软件能较准确地提取心电信号的特征点,成功识别了ST段的形态.结论:该自动分析算法可以提高ST段分析的准确性和可靠性,为临床诊断冠心病提供更准确的依据.     

基于差分方法的QRS波精确定位

目的:应用中值滤波改进QRS复合波检测方法,引入多个判据,精确定位QRS波型及边界?方法:在差分法基础上,首先消除心电信号中工频干扰?基线漂移等噪声,然后提取信号的幅度?斜率信息,依据自适应阈值跟踪波型起伏信息,获得QRS波精确信息?结果:应用LabVIEW软件实现检测系统,经MIT-BIH心电数据测试,正确率达99.69%?结论:实验表明可以有效提高检测能力,为临床心电图辅助诊断奠定基础?     

频域心电图在冠心病诊断中的应用

频域心电图(FCG)是一种无创伤性心电检测新技术,其特点是采用现代信息处理的方法,显示心电信号的频域特征,并对所选的两个导联心电信号的频率、振幅、相位和时差等进行多参数的相关分析。本文对79例冠心病患者和33例健康者进行了频域心电图分析,探讨其对冠心病的诊断价值。     

胎儿心电信号的无创提取：基于时间卷积编解码网络

目的 实现从孕妇腹壁混合心电信号中提取微弱的胎儿心电信号,为准确估计胎儿心率、分析胎儿心电波形等提供基础。方法 利用深度卷积网络（deep CNN）优越的非线性映射能力,本文提出了一种基于时间卷积编解码网络的非线性自适应噪声消除（nonlinear ANC）提取框架,以实现胎儿心电信号的有效提取。首先构建适用于处理胎儿心电信号的深度时间卷积网络（TCED-Net）模型作为非线性映射工具;然后以孕妇胸部心电信号为参考,利用该模型估计孕妇腹壁混合心电信号中的母体心电成分;最后从腹壁混合信号中减去所估计的母体心电成分,以得到完整的胎儿心电信号。实验利用合成心电数据（FECGSYNDB）和临床心电数据（NIFECGDB、PCDB）对方法性能进行测试与对比。结果 本文方法在FECGSYNDB上的胎儿R峰检测精度（[F1]值）、均方误差（MSE）和质量信噪比（qSNR）分别达到98.89 %,0.20和7.84;在NIFECGDB上的[F1]值达到99.1%;在 PCDB 上的[F1]值达到 98.61%。在不同数据集中较之 EKF（[F1=]93.84%）、ES-RNN([F1] =97.20% )和 AECG-DecompNet（[F1]=95.43%）等现有性能最佳的算法,本文方法的R峰检测精度指标分别高出5.05%、1.9%和3.18%,均优于现有最佳方法。结论 与现有算法相比,本文方法可以提取出更为清晰的胎儿心电信号,对孕期进行有效的胎儿健康监护具有一定的应用价值。     

72例高血压患者频域心电图（FCG）分析

频域心电分析是一种心脏无创检测的新技术。采用生物控制的概念,通过计算机心电信号进行快速付立叶变换,把时域信号变换到频域中,使那些时域中不便提取的信息,在频域中表示出来,进行多参量、多指标的综合分析从而补充了常规心电图和其它心脏检测技术的不足。我们应用ＨＢＤ—ＩＩＡ型心电域信息自动诊断仪,分的了７２例高血压患者频域心电图的变化,报告如下：１　材料和方法１１　病例选择本组７２例高血压患者均符合世界卫生组织建议使用的高血压诊断标准。＜高血压病史＞１９年者３３人、６～１０年１４人、２～５年者１７人、年…     

缺血性心脏病115例患者的频域心电图分析

利用电子计算机将心电信号进行付立叶变换。增加频域参数的分析,总结缺血性心脏病心电信号的频域心电图(FCG)的变化特点,以补偿常规心电图检查方法的不足。 1 临床资料 ①正常人组42例,其中<50岁12例,51—60岁18例,60岁以上12例,经体检、常规心电     

星状神经节后心下交感神经同步电活动的记录及预处理方法

目的:建立提取心下交感神经电活动的动物实验、记录及预处理方法. 方法:采用猫做为实验动物模型,信号通过生理数据采集系统记录,运用自回归模型谱分析法、有限冲击响应数字滤波器以及基于成组t检验的算法对原始信号进行预处理. 结果:获得具有明显同步特征的心下交感神经电活动信号,提取出4种可用于进一步分析的电活动特征参数. 结论:动物模型及记录系统能够实现心下交感神经电活动的准确记录,预处理方法从原始信号中提取出电活动特征参数.     

基于心电信号的身份识别系统研究

目的设计一套基于心电信号的身份识别系统,对采集到的心电信号进行算法设计,进而实现身份识别功能。方法首先采集心电数据:一部分是MIT数据库的心电数据,一部分是用便携式心电采集装置采集的心电数据。然后进行心电信号特征提取,在每个心动周期内先找到R波的最高点,然后再提取R波最高点前80个点和后170个点作为处理对象。最后在MATLAB中采用主成分分析(PCA)方法进行数据降维,采用线性判别分析(LDA)方法设计分类器,并进行测试。结果实验结果表明,该心电采集设备可以实时采集心电数据,分类后的数据具有唯一性且分类的正确率达到了90%以上。结论本系统在技术与应用层面上具有一定的创新性,可为社会的安全性提供一定的保障,具有一定应用价值。     

基于小波变换的心电图信号特征点的检测分析

目的:总结小波变换理论在心电图信号特征点检测中的价值。方法:基于小波变换多分辨分析理论,对心电信号进行去噪处理及,通过小波变换逼近信号对基线漂移进行滤除,滤除高频噪声,心电信号平均值设为0。分别采用时间电压法、面积法以及幅值法对平均心电轴进行测算。结果:面积法精确度最佳,显著高于其他两种方法(P0.05)。结论:以小波变换理论作为基础,应用面积法能够显著提高心电图信号特征点检测的准确性。     

基于小波变换的ECG去噪法及仿真

为去除心电信号中的各种噪声,本文以小波变换的多分辨率分析为理论基础,利用自适应阈值调整小波变换系数,用调整后的系数进行心电信号重建.采用MIT-BIH数据库中的心电信号进行仿真、验证,有效地去除了噪声信号.与传统滤波器具去噪相比有明显的优越性.     

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.     

Ideal Filtering Approach on DCT Domain for Biomedical Signals: Index Blocked DCT Filtering Method (IB-DCTFM)

We proposed Index-Blocked Discrete Cosine Transform Filtering Method (IB-DCTFM) to design ideal frequency range filter on DCT domain for biomedical signal which frequently exposed to specific frequency noise such as motion artifacts and 50/60 Hz powerline interference. IB-DCTFM removes unwanted frequency range signal on time domain by blocking specific DCT index on DCT domain. In simulation, electrocardiography, electromyography, photoplethysmography are used as a signal source and FIR, IIR and adaptive filter are used for comparison with proposed IB-DCTFM. To evaluate filter performance, we calculated signal-to-noise ratio and correlation coefficient to clean signal of each signal and filtering method respectively. As a result of filter simulation, average signal to noise ration and correlation coefficient of IB-DCTFM are improved about 75.8 dB/0.477, and FIR, IIR and adaptive filtering results are 24.8 dB/0.130, 54.3 dB/0.440 and 29.5 dB/0.200 respectively.     

Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems

Bradycardia can be modulated using the cardiac pacemaker, an implantable medical device which sets and balances the patient’s cardiac health. The device has been widely used to detect and monitor the patient’s heart rate. The data collected hence has the highest authenticity assurance and is convenient for further electric stimulation. In the pacemaker, ECG detector is one of the most important element. The device is available in its new digital form, which is more efficient and accurate in performance with the added advantage of economical power consumption platform. In this work, a joint algorithm based on biorthogonal wavelet transform and run-length encoding (RLE) is proposed for QRS complex detection of the ECG signal and compressing the detected ECG data. Biorthogonal wavelet transform of the input ECG signal is first calculated using a modified demand based filter bank architecture which consists of a series combination of three lowpass filters with a highpass filter. Lowpass and highpass filters are realized using a linear phase structure which reduces the hardware cost of the proposed design approximately by 50%. Then, the location of the R-peak is found by comparing the denoised ECG signal with the threshold value. The proposed R-peak detector achieves the highest sensitivity and positive predictivity of 99.75 and 99.98 respectively with the MIT-BIH arrhythmia database. Also, the proposed R-peak detector achieves a comparatively low data error rate (DER) of 0.002. The use of RLE for the compression of detected ECG data achieves a higher compression ratio (CR) of 17.1. To justify the effectiveness of the proposed algorithm, the results have been compared with the existing methods, like Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.     

数字保护算法的FPGA实现方法

介绍了一种基于现场可编程逻辑陈列(FPGA)的数字保护算法的实现方法,将算法的实现平台由微控制器(M CU)转向FPGA,构成基于FPGA的数字保护算法专用芯片。仿真结果表明:当每周期取40个采样点时,完成全波傅氏滤波或最小二乘滤波算法仅需几个微秒,大大快于M CU的处理速度,因而可以有效地克服精度与速度之间的矛盾。     

非线性系统辨识的递推-迭代方法

所提出的辨识新方法,以递推最小二乘(RLS)参数估计与非线性规划(BFGS)为主体。其测量数据的部分新息由RLS利用,而另一部分新息则通过BFGS加以采用。通常RLS只能递推地得到“粗略的”参数估计值,而BFGS则迭代地精确化参数的估计值。该辨识算法用于线性系统时,可以提高参数估计值的精度,改善收敛性。另外,该算法中的非线性迭代最优化过程可以克服非线性效应,参数估计值的精度和收敛性可以得到改进,这已由数字仿真验证。     

ECG/PPG Integer Signal Processing for a Ubiquitous Health Monitoring System

A compact ubiquitous-health monitor operated by single 8-bit microcontroller was made. An integer signal processing algorithm for this microcontroller was developed and digital filtering of ECG (electrocardiogram) and PPG (photoplethysmogram) was performed. Rounding-off errors due to integer operation was solved by increasing the number of effective integer digits during CPU operation; digital filter coefficients and data expressed in decimal points were multiplied by a certain number and converted into integers. After filter operation, the actual values were retrieved by dividing with the same number and selecting available highest bits. Our results showed comparable accuracies to those computed by a commercial software. Compared with a floating-point calculation by the same microcontroller, the computation speed became faster by 1.45 ∼ 2.0 times depending on various digital filtering cases. Our algorithm was successfully tested for remote health monitoring with multiple users. If our algorithm were not used, our health monitor should have used additional microcontrollers or DSP chip. The proposed algorithm reduced the size and cost of our health monitor substantially.     

重症监护病人心电导联信号质量评估

目的：研究基于重症监护病人心电导联的信号质量评估算法。方法：通过分析信号的波形特征、统计特性和相互关系导出反映信号质量高低的信号质量指数（Signal Quality Index, SQI），并基于SQI进行病人的心率估计，应用美国麻省理工学院多参数智能重症监护数据库Ⅱ中437例病人的6000多小时高质量数据和添加的各类心电干扰的数据进行算法评价。结果：SQI随信噪比的降低而减小；SQI与心电搏动检测灵敏度和正检测率呈高度正相关关系；基于信号质量评估算法，在严重干扰存在时仍能提供精确的心率估计。结论：信号质量评估算法可对心电信号质量给出客观的评价。     

基于FFT频域积累的非接触生命参数信号检测

目的：提高非接触生命参数检测系统信噪比。方法：采用两种基于FFT的频域积累检测方法，利用Matlab软件环境编写算法，进行仿真实验与实际信号处理。结果：该方法提高了非接触检测系统信号的信噪比，能够在强噪声背景下检测出生命参数信号。结论：该方法适用于低信噪比下的微弱信号提取，在本系统中得到了较好应用。     

雷达式非接触检测中呼吸和心跳信号分离算法的研究

为了有效地从生物雷达的体动信号中分离出呼吸和心跳信号,实现生理特征参数（呼吸率和心率）实时监测,本文在自适应噪声抵消模型的基础上,提取呼吸信号的谐波组合作为模型的参考输入,将生物雷达检测到的体动信号作为模型的原始输入,构建了一套适用于生物雷达检测中分离呼吸、心跳信号的自适应谐波抵消算法。仿真实验结果表明,该算法简单、易于实现,能够实时地分离呼吸和心跳信号。