心电信号特征检测算法研究

提出一种针对心电信号特征检测的快速算法。首先针对干扰信号的频率特性分别采用了中值滤波、梳状滤波器、Butter worth滤波器去除噪声干扰。然后根据心电波形特性采用一种自适应阈值差分算法来进行QRS波检测。接着采用改进后的局域变换法检测P、T波。利用MIT-BIH的QT数据库对所提出的算法进行评估,QRS群波检测率达99.65%以上,对P、T波的定位误差在50 ms以内。试验结果表明,该算法能提高各波的检测精度,适于实际应用。     

Low-frequency detection in ECG signals and joint EEG-Ergospirometric measurements for precautionary diagnosis

HRV (Heart Rate Variability) is an indicator that can be related to different human organs and systems: breathing, heart, brain, pulmonary system, etc. In cardiac clinic, physical exertion can be pre-assessed thanks to HR (Heart Rate) response using appropriate tests to rule out eventual cardiac dysfunction prior to undergo patient to further exams, surgical operations and rehabilitation activities. HR assessment must determine the capability of patient to continue exertion up to a certain level without having angina pain symptoms and brain dysfunctions. The variability of HR is a marker of dynamic load because it is sensitive and responsive to acute stress. Moreover it is also a marker of a cumulative wear and tear because it declines with advancing age. In this paper we propose combined measurements of EEG-Ergospirometry and ECG for patient’s cardio-pulmonary condition assessment for allowing doctors to make a decision on rehabilitation or surgical operation for people suspected of suffering from epilepsy seizures. Measurements assessed using frequency domain parameters have permitted the determination of low and high frequencies that are related to sympathetic and parasympathetic activities respectively.     

Detection of premature ventricular contractions using MLP neural networks: A comparative study

This paper proposes a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising module, a feature extraction module and a classification module. In the first module we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. The feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptron (MLP) neural networks with different number of layers and nine training algorithms are designed. The performances of the networks for speed of convergence and accuracy classifications are evaluated for seven files from the MIT–BIH arrhythmia database. Among the different training algorithms, the resilient back-propagation (RP) algorithm illustrated the best convergence rate and the Levenberg–Marquardt (LM) algorithm achieved the best overall detection accuracy.     

Increasing the accuracy of ECG based biometric analysis by data modelling

Here an effort is made to use human electrocardiogram as a tool of biometric analysis for authentication. The proposed method is based on first accurate extraction of characteristic features from each ECG and then design of a suitable classification methodology to comment on the authenticity. As the feature matrix is a huge one, Principal Component Analysis (PCA) is applied to avoid handling of large amount of data. Next, the reduced features from PCA are fitted into a quadratic polynomial model by the method of least square. Then the fitted values for the allowed set of data is obtained and the range over which they vary, provides the signature matrix of a person. Finally the classification is done by a comparison based on nearest neighbor method. The method is tested on ECG of 20 individuals taken from PTB database. This method has accuracy more than 95% with the best fit modeling which becomes only 80% without data modeling proving the importance of best fit modeling of data before classification. This accuracy is comparable with conventional biometric techniques; moreover, ECG biometric can be used with other authentication scheme, with ECG providing liveliness proof.     

基于C8051F020的生理参数监护实验系统电路设计

文章介绍了一种生理监护实验系统的电路设计,包括心电（ECG）监护电路、血压（BP）监护电路、呼吸（RESP）监护电路、脉搏（Pulse）监护电路、体温（Temp）监护电路。仪器采用低功耗芯片C8051F020作为CPU,可以采集各生理检测模块的信号,并具有显示、键盘管理、计算机通信功能。实验测试表明,系统操作方便,可实现生理信号的采集、处理、显示和通信功能,可用于实验教学。     

基于MATLAB和IP核的心电信号数字滤波器的设计

传统的心电信号滤波功能一般是由模拟滤波器完成,但模拟滤波器存在较多的缺点（处理精度不高、速度不快、容易受到干扰以及适应差等）,已越来越难以满足现代信号采集系统的要求.现提出了一种基于MATLAB和FPGA的数字滤波器的实现方案,能较好地克服上述模拟滤波器的缺点.该数字滤波器首先使用MATLAB和IP核相结合的方法生成代码,然后通过QuartusⅡ软件把编译的程序下载到FPGA中以实现数字滤波器的软硬件设计.此方法使数字滤波器的设计变得非常简单和可靠.经试验,该数字滤波器在心电信号处理运用中满足设计要求.     

基于nRF2401芯片的便携式心电监护仪的研制

介绍了如何采用无线通信芯片nRF2401和C8051F020单片机构成的便携式心电监护仪,实现了对所采集的心电信号和监护参数(parameter)的实时无线发送。本系统与现有同类设备相比,其体积小,重量轻,佩戴方便,监护效果理想。     

基于小波变换心电信号去噪方法

小波变换基于模极大值的去噪方法具有非线性及自适应特性,而且特别适合于进行非平稳的微弱信号及具有较多奇异点信号的消噪,这种信号特点恰好是心电信号最突出的特征,也是常规信号处理方法遇到的最大难题。本文简述了小波变换模极值去噪方法在心电信号处理中的应用,并证实这种方法可以在信号消噪的同时有效地改善信噪比,并同时提高信号的分辨率。