医用数字滤波器的设计及其应用

随着微处理机在医学仪器及其医学信息处理中的广泛应用,数字滤波器以其显著的优越性正逐步替代传统的模拟滤波器,传统的医学信息处理中常用的模拟滤波器主要有:消除50Hz电源线工频干扰的陷波滤波器,消除基线漂移,运动伪迹以及在心电图分析中,为提高QRS波群检测率而消除其中的P波和T波干扰的高通滤波器,消除电手术器械产生的射频干扰以及迭加在心电信号中的肌电信号干扰的低通滤波器。模拟滤波器主要是由运算放大器,电阻和电容等组成,周围环境的改变,器件的精密度和     

50 Hz滤波电路对心肌细胞动作电位波形记录和波形分析的影响

目的 :研究微电极放大器中的 5 0Hz滤波电路对心肌细胞动作电位波形及各项参数的影响。方法 :将心肌细胞动作电位经玻璃微电极、微电极放大器、微分器、A/D转换器输入微型计算机。在使用和不使用微电极放大器中5 0Hz滤波功能两种情况下 ,对心肌细胞动作电位波形进行比较分析 ,并用快速傅立叶变换进行频谱分析。结果 :使用微电极放大器中 5 0Hz滤波功能情况下 ,动作电位波形在 0期严重失真 ,上升时间延长 ,最大上升速度减小 ,其它参数无显著变化。结论 :心肌细胞动作电位波形中含有较大 5 0Hz信号成分 ,在研究心肌细胞动作电位信号时 ,不能使用 5 0Hz滤波器。如果使用 5 0Hz滤波器 ,会造成动作电位波形严重失真 ,影响实验结果     

耳蜗滤波器设计

提出了一种非线性耳蜗滤波器组的设计方法,设计的耳蜗滤波器组结构简单,易于实现,能模拟因输入信号电平的不同而引起的耳蜗滤波器的非线性特性,而且其特征参数能随着更新实验结果的出现灵活地加以修正。文中给出了设计细节以及具体的数字实现方法。     

表面肌电信号采集与降噪处理

目的：本文以设计的表面~g（sEMG）信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法：结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果：通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论：所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

表面肌电信号采集与降噪处理

目的:本文以设计的表面肌电(sEMG)信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法:结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果:通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论:所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

基于小波变换的心电信号去噪综合算法

目的：研究去除心电信号中的基线漂移、工频干扰和肌电干扰等噪声,提高心电信号的自动识别和诊断精度。方法：利用Coif4小波对心电信号进行8尺度分解,采用小波分解重构法去除基线漂移,然后利用改进的小波闽值算法去除工频干扰和肌电干扰。结果：利用Matlab仿真工具,选择MIT-BIH心率失常数据库中信号进行验证,能有效去除这三种噪声,并且很好的保持R波的信息。结论：本算法在不丢失心电信号有用信息的前提下,可以较好的去除三种常见的噪声,可以用于心电信号自动分析之前的预处理。     

基于FPGA 超声信号数字动态滤波器的实现

目的:本文研究了基于现场可编程门阵列(Field Programmable Gate Array,FPGA)超声成像系统中数字动态滤波器的实现方法和过程。方法:动态滤波器中FIR滤波器采用分布式算法(Distributed Arithmetic,DA)实现结构,并在应用中对DA算法进行了改进,包括数据并行处理结构的设计、对查找表(Look Up Table,LUT)输入字长N大小的控制和具有对称系数的FIR滤波器的采用。改进后的DA实现在FPGA资源占用和处理速度之间达到了平衡。同时,结合多级流水线结构,动态滤波器实现了数字超声信号并行处理。结果:采用常值滤波器(远场匹配参数)进行滤波后,超声回波图像远场分辨率达到了要求,但越靠近近场效果越差。相比之下,本文设计的基于FPGA超声信号动态数字滤波器达到了很好的滤波效果,使回声图像近场和远场都有最佳分辨率。结论:利用FPGA实现超声系统中动态滤波器是完全可行的,并且有助于提高系统的稳定性和可靠性,并大大减低系统成本。     

基于小波的心电信号有效性检测方法

目的：检测采集到的信号是否为有效心电信号,提高后续心电诊断和分析的准确率。方法：将采集到的信号进行预处理,即去噪处理,主要抑制基线漂移,50Hz工频及其谐波干扰和肌电干扰;取滑动窗长度为4s,检测该段内信号是否有效。为了验证算法的准确率及对不同心电波形是否具有普遍适用性,对MIT-BIH Arrhythmia Database中48个记录,CU及MIT-BIH Noise Stress Test Database中部分记录进行了仿真、验证。结果：仿真实验证明该方法能正确区分有效和无效信号,错检率较低,实现简单,适合实时处理。结论：本方法准确率高,能减少后续心电诊断和分析的计算量并提高准确率,特别是对室颤检测,符合心电分析的要求。     

彩超系统中壁滤波器设计和性能比较

本文首先介绍了多普勒原理和血流多普勒信号模型,推出了壁滤波器设计在彩色超声成像系统中具有极其重要的地位;其次分析了如何通过无限脉冲冲激响应（IIR）滤波器和回归型滤波器设计壁滤波器的原理,其中无限脉冲冲激响应（IIR）滤波器根据初始状态的不同又可以分为零初始化、阶跃初始化和投影初始化三种滤波器;然后根据上述原理分别对各种形式的壁滤波器进行设计,得到它们的频谱图;最后根据频谱图,对这几种壁滤波器进行性能比较,得到它们的优缺点,对从事壁滤波器设计相关的工作者有一定得借鉴意义。     

基于DSP与FPAA的电化学信号滤波器

目的：研究对自主研发的DNA电化学分析仪所采集电化学信号的噪声的滤除方法。方法：根据电化学噪声信号的特征及其频率分布范围,采用由现场可编程模拟阵列（FPAA）实现的有高阶低通滤波器和基于DSP实现的FIR数字滤波器相结合的方式,对混合在电化学信号中的高频噪声信号进行滤除。结果：对滤波结果进行分析,频率在100HZ以上的噪声信号基本滤除。结论：FPAA与DSP芯片的结合构成了一个稳定性高,线性相位特性好,实时性强的高效滤波器。     

Empirical mode decomposition based filtering techniques for power line interference reduction in electrocardiogram using various adaptive structures and subtraction methods

Electrocardiogram (ECG) is a vital sign monitoring measurement of the cardiac activity. One of the main problems in biomedical signals like electrocardiogram is the separation of the desired signal from noises caused by power line interference, muscle artifacts, baseline wandering and electrode artifacts. Different types of digital filters are used to separate signal components from unwanted frequency ranges. Adaptive filter is one of the primary methods to filter, because it does not need the signal statistic characteristics. In contrast with Fourier analysis and wavelet methods, a new technique called EMD, a fully data-driven technique is used. It is an adaptive method well suited to analyze biomedical signals. This paper foregrounds an empirical mode decomposition based two-weight adaptive filter structure to eliminate the power line interference in ECG signals. This paper proposes four possible methods and each have less computational complexity compared to other methods. These methods of filtering are fully a signal-dependent approach with adaptive nature, and hence it is best suited for denoising applications. Compared to other proposed methods, EMD based direct subtraction method gives better SNR irrespective of the level of noises.     

A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Diaphragmatic electromyogram (EMGdi) signals convey important information on respiratory diseases. In this paper, an adaptive filter for removing the electrocardiographic (ECG) interference in EMGdi signals based on wavelet theory is proposed. Power spectrum analysis was performed to evaluate the proposed method. Simulation results show that the power spectral density (PSD) of the extracted EMGdi signal from an ECG corrupted signal is within 1.92% average error relative to the original EMGdi signal. Testing on clinical EMGdi data confirm that this method is also efficient in removing ECG artifacts from the corrupted clinical EMGdi signal.     

基于小波变换的心电信号去噪算法

目的:去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信号,避免噪声对心电信号特征点的识别和提取造成误判和漏判。方法:首先利用coif4小波对心电信号按Mallat算法进行分解,然后采用软、硬阈值折衷与小波重构的算法进行去噪。结果:采用MIT/BIH Arrhythmia Database中的心电信号进行仿真、验证,有效去除了三种常见的噪声信号。结论:本方法实时性好,为临床分析与诊断奠定了基础。     

Power-line interference cancellation in ECG signals

The efficiency of notchfilters and a subtraction procedure for power-line interference cancellation in electrocardiogram (ECG) signals is assessed. In contrast with the subtraction procedure, widely used digital notch filters unacceptably affect QRS complexes. The procedure eliminates interferences of variable amplitude and frequency. The frequency modulations are overcome by adaptive synchronized sampling. Initially, this is accomplished by current hardware power-line frequency measurement. Because this approach is impossible in battery-supplied and some computer-aidd devices, a software measurement of the power-line interference period is developed.     

A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS–wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).     

A Powerful Algorithm to aid Cardiac Arrhythmia Diagnosis

Abstract

The Electrocardiogram (ECG), represents the electrical activity of the heart. It is characterised by a number of waves P, QRS, T which are correlated to the status of the heart activity. In this paper, the aim is to present a powerful algorithm to aid cardiac diagnosis. The approach used is based on a determinist method, that of the tree decision. However, the different waves of the ECG signal need to be identified and then measured following a signal to noise enhancement. Signal to noise enhancement is performed by a combiner linear adaptive filter whereas P, QRS, T wave identification and measurement are performed by a derivative approach. Results obtained on simulated and real ECG signals are shown to be highly, satisfactory in the aid of cardiac arrhythmia diagnosis, such as junctionnal escapes, blocks, etc.     

A Powerful Algorithm to aid Cardiac Arrhythmia Diagnosis

The Electrocardiogram (ECG), represents the electrical activity of the heart. It is characterised by a number of waves P, QRS, T which are correlated to the status of the heart activity. In this paper, the aim is to present a powerful algorithm to aid cardiac diagnosis. The approach used is based on a determinist method, that of the tree decision. However, the different waves of the ECG signal need to be identified and then measured following a signal to noise enhancement. Signal to noise enhancement is performed by a combiner linear adaptive filter whereas P, QRS, T wave identification and measurement are performed by a derivative approach. Results obtained on simulated and real ECG signals are shown to be highly, satisfactory in the aid of cardiac arrhythmia diagnosis, such as junctionnal escapes, blocks, etc.     

Removal of ECG interference from surface respiratory electromyography

The signal to noise ratio (SNR) of surface respiratory electromyography signal is very low. Indeed EMG signal is contaminated by different types of noise especially the cardiac artefact ECG. This article explores the problem of removing ECG artefact from respiratory EMG signal. The new method uses an adaptive structure with an electrocardyographic ECG reference signal carried out by wavelet decomposition. The proposed algorithm requires only one channel to both estimating the adaptive filter input reference noise and the respiratory EMG signal. This new technique demonstrates how two steps will be combined: the first step decomposes the signal with forward discrete wavelet transform into sub-bands to get the wavelet coefficients. Then, an improved soft thresholding function was applied. And the ECG input reference signal is reconstructed with the transformed coefficients whereas, the second uses an adaptive filter especially the LMS one to remove the ECG signal. After trying statistical as well as mathematical analysis, the complete investigation ensures that all details and steps make proof that our rigorous method is appropriate. Compared to the results obtained using previous techniques, the results achieved using the new algorithm show a significant improvement in the efficiency of the ECG rejection.     

SVM-based algorithm for recognition of QRS complexes in electrocardiogram

Among all electrocardiogram (ECG) components, the QRS complex is the most significant feature. This paper presents a new algorithm for recognition of QRS complexes in the electrocardiogram (ECG) based on support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander in the ECG signal. SVM is used as a classifier to delineate QRS and non-QRS regions. Algorithm performance was evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.3% of the detection rate. The percentage of false positive and false negative was 12.4 and 0.7% respectively. It could function reliably even under the condition of poor signal quality of the ECG signal.