自适应噪声抵消在抑制高频电刀对心电监护仪干扰中的应用研究

本文讨论了如何利用自适应噪声抵消去除高频电刀工作时对心电监护波形的干扰。本课题基于高频电刀干扰成分的研究，采用了滤波系统去除高频成分的干扰；利用了自适应噪声抵消系统实现对噪声的跟踪、去除。并利用MATLAB语言对抑制措施进行动态仿真，验证了抑制措施的可行性。     

消除ECG信号噪声的信号平均方法的探讨

本文讨论了信号平均法去除噪声的基本原理，针对带有随机噪声的ECG信号，利用UW Digiscope程序进行了实验仿真，结果表明，该方法可以在不失真的前提下通过多次叠加提高信噪比，从而分离出目标信号，抑制了噪声。     

Alexander fractional differential window filter for ECG denoising

The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart. During the process of recording and transmission, ECG signals are often corrupted by various types of noises. Minimizations of these noises facilitate accurate detection of various anomalies. In the present paper, Alexander fractional differential window (AFDW) filter is proposed for ECG signal denoising. The designed filter is based on the concept of generalized Alexander polynomial and the R–L differential equation of fractional calculus. This concept is utilized to formulate a window that acts as a forward filter. Thereafter, the backward filter is constructed by reversing the coefficients of the forward filter. The proposed AFDW filter is then obtained by averaging of the forward and backward filter coefficients. The performance of the designed AFDW filter is validated by adding the various type of noise to the original ECG signal obtained from MIT-BIH arrhythmia database. The two non-diagnostic measure, i.e., SNR, MSE, and one diagnostic measure, i.e., wavelet energy based diagnostic distortion (WEDD) have been employed for the quantitative evaluation of the designed filter. Extensive experimentations on all the 48-records of MIT-BIH arrhythmia database resulted in average SNR of 22.014?±?3.806365, 14.703?±?3.790275, 13.3183?±?3.748230; average MSE of 0.001458?±?0.00028, 0.0078?±?0.000319, 0.01061?±?0.000472; and average WEDD value of 0.020169?±?0.01306, 0.1207?±?0.061272, 0.1432?±?0.073588, for ECG signal contaminated by the power line, random, and the white Gaussian noise respectively. A new metric named as morphological power preservation measure (MPPM) is also proposed that account for the power preservance (as indicated by PSD plots) and the QRS morphology. The proposed AFDW filter retained much of the original (clean) signal power without any significant morphological distortion as validated by MPPM measure that were 0.0126, 0.08493, and 0.10336 for the ECG signal corrupted by the different type of noises. The versatility of the proposed AFDW filter is also validated by its application on the ECG signal from MIT-BIH database corrupted by the combination of the noises as well as on the real noisy ECG signals are taken from MIT-BIH ID database. Furthermore, the comparative study has also been done between the proposed AFDW filter and existing state of the art denoising algorithms. The results clearly prove the supremacy of our proposed AFDW filter.     

一种基于RLS-ANC系统的FECG信号提取新方法

本文介绍一种利用递推最小二乘 (RecursiveLeastSquares ,RLS)自适应滤波算法提取胎儿心电(FECG)的新方法。该方法采用非入侵方式从孕妇腹部和胸部获取信息作为主输入和参考输入 ,首次应用多参考输入系统的RLS自适应滤波算法消除母亲心电 (MECG) ,实时提取胎儿心电信号。实验结果表明 ,本方法不但具有良好的提取效果 ,而且对非平稳信号的适应能力强 ,收敛速度快于NLMS(NormalizedLeastMeanSquares)算法。     

Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering

This paper presents a novel electrocardiogram (ECG) denoising approach based on variational mode decomposition (VMD). This work also incorporates the efficacy of the non-local means (NLM) estimation and the discrete wavelet transform (DWT) filtering technique. Current ECG denoising methods fail to remove noise from the entire frequency range of the ECG signal. To achieve the effective ECG denoising goal, the noisy ECG signal is decomposed into narrow-band variational mode functions (VMFs) using VMD method. The idea is to filter out noise from these narrow-band VMFs. To achieve that, the center frequency information associated with each VMFs is used to exclusively divide them into lower- and higher-frequency signal groups. The higher frequency VMFs were filtered out using DWT-thresholding technique. The lower frequency VMFs are denoised through NLM estimation technique. The non-recursive nature of VMD enables the parallel processing of NLM estimation and DWT filtering. The traditional DWT-based approaches need large decomposition levels to filter low frequency noises and at the same time NLM technique suffers from the rare-patch effect in high-frequency region. On the contrary, in the proposed framework both NLM and DWT approaches complement each other to overcome their individual ill-effects. The signal reconstruction is performed using the denoised high frequency and low frequency VMFs. The simulation performed on the MIT-BIH Arrhythmia database shows that the proposed method outperforms the existing state-of-the-art ECG denoising techniques.     

基于提升小波的心电信号基线漂移的去除方法

目的：避免传统小波变换基于卷积算法中的冗余计算,同时去除心电信号（ECG）在采集中混入其中的基线漂移噪声。方法：根据提升小波变换采取双小波基函数结合的方法,经分解、含噪声子带系数置零、逆变换形成去噪的心电信号。结果：运用MATLAB环境对MIT-BIH数据库提供的心电信号数据及基线漂移噪声信号bw进行去除基线漂移仿真验证,其基线漂移均被有效去除。结论：ECG信号经该方法处理后其所含有的基线漂移噪声被准确去除,且原信号中的波形信息被有效保留,可为心电信号特征参数的检测提供帮助。     

动态心电信号干扰的识别研究

目的研究能够有效滤除动态心电信号各种噪声和干扰,使有效信号得以突出的算法。方法分析动态心电信号干扰的形态特点及统计特点,针对不同种类的干扰提出识别办法,估算信噪比。结果本算法能有效估计信噪比的大小,有助于在心电监护中抛弃计算错误的导联,减小有效信号值估的偏差。结论本文算法具有较强的抗干扰能力,可靠性高,使监护系统能够有效地利用多导联的信息得到准确的分析诊断结果。     

System for automatic heart rate calculation in epileptic seizures

This article presents a comprehensive system for automatic heart rate (HR) detection. The system is robust and resistant to disturbances (noise, interferences, artifacts) occurring mainly during epileptic seizures. ECG signal filtration (IIR) and normalization due to skewness and standard deviation were used as preprocessing steps. A key element of the system is a reference QRS complex pattern calculated individually for each ECG recording. Next, a cross-correlation of the reference QRS pattern with short, normalized ECG windows is calculated and the maxima of the correlation are found (R-wave locations). Determination of the RR intervals makes possible calculation of heart rate changes and also heart rate variability (HRV). The algorithm was tested using a simulation in which a noise of an amplitude several times higher than ECG standard deviation levels was added. The proposed algorithm is characterized by high QRS detection accuracy, and high sensitivity and specificity. The algorithm proved to be useful in clinical practice, where it was used to automatically determine HR for ECG signals recorded before and during 58 focal seizures in 56 adult patients with intractable temporal lobe epilepsy.     

径向基函数自适应减法器提取诱发电位的性能评价

目的：针对体感诱发电位（somatosensory evoked potentials,SEP）的特征,基于现场可编程门阵列（field programmable gate array,FPGA）硬件平台设计径向基函数自适应减法器,实现体感诱发电位的快速提取。方法：通过对各模块的硬件算法设计,利用Simulink仿真工具对5例接受脊柱侧弯手术患者的原始SEP信号进行仿真实验,以工频干扰、EEG脑电为噪声,对径向基函数自适应减法器提取SEP的性能进行评价。结果：不同输入信噪比条件下,径向基函数自适应减法器比单一自适应减法器输出信号的信噪比高;输入信噪比为-15dB时,单一的自适应噪声减法器（adaptive noise canceller．ANC）输出相对于模板信号的失真比径向基函数自适应减法器（ANC—RBF）小,但输入信噪比为-25、-30dB时,ANC输出相对于模板信号的失真比ANC—RBF大;ANC—RBF提取的SEP波形比较平滑。结论：径向基函数自适应减法器比单一自适应减法器对术中体感诱发电位具有更好的去噪效果,不仅提取信号的失真度较小,而且信号波形更为平滑,使SEP信号的潜伏期和幅值更易识别。     

论心电信号检测中的噪声与干扰及其消除方法

目的：认识心电信号是从体表检测到的心脏电生理信号,它对心脏疾病的诊断意义重大。方法：强调在心电信号检测过程中,很容易受到噪声（干扰）的影响,如随机噪声、工频干扰、检测系统的内部噪声等。结果：分析了心电检测中各种噪声和干扰产生的原因及消除和减小噪声（干扰）的方法。结论：为了尽可能地消除噪声（干扰）,又不使检测到的心电信号失真,这就需要改进采集电路、滤波电路,运用新的运算方法。     

Heartbeat detection in multimodal physiological signals using signal quality assessment based on sample entropy

This paper presents a novel technique to identify heartbeats in multimodal data using electrocardiogram (ECG) and arterial blood pressure (ABP) signals. Multiple physiological signals such as ECG, ABP, and Respiration are often recorded in parallel from the activity of heart. These signals generally possess related information as they are generated by the same physical system. The ECG and ABP correspond to the same phenomenon of contraction and relaxation activity of heart. Multiple signals acquired from various sensors are generally processed independently, thus discarding the information from other measurements. In the estimation of heart rate and heart rate variability, the R peaks are generally identified from ECG signal. Efficient detection of R-peaks in electrocardiogram (ECG) is a key component in the estimation of clinically relevant parameters from ECG. However, when the signal is severely affected by undesired artifacts, this becomes a challenging task. Sometimes in clinical environment, other physiological signals reflecting the cardiac activity such as ABP signal are also acquired simultaneously. Under the availability of such multimodal signals, the accuracy of R peak detection methods can be improved using sensor-fusion techniques. In the proposed method, the sample entropy (SampEn) is used as a metric for assessing the noise content in the physiological signal and the R peaks in ECG and the systolic peaks in ABP signals are fused together to enhance the efficiency of heartbeat detection. The proposed method was evaluated on the 100 records from the computing in cardiology challenge 2014 training data set. The performance parameters are: sensitivity (Se) and positive predictivity (PPV). The unimodal R peaks detector achieved: Se _gross = 99.40%, PPV _gross = 99.29%, Se _average = 99.37%, PPV _average = 99.29%. Similarly unimodal BP delineator achieved Se _gross = 99.93%, PPV _gross = 99.99%, Se _average = 99.93%, PPV _average = 99.99% whereas, the proposed multimodal beat detector achieved: Se _gross = 99.65%, PPV _gross = 99.91%, Se _average = 99.68%, PPV _average = 99.91%.     

Impedance cardiography signal denoising using discrete wavelet transform

Impedance cardiography (ICG) is a non-invasive technique for diagnosing cardiovascular diseases. In the acquisition procedure, the ICG signal is often affected by several kinds of noise which distort the determination of the hemodynamic parameters. Therefore, doctors cannot recognize ICG waveform correctly and the diagnosis of cardiovascular diseases became inaccurate. The aim of this work is to choose the most suitable method for denoising the ICG signal. Indeed, different wavelet families are used to denoise the ICG signal. The Haar, Daubechies (db2, db4, db6, and db8), Symlet (sym2, sym4, sym6, sym8) and Coiflet (coif2, coif3, coif4, coif5) wavelet families are tested and evaluated in order to select the most suitable denoising method. The wavelet family with best performance is compared with two denoising methods: one based on Savitzky–Golay filtering and the other based on median filtering. Each method is evaluated by means of the signal to noise ratio (SNR), the root mean square error (RMSE) and the percent difference root mean square (PRD). The results show that the Daubechies wavelet family (db8) has superior performance on noise reduction in comparison to other methods.     

ECG信号分析的参数模型法

本文介绍了一种新的ＥＣＧ信号分析方法，称为参数模型法，它是用一组模型参数来表示ＥＣＧ信号，从而简化分析工作。运用本文建模方法得到的参数（Ｚ域上的一组零极点），能够很好地体现原始波形的时域特征。如果将这些参数进行聚类，它们分别将对应于时域上的Ｐ，ＱＲＳ．Ｔ波。另外，通过建模，有效地滤除了原来信号中带有的５０Ｈｚ噪声。     

非接触生物雷达基于自适应滤波的心跳信号检测

目的:从生物雷达非接触检测到的呼吸和心跳的混合信号中有效地分离出心跳信号。方法:利用基于RLS算法的自适应噪声抵消器,提出一种分离心跳信号和呼吸信号的方法。结果:采用基于RLS算法的自适应抵消器能够分离出心跳信号,其心率与从心电信号获得的心率具有很强相关性(γ2=0.95,P<0.000 1)。结论:该文提出的分离心跳信号的方法具有较好的实用性,有望实现呼吸和心跳信号的实时分离。     

心电信号的非线性小波检测方法

将非线性小波方法应用于心电信号的检测。利用二进Daubechies小波对有高频噪声干扰的心电信号按Mallat算法进行小波分解;探讨了非线性小波检测方法;结合Lipschitz指数判据,将其应用于高频干扰心电信号的去噪,实现了滤除噪声的同时又有效地恢复了心电信号。     

便携式心电检测放大电路设计

目的：为便于日常心电监护，开发了一种便携式心电检测系统，介绍这种便携式心电检测系统中放大电路的设计。方法：该心电放大电路以AD620、OPA4277和TLC2254作为放大电路核心元件，针对心电信号的特殊信号和干扰频率范围，进行了分析，对由电极采集到的心电信号，通过前置放大部分，将微弱的心电信号高保真放大，并通过低通滤波、高通滤波及50Hz陷波滤除干扰。结果：差模电压增益为1000，共模抑制比为90dB，输入阻抗大于10GQ，通频带为0．035～110Hz。结论：系统具有高输入阻抗、高共模抑制比、低噪声、低温漂和高信噪比等优点，而且成本低、体积小、耗电少、携带方便。     

基于AVR单片机的肌电信号生物反馈测试仪的设计

肌电信号(EMG)是一种伴随肌肉运动而产生的生物电信号,对表面EMG的分析研究可发现它与肌肉生理状态和肢体运动模式之间的对应关系。基于生物反馈技术研制肌电信号生物反馈仪采用AT-MEGA16 AVR单片机为核心元件,通过信号调理电路消除表面EMG工频干扰和噪声,并利用声光报警反馈肌肉紧张程度信息。本文介绍了肌电信号生物反馈仪的基本构成及其特点,并对肌电生物信号的特点和提取方法进行了分析,采用均方根值(RMS)法实时反映肌肉活动状态,试验结果表明本系统能有效缓解肌肉的紧张程度,在康复医学方面有较好的应用前景。     

基于信噪差分比方法的磁共振射频线圈在头颅血管造影中的对比研究

用信噪比和血管信噪差分比方法．对相控阵表面头线圈和鸟笼头线圈的体模和人体磁共振血管造影（MRA）成像性能进行了对比研究,研究结果表明．相控阵线圈具有优势,也证明了该方法在磁共振线圈评价和临床选择方面的有效性。     

USB接口在心电信号数字化中的应用

心电图在临床诊断中应用广泛，心电图机只能即时记录心电信号而无法保存数据。本文将心电信号进行A／D转换，通过数据缓存，将数据送入USB数据传输接口，再和计算机等设备的USB接口相连，可以把心电信号数据输入到计算机等设备中，为心电信号的保存、分析、参数计算等提供了必要的手段。     

生物雷达检测中呼吸和心跳实时分离技术的研究

目的：从生物雷达检测到的体动信号中分离出心跳信号。方法：采用自适应噪声抵消模型,基于递归最小二乘算法（recursive-leastsquare,RLS）调整模型的输出,并将分离出的心跳信号与心电信号进行比较。结果：该方法可以从体动信号中分离出心跳信号,而且从心跳信号中提取的心率值与从心电信号提取的心率值具有很强的相关性。结论：基于自适应噪声抵消技术,可以实现生物雷达检测中呼吸和心跳的分离。