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.     

Removal of power-line interference from the ECG: a review of the subtraction procedure

Background  

Modern biomedical amplifiers have a very high common mode rejection ratio. Nevertheless, recordings are often contaminated by residual power-line interference. Traditional analogue and digital filters are known to suppress ECG components near to the power-line frequency. Different types of digital notch filters are widely used despite their inherent contradiction: tolerable signal distortion needs a narrow frequency band, which leads to ineffective filtering in cases of larger frequency deviation of the interference. Adaptive filtering introduces unacceptable transient response time, especially after steep and large QRS complexes. Other available techniques such as Fourier transform do not work in real time. The subtraction procedure is found to cope better with this problem.     

Tremor suppression in ECG

Background  

Electrocardiogram recordings are very often contaminated by high-frequency noise usually power-line interference and EMG disturbances (tremor). Specific method for interference cancellation without affecting the proper ECG components, called subtraction procedure, was developed some two decades ago. Filtering out the tremor remains a priori partially successful since it has a relatively wide spectrum, which overlaps the useful ECG frequency band.     

Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques.

Trunk electromyographic signals (EMG) are often contaminated with heart muscle electrical activity (ECG) due to the proximity of the collection sites to the heart and the volume conduction characteristics of the ECG through the torso. Few studies have quantified ECG removal techniques relative to an uncontaminated EMG signal (gold standard or criterion measure), or made direct comparisons between different methods for a given set of data. Understanding the impacts of both untreated contaminated EMG and ECG elimination techniques on the amplitude and frequency parameters is vital given the widespread use of EMG. The purpose of this study was to evaluate four groups of current and commonly used techniques for the removal of ECG contamination from EMG signals. ECG recordings at two intensity levels (rest and 50% maximum predicted heart rate) were superimposed on 11 uncontaminated biceps brachii EMG signals (rest, 7 isometric and 3 isoinertial levels). The 23 removal methods used were high pass digital filtering (finite impulse response (FIR) using a Hamming window, and fourth-order Butterworth (BW) filter) at five cutoff frequencies (20, 30, 40, 50, and 60 Hz), template techniques (template subtraction and an amplitude gating template), combinations of the subtraction template and high pass digital filtering, and a frequency subtraction/signal reconstruction method. For muscle activation levels between 10% and 25% of maximum voluntary contraction, the template subtraction and BW filter with a 30 Hz cutoff were the two best methods for maximal ECG removal with minimal EMG distortion. The BW filter with a 30 Hz cutoff provided the optimal balance between ease of implementation, time investment, and performance across all contractions and heart rate levels for the EMG levels evaluated in this study.     

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.     

心电图信号放大和记录中50Hz干扰滤波的新技术——符合滤波

介绍一种用硬、软件系统实现的滤波器,它能实时滤除ECG信号中50Hz及其高次谐波的干扰,该技术称为符合滤波。在信号处理过程中,当干扰发生变化时滤波器能跟踪这种变化,保持滤波器性能不变。     

Complexity and characteristic frequency studies in ECG signals of mice based on multiple scale factors

Existing methods of physiological signal analysis based on nonlinear dynamic theories only examine the complexity difference of the signals under a single sampling frequency. We developed a technique to measure the multifractal characteristic parameter intimately associated with physiological activities through a frequency scale factor. This parameter is highly sensitive to physiological and pathological status. Mice received various drugs to imitate different physiological and pathological conditions, and the distributions of mass exponent spectrum curvature with scale factors from the electrocardiogram (ECG) signals of healthy and drug injected mice were determined. Next, we determined the characteristic frequency scope in which the signal was of the highest complexity and most sensitive to impaired cardiac function, and examined the relationships between heart rate, heartbeat dynamic complexity, and sensitive frequency scope of the ECG signal. We found that all animals exhibited a scale factor range in which the absolute magnitudes of ECG mass exponent spectrum curvature achieve the maximum, and this range (or frequency scope) is not changed with calculated data points or maximal coarse-grained scale factor. Further, the heart rate of mice was not necessarily associated with the nonlinear complexity of cardiac dynamics, but closely related to the most sensitive ECG frequency scope determined by characterization of this complex dynamic features for certain heartbeat conditions. Finally, we found that the health status of the hearts of mice was directly related to the heartbeat dynamic complexity, both of which were positively correlated within the scale factor around the extremum region of the multifractal parameter. With increasing heart rate, the sensitive frequency scope increased to a relatively high location. In conclusion, these data provide important theoretical and practical data for the early diagnosis of cardiac disorders.     

Comparison of three methods for beat-to-beat-interval extraction from continuous blood pressure and electrocardiogram with respect to heart rate variability analysis.

In recent years the analysis of heart rate variability (HRV) has become a suitable method for characterizing autonomous cardiovascular regulation. The aim of this study was to investigate the differences in HRV estimated from continuous blood pressure (BP) measurement by different methods in comparison to electrocardiogram (ECG) signals. The beat-to-beat intervals (BBI) were simultaneously extracted from the ECG and blood pressure of 9 cardiac patients (10 min, Colin system, 1000-Hz sampling frequency). For both data types, slope, peak, and correlation detection algorithms were applied. The short-term variability was calculated using concurrent 10-min BP and ECG segments. The root mean square errors in comparison to ECG slope detection were: 1.74 ms for ECG correlation detection; 5.42 ms for ECG peak detection; 5.45 ms for BP slope detection; 5.75 ms for BP correlation detection; and 11.96 ms for BP peak detection. Our results show that the variability obtained with ECG is the most reliable. Moreover, slope detection is superior to peak detection and slightly superior to correlation detection. In particular, for ECG signals with higher frequency characteristics, peak detection often exhibits more artificial variability. Besides measurement noise, respiratory modulation and pulse transit time play an important role in determining BBI. The slope detection method applied to ECG should be preferred, because it is more robust as regards morphological changes in the signals, as well as physiological properties. As the ECG is not recorded in most animal studies, distal pulse wave measurement in combination with correlation or slope detection may be considered an acceptable alternative.     

A mathematical algorithm for ECG signal denoising using window analysis

BACKGROUND: The presence of parasite interference signals could cause serious problems in the registration of ECG signals and many works have been done to suppress electromyogram (EMG) artifacts noises and disturbances from electrocardiogram (ECG). Recently, new developed techniques based on global and local transforms have become popular such as wavelet shrinkage approaches (1995) and time-frequency dependent threshold (1998). Moreover, other techniques such as artificial neural networks (2003), energy thresholding and Gaussian kernels (2006) are used to improve previous works. This review summarizes windowed techniques of the concerned issue. METHODS AND RESULTS: We conducted a mathematical method based on two sets of information, which are dominant scale of QRS complexes and their domain. The task is proposed by using a varying-length window that is moving over the whole signals. Both the high frequency (noise) and low frequency (base-line wandering) removal tasks are evaluated for manually corrupted ECG signals and are validated for actual recorded ECG signals. CONCLUSIONS: Although, the simplicity of the method, fast implementation, and preservation of characteristics of ECG waves represent it as a suitable algorithm, there may be some difficulties due to pre-stage detection of QRS complexes and specification of algorithm's parameters for varying morphology cases.     

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。     

Description and validation of an ECG removal procedure for EMGdi power spectrum analysis

We describe a cross-correlation procedure for removing contaminating electrocardiogram (ECG) complexes from the diaphragmatic electromyogram (EMGdi). First, the operator selects ECG templates from the EMGdi signal during expiratory intervals. Second, these templates are used to locate ECG complexes occurring during inspiratory EMGdi activity. Third, at the point of maximum correlation between the template and these ECG complexes, the template is adjusted in size and offset to "match" the ECG complex, and adjustments are determined by the linear regression coefficients. Finally, the modified template is subtracted from the EMGdi signal. To evaluate our method, we compared the power spectral density (PSD) obtained from processing EMGdi signals by our method with those obtained from the EMGdi signal in which ECG complexes had been removed by gating. Our results indicate that PSD obtained by these two different methods shows no statistically significant differences with respect to the following features: centroid frequency, median frequency, total power, standard deviation, skewness, and kurtosis.     

Effects of electrocardiography contamination and comparison of ECG removal methods on upper trapezius electromyography recordings

Electromyography (EMG) recordings from the trapezius are often contaminated by the electrocardiography (ECG) signal, making it difficult to distinguish low-level muscle activity from muscular rest. This study investigates the influence of ECG contamination on EMG amplitude and frequency estimations in the upper trapezius during muscular rest and low-level contractions. A new method of ECG contamination removal, filtered template subtraction (FTS), is described and compared to 30 Hz high-pass filter (HPF) and averaged template subtraction (ATS) methods. FTS creates a unique template of each ECG artifact using a low-pass filtered copy of the contaminated signal, which is subtracted from contaminated periods in the original signal. ECG contamination results in an over-estimation of EMG amplitude during rest in the upper trapezius, with negligible effects on amplitude and frequency estimations during low-intensity isometric contractions. FTS and HPF successfully removed ECG contamination from periods of muscular rest, yet introduced errors during muscle contraction. Conversely, ATS failed to fully remove ECG contamination during muscular rest, yet did not introduce errors during muscle contraction. The relative advantages and disadvantages of different ECG contamination removal methods should be considered in the context of the specific motor tasks that require analysis.     

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。     

利用Poincare 散点图法检测t 波交替的指标研究

目的:利用Poincare散点图进行t波交替检测,不仅从形态上找到检测标准,进一步研究散点中有效的定量指标。方法:以European ST-T Database标准心电数据库和MIT-BIH心律失常数据库的心电信号作为检测对象,以128个连续心拍的t波中的7个点为检测数据,相邻心拍t波差分后组成新序列,并由差分序列作出散点图,观察散点形态。根据形态区别和t波交替的幅值变化特点,利用个散点到x+y=0直线的距离均值作为定量检测指标D0,为避免不同心电信号幅值影响,D0除以RQ峰值差为最终指标D,找出合适阈值判定是否存在t波交替,并与谱分析法的检测结果比较分析。结果:①从Poincare散点图形态上,存在t波交替的散点图与正常t波存在明显区别,存在t波交替则散点集中在以x+y=0为轴线的附近,形成类似椭圆的狭长形状;而正常t波形成的散点会以原点为中心均匀分布,散点形态为圆形。②由t波交替的特点和散点图形态可知,定量检测指标D越小,就越有可能存在t波交替。经过大量仿真测试和谱分析法的比较,规定检测标准为,当D<=35uv时,存在t波交替;指标D与谱分析法的结论相吻合,并且两种方法的判定结果由kappa一致性检验,一致性程度好,进一步说明D指标具有优越的敏感性,是t波交替检测的有效指标。结论:Poincare散点图的散点分布形态和散点到x+y=0轴线的距离均值分别是是t波交替有效的定性和定量检测指标。     

A rapid TRIzol-based two-step method for DNA-free RNA extraction from Arabidopsis siliques and dry seeds

Extraction of high-quality RNA from Arabidopsis seeds has been a challenge. Here we report a two-step TRIzol-based procedure for RNA extraction from Arabidopsis siliques and dry seeds. This procedure employs a modified, high pH (pH 9.5) extraction buffer. High pH plus the addition of either DTT or β-mercaptoethanol in the extraction buffer effectively inhibits RNase activity during the extraction, and removes most polysaccharides, polyphenols and other insoluble material. TRIzol reagent was subsequently used to purify the RNA. Using this procedure we isolated high-quality DNA-free RNA samples without DNase I treatment from Arabidopsis seeds or siliques in less than 3 h.     

Isolation of Fetal ECG Signals from Abdominal ECG Using Wavelet Analysis

ObjectiveMonitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance.MethodElectrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG.ResultsThe FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm.ConclusionThe proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.     

Multivariate and multiorgan analysis of cardiorespiratory variability signals: the CAP sleep case.

Signals from different systems are analyzed during sleep on a beat-to-beat basis to provide a quantitative measure of synchronization with the heart rate variability (HRV) signal, oscillations of which reflect the action of the autonomic nervous system. Beat-to-beat variability signals synchronized to QRS occurrence on ECG signals were extracted from respiration, electroencephalogram (EEG) and electromyogram (EMG) traces. The analysis was restricted to sleep stage 2. Cyclic alternating pattern (CAP) periods were detected from EEG signals and the following conditions were identified: stage 2 non-CAP (2 NCAP), stage 2 CAP (2 CAP) and stage 2 CAP with myoclonus (2 CAP MC). The coupling relationships between pairs of variability signals were studied in both the time and frequency domains. Passing from 2 NCAP to 2 CAP, sympathetic activation is indicated by tachycardia and reduced respiratory arrhythmia in the heart rate signal. At the same time, we observed a marked link between EEG and HRV at the CAP frequency. During 2 CAP MC, the increased synchronization involved myoclonus and respiration. The underlying mechanism seems to be related to a global control system at the central level that involves the different systems.     

Frequency analysis of atrial fibrillation from the surface electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy.     

Simulation analysis of interference EMG during fatiguing voluntary contractions. Part II--changes in amplitude and spectral characteristics.

Capabilities of amplitude and spectral methods for information extraction from interference EMG signals were assessed through simulation and preliminary experiment. Muscle was composed of 4 types of motor units (MUs). Different hypotheses on changes in firing frequency of individual MUs, intracellular action potential (IAP) and muscle fibre propagation velocity (MFPV) during fatigue were analyzed. It was found that changes in amplitude characteristics of interference signals (root mean square, RMS, or integrated rectified value, IEMG) detected by intramuscular and surface electrodes differed. RMS and IEMG of surface detected interference signals could increase even under MU firing rate reduction and without MU synchronisation. IAP profile lengthening can affect amplitude characteristics more significantly than MU firing frequency. Thus, an increase of interference EMG amplitude is unreliable to reflect changes in the neural drive. The ratio between EMG amplitude and contraction response can hardly characterise the so-called 'neuromuscular efficiency'. The recently proposed spectral fatigue indices can be used for quantification of interference EMG signals. The indices are practically insensitive to MU firing frequency. IAP profile lengthening and decrease in MFPV enhanced the index value, while recruitment of fast fatigable MUs reduced it. Sensitivity of the indices was higher than that of indices traditionally used.     

Automatic estimation of the correlation dimension for the analysis of electrocardiograms

The main purpose of the present work is the definition of a fully automatic procedure for correlation dimension (D₂) estimation. In the first part, the procedure for the estimation of the correlation dimension (D₂) is proposed and tested on various types of mathematical models: chaotic (Lorenz and Henon models), periodical (sinusoidal waves) and stochastic (Gaussian and uniform noise). In all cases, accurate D₂ estimates were obtained. The procedure can detect the presence of multiple scaling regions in the correlation integral function. The connection between the presence of multiple scaling regions and multiple dynamic activities cooperating in a system is investigated through the study of composite time series. In the second part of the paper, the proposed algorithm is applied to the study of cardiac electrical activity through the analysis of electrocardiographic signals (ECG) obtained from the commercially available MIT-BIH ECG arrhythmia database. Three groups of ECG signals have been considered: the ECGs of normal subjects and ECGs of subjects with atrial fibrillation and with premature ventricular contraction. D₂ estimates are computed on single ECG intervals (static analysis) of appropriate duration, striking a balance between stationarity requisites and accurate computation requirements. In addition, D₂ temporal variability is studied by analyzing consecutive intervals of ECG tracings (dynamic analysis). The procedure reveals the presence of multiple scaling regions in many ECG signals, and the D₂ temporal variability differs in the three ECG groups considered; it is greater in the case of atrial fibrillation than in normal sinus rhythms. This study points out the importance of considering both the static and dynamic D₂ analysis for a more complete study of the system under analysis. While the static analysis visualizes the underlying heart activity, dynamic D₂ analysis insights the time evolution of the underlying system. Received: 11 April 1997 / Accepted in revised form: 19 March 1999