A successive cancellation algorithm for fetal heart-rate estimation using an intrauterine ECG signal

In this paper, we present a two-stage successive cancellation (SC) algorithm that sequentially separates fetal and maternal heartbeats from an intrauterine electrocardiogram (IuECG) signal containing both fetal and maternal QRS complexes. The ECG signal is modeled as a series of fetal, maternal, and noise events. Peak detection is first employed to locate the potential fetal and maternal QRS complexes, referred to as candidate events. Each stage automatically generates a template of a source from the candidate events in the initialization period, and thereafter performs classification of the remaining candidate events based on a template matching technique. The detected events of the stronger signal are subtracted from the composite ECG signal prior to initialization and classification of the weaker signal. Once the fetal and maternal complexes are successfully detected and separated, a counting mechanism is utilized to derive the corresponding heart rates. Computer simulation results on real IuECG data demonstrate the effectiveness of the SC algorithm.     

Neural-network-based adaptive matched filtering for QRS detection

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.     

基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

基于生成对抗网络的动态心电信号QRS波检测

心电信号分析是预防心血管疾病的重要举措,QRS波的精确检测不仅是心电信号处理的关键步骤且对心率计算和异常情况分析具有重要作用.针对动态心电信号存在信号质量差或异常节奏波形导致常用QRS波检测方法精度较低的问题,本文提出了 一种基于生成对抗网络新型QRS波检测算法.该算法以Pix2Pix网络为基础,生成网络采用U-Net...     

基于小波和扩展卡尔曼的心电检测

文中将小波变换和扩展卡尔曼滤波器相结合,利用小波变换多尺度多分辨的特点,将心电信号进行分解。然后对心电信号在各尺度上进行扩展卡尔曼滤波。最后在扩展卡尔曼滤波的输出结果上进行QRS波形检测。文中方法经MIT-BIH心电数据库检验,QRS波Se（探测灵敏度）在99.40%以上,同时,QRS＋P（正探测率）在99.39%以上,提高了心电信号检测的正确率。     

QRS slopes for detection and characterization of myocardial ischemia.

In this study, the upward I(US) and downward I(DS) slopes of the QRS complex are proposed as indices for quantifying ischemia-induced electrocardiogram (ECG) changes. Using ECG recordings acquired before and during percutaneous transluminal coronary angioplasty (PTCA), it is found that the QRS slopes are considerably less steep during artery occlusion, in particular for I(DS). With respect to ischemia detection, the slope indices outperform the often used high-frequency index (defined as the root mean square (rms) of the bandpass-filtered QRS signal for the frequency band 150-250 Hz) as the mean relative factors of change are much larger for I(US) and I(DS) than for the high-frequency index (6.9 and 7.3 versus 3.7). The superior performance of the slope indices is equally valid when other frequency bands of the high-frequency index are investigated (the optimum one is found to be 125-175 Hz). Employing a simulation model in which the slopes of a template QRS are altered by different techniques, it is found that the slope changes observed during PTCA are mostly due to a widening of the QRS complex or a decrease of its amplitudes, but not a reduction of its high-frequency content or a combination of this and the previous effects. It is concluded that QRS slope information can be used as an adjunct to the conventional ST segment analysis in the monitoring of myocardial ischemia.     

Detection of Fetal Heart Rate Through 3-D Phase Space Analysis From Multivariate Abdominal Recordings

A novel three-stage methodology for the detection of fetal heart rate (fHR) from multivariate abdominal ECG recordings is introduced. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected, using band-pass filtering and phase space analysis. The maternal fiducial points are used to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, two denoising procedures are applied to enhance the fetal QRS complexes. The phase space characteristics are employed to identify fetal heart beats not overlapping with the maternal QRSs, which are eliminated in the first stage. The extraction of the fHR is accomplished in the third stage, using a histogram-based technique in order to identify the location of the fetal heart beats that overlap with the maternal QRSs. The methodology is evaluated on simulated multichannel ECG signals, generated by a recently proposed model with various SNRs, and on real signals, recorded from pregnant women in various weeks during gestation. In both cases, the obtained results indicate high performance; in the simulated ECGs, the accuracy ranges from 72.78% to 98.61%, depending on the employed SNR, while in the real recordings, the average accuracy is 95.45%. The proposed methodology is advantageous since it copes with the existence of noise from various sources while it is applicable in multichannel abdominal recordings.      

A new method for the extraction of fetal ECG from the composite abdominal signal

We developed a wavelet transform-based method to extract the fetal electrocardiogram (ECG) from the composite abdominal signal. This is based on the detection of the singularities obtained from the composite abdominal signal, using the modulus maxima in the wavelet domain. Modulus maxima locations of the abdominal signal are used to discriminate between maternal and fetal ECG signals. Two different approaches have been considered. In the first approach, at least one thoracic signal is used as the a prior to perform the classification whereas in the second approach no thoracic signal is needed. A reconstruction method is utilized to obtain the fetal ECG signal from the detected fetal modulus maxima. The proposed technique is different from the classical time-domain methods, in that we exploit the most distinct features of the signal, leading to more robustness with respect to signal perturbations. Results of experiments with both synthetic and real ECG data have been presented to demonstrate the efficacy of the proposed method.     

Applications of adaptive filtering to ECG analysis: noisecancellation and arrhythmia detection

Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm.     

Extraction of fetal electrocardiogram using adaptive neuro-fuzzy inference systems

In this paper, we investigate the use of adaptive neuro-fuzzy inference systems (ANFIS) for fetal electrocardiogram (FECG) extraction from two ECG signals recorded at the thoracic and abdominal areas of the mother's skin. The thoracic ECG is assumed to be almost completely maternal (MECG) while the abdominal ECG is considered to be composite as it contains both the mother's and the fetus' ECG signals. The maternal component in the abdominal ECG signal is a nonlinearly transformed version of the MECG. We use an ANFIS network to identify this nonlinear relationship, and to align the MECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the FECG component by subtracting the aligned version of the MECG signal from the abdominal ECG signal. We validate our technique on both real and synthetic ECG signals. Our results demonstrate the effectiveness of the proposed technique in extracting the FECG component from abdominal signals of very low maternal to fetal signal-to-noise ratios. The results also show that the technique is capable of extracting the FECG even when it is totally embedded within the maternal QRS complex.     

QRS feature extraction using linear prediction

This communication proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. This communication also indicates that the prediction order need not be more than two for fast arrhythmia detection. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex, the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to a set of three states pulse-code train relative to the original ECG signal. The pulse-code train has the advantage of easy implementation in digital hardware circuits to achieve automated ECG diagnosis. The algorithm performs very well in feature extraction in arrhythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contraction) detection has at least a 92 percent sensitivity for MIT/BIH arrhythmia database.     

Wavelet packets feasibility study for the design of an ECG compressor

Most of the recent electrocardiogram (ECG) compression approaches developed with the wavelet transform are implemented using the discrete wavelet transform. Conversely, wavelet packets (WP) are not extensively used, although they are an adaptive decomposition for representing signals. In this paper, we present a thresholding-based method to encode ECG signals using WP. The design of the compressor has been carried out according to two main goals: (1) The scheme should be simple to allow real-time implementation; (2) quality, i.e., the reconstructed signal should be as similar as possible to the original signal. The proposed scheme is versatile as far as neither QRS detection nor a priori signal information is required. As such, it can thus be applied to any ECG. Results show that WP perform efficiently and can now be considered as an alternative in ECG compression applications.     

New Concepts for PVC Detection

A new scheme is proposed for the detection of premature ventricular beats, which is a vital function in rhythm monitoring of cardiac patients. A transformation based on the first difference of the digitized electrocardiogram (ECG) signal is developed for the detection and delineation of QRS complexes. The method for classifying the abnormal complexes from the normal ones is based on the concepts of minimum phase and signal length. The parameters of a linear discriminant function obtained from a training feature vector set are used to classify the complexes. Results of application of the scheme to ECG of two arrhythmia patients are presented.     

Denoising and R-Peak Detection of Electrocardiogram Signal Based on EMD and Improved Approximate Envelope

The electrocardiogram (ECG ) signal is prone to various high and low frequency noises, including baseline wandering and power-line interference, which become the source of errors in QRS and in other extracted features. This paper presents a new ECG signal-processing approach based on empirical mode decomposition (EMD) and an improved approximate envelope method. To reduce the number of the initial intrinsic mode functions (IMFs), a Butterworth lowpass filter is used to eliminate high frequency noises before the EMD. To correct baseline wandering and to eliminate low frequency noises, the two last-order IMFs are abandoned. An improved approximate envelope is proposed and applied after the Hilbert transform to enhance the energy of QRS complexes and to suppress unwanted P/T waves and noises. Then, an algorithm based on the slope threshold is used for R-peak detection. The proposed denoising and R-peak detection algorithm are validated using the MIT-BIH Arrhythmia Database. The simulation results show that the proposed method can effectively eliminate the Gaussian noise, baseline wander, and power-line interference added to the ECG signal. The method can also function reliably even under poor signal quality and with long P and T peaks. The QRS detector has an average sensitivity of Se=99.94 % and a positive predictivity of +P=99.87 % over the first lead of the MIT-BIH Arrhythmia Database.     

Analysis of first-derivative based QRS detection algorithms.

Accurate QRS detection is an important first step for the analysis of heart rate variability. Algorithms based on the differentiated ECG are computationally efficient and hence ideal for real-time analysis of large datasets. Here, we analyze traditional first-derivative based squaring function (Hamilton-Tompkins) and Hilbert transform-based methods for QRS detection and their modifications with improved detection thresholds. On a standard ECG dataset, the Hamilton-Tompkins algorithm had the highest detection accuracy (99.68% sensitivity, 99.63% positive predictivity) but also the largest time error. The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination. The high accuracy of the Hilbert transform-based method compared to detection with the second derivative of the ECG is ascribable to its inherently uniform magnitude spectrum. For all algorithms, detection errors occurred mainly in beats with decreased signal slope, such as wide arrhythmic beats or attenuated beats. For best performance, a combination of the squaring function and Hilbert transform-based algorithms can be applied such that differences in detection will point to abnormalities in the signal that can be further analyzed.     

Wavelet transform-based QRS complex detector

In this paper, we describe a QRS complex detector based on the dyadic wavelet transform (Dy WT) which is robust to time-varying QRS complex morphology and to noise. We design a spline wavelet that is suitable for QRS detection. The scales of this wavelet are chosen based on the spectral characteristics of the electrocardiogram (ECG) signal. We illustrate the performance of the Dy WT-based QRS detector by considering problematic ECG signals from the American Heart Association (AHA) data base. Seventy hours of data was considered. We also compare the performance of Dy WT-based QRS detector with detectors based on Okada, Hamilton-Tompkins, and multiplication of the backward difference algorithms. From the comparison, results we observed that although no one algorithm exhibited superior performance in all situations, the Dy WT-based detector compared well with the standard techniques. For multiform premature ventricular contractions, bigeminy, and couplets tapes, the Dy WT-based detector exhibited excellent performance.     

Digital fractional order operators for R-wave detection in electrocardiogram signal

In this study, we present an effective R-wave detection method in the QRS complex of the electrocardiogram (ECG) based on digital differentiation and integration of fractional order. The detection algorithm is performed in two steps. The pre-processing step is based on a fractional order digital band-pass filter whose fractional order is obtained by maximising the signal to noise ratio of the ECG signal, followed by a five points differentiator of fractional order 1.5 then the squaring transformation and the smoothing are used to generate peaks corresponding to the ECG parts with high slopes. The detection step is a new and simple strategy which is also based on fractional order operators for the localisation of the R waves. The MIT/BIH arrhythmia database is used to test the effectiveness of the proposed method. The algorithm has provided very good performance and has achieved about 99.86% of the detection rate for the standard database. The results obtained are presented, discussed and compared to the most recent and efficient R-wave detection algorithms.     

Detection of ECG characteristic points using wavelet transforms

An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious base line drift and noise     

Digital Filters for Real-Time ECG Signal Processing Using Microprocessors

Traditionally, analog circuits have been used for signal conditioning of electrocardiograms. As an alternative, algorithms implemented as programs on microprocessors can do similar filtering tasks. Also, digital filter algorithms can perform processes that are difficult or impossible using analog techniques. Presented here are a set of real-time digital filters each implemented as a subroutine. By calling these subroutines in an appropriate sequence, a user can cascade filters together to implement a desired filtering task on a single microprocessor. Included are an adaptive 60-Hz interference filter, two low-pass filters, a high-pass filter for eliminating dc offset in an ECG, an ECG data reduction algorithm, band-pass filters for use in QRS detection, and a derivative-based QRS detection algorithm. These filters achieve real-time speeds by requiring only integer arithmetic. They can be implemented on a diversity of available microprocessors.     

Modeling QRS complex in dECG

It is proposed to model the derivative of electrocardiogram (ECG) signal, which we refer to as dECG, instead of the ECG signal. It is shown that the QRS complex in the dECG signal can be represented in the frequency domain by an all-pole model of appropriate order, the coefficients of the model being determined using the covariance method of linear prediction applied over an analysis interval that includes the QRS complex and that is centered about the R-peak. Modeling of dECG, instead of ECG, gives a better spectral representation of the QRS complex.