Frequency analysis of the electrocardiogram with maximum entropymethod for identification of patients with sustained ventriculartachycardia

Late potentials in the terminal phase of the QRS-complex during sinus rhythm have been proposed to identify a subgroup of patients with myocardial infarction at risk of ventricular tachycardia (VT). Frequency analysis of the ECG with Fourier transform (FFT) has been applied for detection of these microvolt level signals, but is limited by poor frequency resolution of short data segments and spectral leakage. We therefore developed frequency analysis using the maximum entropy method (MEM) based on an autoregressive (AR) model. Orthogonal electrocardiograms were recorded from the body surface of patients with and without VT, and healthy persons after low noise, high-gain amplification. Multiple 40 ms segments (time intervals 2 ms, AR-parameters tapered) were analyzed (spectrotemporal mapping): low-frequency components were eliminated by building difference spectra with optimal high order and fixed low order. The MEM-spectra revealed high frequency components (40-200 Hz) in the terminal phase of the QRS-complex and in the ST-section in 26/38 patients with VT, but only in 2/20 without VT and in 1/20 healthy persons (p less than 0.05). Unlike FFT, MEM allowed localization of late potentials by the analysis of short data segments. Thus, MEM offers promise for noninvasive identification of patients with sustained VT after myocardial infarction and detailed analysis of late potentials.     

Cancellation of ventricular activity in the ECG: evaluation of novel and existing methods

Due to the much higher amplitude of the electrical activity of the ventricles in the surface electrocardiogram (ECG), its cancellation is crucial for the analysis and characterization of atrial fibrillation. In this paper, two different methods are proposed for this cancellation. The first one is an average beat subtraction type of method. Two sets of templates are created: one set for the ventricular depolarization waves and one for the ventricular repolarization waves. Next, spatial optimization (rotation and amplitude scaling) is applied to the QRS templates. The second method is a single beat method that cancels the ventricular involvement in each cardiac cycle in an independent manner. The estimation and cancellation of the ventricular repolarization is based on the concept of dominant T and U waves. Subsequently, the atrial activities during the ventricular depolarization intervals are estimated by a weighted sum of sinusoids observed in the cleaned up segments. ECG signals generated by a biophysical model as well as clinical ECG signals are used to evaluate the performance of the proposed methods in comparison to two standard ABS-based methods.     

Ventricular tachycardia and fibrillation detection by a sequentialhypothesis testing algorithm

An algorithm for detecting ventricular fibrillation (VF) and ventricular tachycardia (VT) by the method of sequential hypothesis testing is presented. The algorithm first generates a binary sequence by comparing the signal to a threshold. The probability distribution of the time intervals of the binary sequence is obtained, and Wald's sequential hypothesis testing procedure is next employed to discriminate the arrhythmias. Sequential hypothesis testing of 85 cases resulted in identification of 1) 97.64% VF and 97.65% VT episodes after 5 s, and 2) 100% identification of both VF and VT after 7 s. The desired false positive and false negative error probabilities can be preprogrammed into the algorithm. An important feature of the sequential method is that extra time for detection can be traded off for improved accuracy, and vice versa.     

A robust sequential detection algorithm for cardiac arrhythmiaclassification

In ibid., vol. 37, no. 9, p. 837-43 (1990) and Proc. IEEE 9th Annu. Conf. Eng. Med. Biol. Soc., p. 918-19 (1988) N.V. Thakor et al. describe a sequential probability ratio test (SPRT) based on threshold crossing intervals (TCI) for the discrimination of ventricular fibrillation (VF) from ventricular tachycardia (VT). However, in applying their algorithm to data from the MIT-BIH malignant arrhythmia database, the authors observed some overlap in the distributions of TCI for VF and VT resulting in 16% overall error rate for the discrimination. In this communication, the authors describe a modified SPRT algorithm, using a new feature dubbed blanking variability (BV) as the basis for discrimination. Using the MIT-BIH database, the preliminary results showed that the proposed method decreases the overall error rate to 5%     

A two-stage discrimination of cardiac arrhythmias using a total least squares-based prony modeling algorithm

In this paper, we describe a new approach for the discrimination among ventricular fibrillation (VF), ventricular tachycardia (VT) and superventricular tachycardia (SVT) developed using a total least squares (TLS)-based Prony modeling algorithm. Two features, dubbed energy fractional factor (EFF) and predominant frequency (PF), are both derived from the TLS-based Prony model. In general, EFF is adopted for discriminating SVT from ventricular tachyarrhythmias (i.e., VF and VT) first, and PF is then used for further separation of VF and VT. Overall classification is achieved by performing a two-stage process to the indicators defined by EFF and PF values, respectively. Tests conducted using 91 episodes drawn from the MIT-BIH database produced optimal predictive accuracy of (SVT, VF, VT) = (95.24%, 96.00%, 97.78%). A data decimation process is also introduced in the novel method to enhance the computational efficiency, resulting in a significant reduction in the time required for generating the feature values.     

CPR artifact removal from human ECG using optimal multichannel filtering

The purpose of this study was to assess whether the artifacts presented by precordial compressions during cardiopulmonary resuscitation could be removed from the human electrocardiogram (ECG) using a filtering approach. This would allow analysis and defibrillator charging during ongoing precordial compressions yielding a very important clinical improvement to the treatment of cardiac arrest patients. In this investigation we started with noise-free human ECGs with ventricular fibrillation (VF) and ventricular tachycardia (VT) records. To simulate a realistic resuscitation situation, we added a weighted artifact signal to the human ECG, where the weight factor was chosen to provide the desired signal-to-noise ratio (SNR) level. As artifact signals we used ECGs recorded from animals in asystole during precordial compressions at rates 60, 90, and 120 compressions/min. The compression depth and the thorax impedance was also recorded. In a real-life situation such reference signals are available and, using an adaptive multichannel Wiener filter, we construct an estimate of the artifact signal, which subsequently can be subtracted from the noisy human ECG signal. The success of the proposed method is demonstrated through graphic examples, SNR, and rhythm classification evaluations.     

Detecting ventricular tachycardia and fibrillation by complexity measure

Sinus rhythm (SR), ventricular tachycardia (VT) and ventricular fibrillation (VF) belong to different nonlinear physiological processes with different complexity. In this study, we present a novel, and computationally fast method to detect VT and VF, which utilizes a complexity measure suggested by Lempel and Ziv [1]. For a specific window length (i.e., the length of data segment to be analyzed), the method first generates a 0-1 string by comparing the raw electrocardiogram (ECG) data to a selected suitable threshold. The complexity measure can be obtained from the 0-1 string only using two simple operations, comparison and accumulation. When the window length is 7 s, the detection accuracy for each of SR, VT, and VF is 100% for a test set of 204 body surface records (34 SR, 85 monomorphic VT, and 85 VF). Compared with other conventional time- and frequency-domain methods, such as rate and irregularity, VF-filter leakage, and sequential hypothesis testing, the new algorithm is simple, computationally efficient, and well suited for real-time implementation in automatic external defibrillators (AED's).     

Machine learning approach to recognize ventricular arrhythmias using VMD based features

The occurrence of life-threatening ventricular arrhythmias (VAs) such as Ventricular tachycardia (VT) and Ventricular fibrillation (VF) leads to sudden cardiac death which requires detection at an early stage. The main aim of this work is to develop an automated system using machine learning tool for accurate prediction of VAs that may reduce the mortality rate. In this paper, a novel method using variational mode decomposition (VMD) based features and C4.5 classifier for detection of ventricular arrhythmias is presented. The VMD model was used to decompose the electrocardiography (ECG) signals to extract useful informative features. The method was tested for ECG signals obtained from PhysioNet database. Two standard databases i.e. CUDB (Creighton University Ventricular Tachyarrhythmia Database) and VFDB (MIT-BIH Malignant Ventricular Ectopy Database) were considered for this work. A set of time–frequency features were extracted and ranked by the gain ratio attribute evaluation method. The ranked features are subjected to support vector machine (SVM) and C4.5 classifier for classification of normal, VT and VF classes. The best detection was obtained with sensitivity of 97.97%, specificity of 99.15%, and accuracy of 99.18% for C4.5 classifier with a 5 s data analysis window. These results were better than SVM classifier result having an average accuracy of 86.87%. Hence, the proposed method demonstrates the efficiency in detecting the life-threatening VAs and can serve as an assistive tool to clinicians in the diagnosis process.

     

A low-complexity intracardiac electrogram compression algorithm

Implantable cardioverter defibrillators (ICD's) detect, diagnose and treat the potentially fatal heart arrhythmias known as bradycardia, ventricular tachycardia (VT), and ventricular fibrillation (VF) in cases where these arrhythmias are resistant to surgical and drug-based treatments by direct sensing and electrical stimulation of the heart muscle. Since the ICD is implanted, power consumption, reliability, and size are severe design constraints. This paper targets the problems associated with increasing the signal recording capabilities of an ICD. A data-compression algorithm is described which has been optimized for low power consumption and high reliability implementation. Reliance on a patient's morphology or that of a population of patients is avoided by adapting to the intracardiac electrogram (ICEG) amplitude and phase variations and by using adaptive scalar quantization. The algorithm is compared to alternative compression algorithms which are also patient independent using a subset of VT arrhythmias from a data base of 146 patients. At low distortion the algorithm is closest to the Shannon lower bound achieving an average of 3.5 b/sample at 5% root mean square distortion for a 250-Hz sample rate. At higher distortion vector quantization and Karhunen-Loeve Transform approaches are superior but at the cost of considerable additional computational complexity     

Linear Amplification with Nonlinear Components

A technique for producing bandpass linear amplification with nonlinear components (LINC) is described. The bandpass signal first is separated into two constant envelope component signals. All of the amplitude and phase information of the original bandpass signal is contained in phase modulation on the component signals. These constant envelope signals can be amplified or translated in frequency by amplifiers or mixers which have nonlinear input-output amplitude transfer characteristics. Passive linear combining of the amplified and/or translated component signals produces an amplified and/or translated replica of the original signal.     

LFM-CW雷达中的距离分段——欠采样处理方法

本文提出了一种用于线性调频连续波(LFM-CW)雷达的信号处理新方法.该方法用带通滤波器在频域内实现差拍信号的距离分段,然后用第一距离段所需采样率对每个距离段进行采样和FFT处理。在距离分辨单元数很大时,这种方法可以大大降低LFM-CW雷达对信号处理器的要求。     

Autoregressive modeling of epicardial electrograms duringventricular fibrillation

During ventricular fibrillation (VF), electrograms from bipolar epicardial electrodes generally appear to have little organization or structure. The authors sought to identify any well-defined organization or structure in these signals by determining if they could be modeled as an autoregressive (AR) stochastic process with a white noise excitation during the short time period (6.5-8 s) typically used by automatic implantable defibrillators. The AR model is then used to synthesize VF signals, which are compared with the original VF signal for each patient. The results indicate that the RMS amplitudes of the synthesized waveforms are similar to those of the true waveforms. Although the synthesized signals had higher rate, more regular RR intervals, more zero crossings per second, and less time spent at baseline than the signal from which they were generated, these differences are generally not significant (p⩾0.05). The use of such synthesized VF signals may allow more thorough testing of VF detection algorithms than is possible with the present limited libraries of human VF recordings     

Removal of CPR artifacts from the ventricular fibrillation ECG by adaptive regression on lagged reference signals

BACKGROUND AND OBJECTIVE: Removing cardiopulmonary resuscitation (CPR)-related artifacts from human ventricular fibrillation (VF) electrocardiogram (ECG) signals provides the possibility to continuously detect rhythm changes and estimate the probability of defibrillation success. This could reduce "hands-off" analysis times which diminish the cardiac perfusion and deteriorate the chance for successful defibrillations. METHODS AND RESULTS: Our approach consists in estimating the CPR part of a corrupted signal by adaptive regression on lagged copies of a reference signal which correlate with the CPR artifact signal. The algorithm is based on a state-space model and the corresponding Kalman recursions. It allows for stochastically changing regression coefficients. The residuals of the Kalman estimation can be identified with the CPR-filtered ECG signal. In comparison with ordinary least-squares regression, the proposed algorithm shows, for low signal-to-noise ratio (SNR) corrupted signals, better SNR improvements and yields better estimates of the mean frequency and mean amplitude of the true VF ECG signal. CONCLUSIONS: The preliminary results from a small pool of human VF and animal asystole CPR data are slightly better than the results of comparable previous studies which, however, not only used different algorithms but also different data pools. The algorithm carries the possibility of further optimization.     

Analysis of beat-to-beat variability of frequency contents in theelectrocardiogram using two-dimensional Fourier transforms

Late potentials are very small signals (1-20 μV) in the surface ECG with high-frequency components, which are found in patients prone to sustained ventricular tachycardia. Evaluation of these signals requires either very sophisticated recording techniques for single-beat analysis or signal averaging. Signal averaging, however, might disregard information about risk stratification. Therefore, the authors developed the Single-Beat Spectral Variance (SBSV) based on two-dimensional (2-D) Fourier transform of 80 ms segments of 128 consecutive beats. This approach depicts the beat-to-beat variability of the frequency contents of these ECG segments. An index function enables an objective detection of late potentials. The authors investigated 35 patients after myocardial infarction and sustained ventricular tachycardia (Group 1), 50 patients after myocardial infarction without ventricular arrhythmias (Group 2) and ten healthy volunteers, SBSV classified 29 of 35 patients (83%) of Group 1 as pathologic, 14 of these 29 patients (48%) exclusively on the basis of marked Wenckebach-like conduction pattern. In Group 2, only five of 50 patients showed abnormal SBSV. In Group 3, the authors found no pathologic result. Thus, SBSV is a promising new method to investigate late potentials inpatients after myocardial infarction, SBSV-contains not only the results of frequency analysis after signal averaging, but also evaluates variable ECG components     

基于EEMD与多重分形的心电信号特征提取与分类

心电信号的快速分类在心脏病医学诊断领域具有至关重要的作用,为了降低人工识别的成本,提高心电信号分类的准确率。文章以正常搏动、房性早搏、室性早搏、左束支传导阻滞及右束支传导阻滞信号为研究对象,用集合经验模态分解分解心电信号,并结合相关系数来选取本征模态函数进行重构心电信号。从心电信号的非线性动力学角度出发,用多重分形理论进行分析,研究其质量指数曲线、广义分形维数和多重分形谱,提取合适的多重分形特征,用于支持向量机的训练。实验结果表明,用该方法训练测试30次得到的分类准确率平均值为96.09%,单次实验对正常搏动、左束支传导阻滞信号的分类精确率可达97%以上,证明该方法在心电信号分类中的有效性。     

A new method for measuring small local vibrations in the heartusing ultrasound

In order to diagnose ventricular dysfunction based on the acoustic characteristics of the heart muscle of the ventricle, it is necessary to detect vibration signals from various parts of the ventricular wall. This is, however, difficult using previously proposed ultrasonic diagnostic methods or systems. The reason is that the amplitude of the cardiac motion is large during one beat period which produces large fluctuations in the transit time required for ultrasonic waves to travel from the transducer to the heart and back. This paper proposes a new method for overcoming this problem and accurately measuring small vibrations of the ventricle wall using ultrasound. In this method, the demodulated ultrasound signal reflected at the heart wall is converted from analogue to digital (A/D) signal at a high sampling frequency; from the resultant digital signal, the velocity of the wall is accurately obtained over a wide dynamic range based on the Doppler effect. The proposed method is preliminarily applied to the detection of small vibrations on the aortic wall and the interventricular septum. The new method offers potential for research in acoustical diagnosis of heart and artery dysfunction     

A generalization of nonuniform bandpass sampling

Nth-order nonuniform sampling is described for generalized bandpass signal frequency position, bandwidth, sampling rate, frequency-shift and phase-shift. A bandpass extension to the Nyquist criterion is derived, showing that restrictions on bandpass frequency position for odd orders of nonuniform sampling tend to zero as N tends to infinity. Bandpass interpolants based on the sinc function are derived for the generalized Nth-order sampled bandpass signals. It is shown that, for minimum (Nyquist) rate sampling, these interpolants are comprised of N bandpass filters, each with independent phase. The number of bandpass filters comprising the interpolant is found to decrease as the sample rate increases. The advantage of describing Nth-order sampling as the Nth replication and uniform sampling of a signal is demonstrated. Finally, digital implementation of the Nth-order bandpass sampling interpolants is discussed. It is established that it is not practicable to attempt to perform nonuniform bandpass sampling at the theoretical minimum rate, where the interpolation is to be performed digitally     

Measurement of the statistics of DFB laser frequency fluctuations

The statistics of the frequency of the bandpass filtered beat signal between a DFB-laser and an external cavity laser were measured. The distribution of frequency fluctuations from the centre frequency is shown to be very close to Gaussian, with the variance given by the product of beat signal linewidth and IF-filter bandwidth     

On the representation of CPM signals by linear superposition ofimpulses in the bandpass domain

A novel signal generation concept for continuous phase modulations (CPMs) with modulation index 1/2 based on real impulses is presented. With this concept, bandpass CPM signals can be generated directly in one step instead of the two consecutive steps, namely, the generation of the complex envelope and the modulation of the carrier by the complex envelope, which are necessary in conventional signal generators. Mathematical expressions for both the real impulses and the bandpass CPM signals are derived and a simple modulator structure is discussed. Examples for the real impulses are given. Among these are the well-known CPM schemes of minimum shift keying (MSK), sinusoidal frequency shift keying (SFSK), and Gaussian minimum shift keying (GMSK). As an example, the validity of the novel signal generation concept is shown for the latter CPM scheme     

Modeling the relationship between concurrent epicardial action potentials and bipolar electrograms

A signal analysis approach to building the relationship between concurrent epicardial cell action potentials (AP's) and bipolar electrograms is presented. Wavelet network, one nonlinear black-box modeling method, is used to identify the relationship between cell AP's and bipolar electrocardiograms. The electrical signals were simultaneously measured from the epicardium of isolated Langendorff-perfused rabbit hearts during three different rhythm conditions: normal sinus rhythm (NSR), normal sinus rhythm after ischemia (NSRI), and ventricular fibrillation (VF). For NSR and NSRI, the proposed modeling method successfully captures the nonlinear input-output relationship and provides an accurate output, but the method fails in case of VF. This result suggests that a time-invariant nonlinear modeling method such as wavelet network is not appropriate for VF rhythm, which is thought to be time-varying as well as chaotic, but still useful in detection of VF. A new arrhythmia detection algorithm, with potential application in implantable devices, is proposed for identifying the time of rhythmic bifurcation.