On the detection of QRS variations in the ECG

Detection of subtle beat-to-beat variations in the morphology of the ECG is complicated by the effects of alignment errors and respiration. A method of directly estimating the alignment error (trigger jitter) from an ECG is derived by relating the variance to the squared slope of the averaged QRS complex. Results based on recordings obtained from 12 normal subjects and alignment performed by the cross-correlation method showed that the alignment errors were dependent upon the choice of the alignment channel, with the best distribution of the errors occurring when alignment was based on the vector magnitude of the three orthogonal leads. The estimated average alignment errors ranged from 0.33-0.42 ms, which were near the optimal value of 0.29 ms based on the sampling rate of 1000 samples/s. It was shown that the effects of respiration could be reduced by normalizing the amplitude of the QRS complexes. It was also estimated that a significant proportion of the variation (0.54-0.67) in the normalized ECG's could be attributed to alignment errors and noise     

A novel method for beat-to-beat detection of ventricular late potentials

A novel method for beat-to-beat detection of ventricular late potentials (VLP) from high-resolution electrocardiograms (ECGs) is presented. ECG signals from the X lead are first filtered using a bandpass filter, and then a time-sequence adaptive filter, to improve its signal-to-noise ratio. Eight features are extracted using wavelet transform, from the VLP time-frequency distribution of the filtered ECG signals, and used as inputs of specially designed artificial neural network for VLP recognition. The artificial neural network was trained and tested using clinical data, respectively. The results show that the presented method can detect beat-to-beat-based VLP with sensitivity of 80% and specificity of 77%, and the detection accuracy is 78%.     

A low-complexity data-adaptive approach for premature ventricular contraction recognition

Premature ventricular contraction (PVC) may lead to life-threatening cardiac conditions. Real-time automated PVC recognition approaches provide clinicians the useful tools for timely diagnosis if dangerous conditions surface in their patients. Based on the morphological differences of the PVC beats in the ventricular depolarization phase (QRS complex) and repolarization phase (mainly T-wave), two beat-to-beat template-matching procedures were implemented to identify them. Both templates were obtained by a probability-based approach and hence were fully data-adaptive. A PVC recognizer was then established by analyzing the correlation coefficients from the two template-matching procedures. Our approach was trained on 22 ECG recordings from the MIT-BIH arrhythmia database (MIT-BIH-AR) and then tested on another 22 nonoverlapping recordings from the same database. The PVC recognition accuracy was 98.2 %, with the sensitivity and positive predictivity of 93.1 and 81.4 %, respectively. To evaluate its robustness against noise, our approach was applied again to the above testing set, but this time, the ECGs were not preprocessed. A comparable performance was still obtained. A good generalization capability was also confirmed by validating our approach on an independent St. Petersburg Institute of Cardiological Technics database. In addition, our performance was comparable with these published complex approaches. In conclusion, we have developed a low-complexity data-adaptive PVC recognition approach with good robustness against noise and generalization capability. Its performance is comparable to other state-of-the-art methods, demonstrating a good potential in real-time application.     

Real-time discrimination of ventricular tachyarrhythmia withFourier-transform neural network

The authors have developed a method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat. Changes in QRS complexes due to rhythm origination and conduction path were observed with the Fourier transform, and three kinds of rhythms were discriminated by a neural network. In this paper, the potential of the authors' method for clinical uses and real-time detection was examined using human surface ECGs and intracardiac electrograms (EGMs). The method achieved high sensitivity and specificity (>0.98) in discrimination of supraventricular rhythms from ventricular ones. The authors also present a hardware implementation of the algorithm on a commercial single-chip CPU     

Influence of I(Ks) heterogeneities on the genesis of the T-wave: a computational evaluation

Despite the commonly accepted notion that action potential duration (APD) is distributed heterogeneously throughout the ventricles and that the associated dispersion of repolarization is mainly responsible for the shape of the T-wave, its concordance and exact morphology are still not completely understood. This paper evaluated the T-waves for different previously measured heterogeneous ion channel distributions. To this end, cardiac activation and repolarization was simulated on a high resolution and anisotropic biventricular model of a volunteer. From the same volunteer, multichannel ECG data were obtained. Resulting transmembrane voltage distributions for the previously measured heterogeneous ion channel expressions were used to calculate the ECG and the simulated T-wave was compared to the measured ECG for quantitative evaluation. Both exclusively transmural (TM) and exclusively apico-basal (AB) setups produced concordant T-waves, whereas interventricular (IV) heterogeneities led to notched T-wave morphologies. The best match with the measured T-wave was achieved for a purely AB setup with shorter apical APD and a mix of AB and TM heterogeneity with M-cells in midmyocardial position and shorter apical APD. Finally, we probed two configurations in which the APD was negatively correlated with the activation time. In one case, this meant that the repolarization directly followed the sequence of activation. Still, the associated T-waves were concordant albeit of low amplitude.     

Temporal and spatial phase analyses of the electrocardiogram stratify intra-atrial and intra-ventricular organization

We hypothesized that electrocardiogram (ECG) spatial phase analysis would define a spectrum of intracardiac organization from atrial fibrillation (AF), nonisthmus-dependent and isthmus-dependent atrial flutter (AFL) to supraventricular tachycardias (SVT), and similarly for ventricular arrhythmias. We analyzed arrhythmia ECGs of 33 patients with isthmus (n = 9) and nonisthmus (n = 5) dependent AFL and SVT: atrial (n = 3), atrioventricular nodal (n = 3), and orthodromic reciprocating (n = 3) tachycardias, as well as AF (n = 5), ventricular tachycardia (monomorphic, VT-MM; n = 7), and fibrillation (VF; n = 3). ECG spatial phase was considered coherent when the correlation coefficient of an atrial (or ventricular) template to its ECG over time maintained a constant relationship in XY, XZ, and YZ planes. Regularity was quantified spectrally from ECG and correlation series. Spatial coherence occurred in 9/9 cases of isthmus--but only 1/5 of cases of nonisthmus-dependent AFL (p < 0.01; chi2). All showed one dominant spectral peak (temporal coherence). In AF, spatial phase was inconsistent in all planes and spectra were broad band. Temporal and spatial coherence occurred in other SVT. VT-MM maintained spatial phase and a single spectral peak, while VF displayed neither. Our conclusions are that temporal and spatial phase analysis from the ECG stratifies intra-atrial and intra-ventricular organization and reveals subtle variability lost on visual inspection.     

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.     

A single-lead ECG enhancement algorithm using a regularized data-driven filter

We presented a novel way of deriving a subspace filter for enhancing a noisy electrocardiogram (ECG) signal contaminated by electromyogram (EMG). The new subspace filter was based on a multiple cycle prediction (MCP) modeling of a single-lead ECG. The adoption of an MCP model resulted in a data matrix more suitable for separating noise and signal subspaces than the linear prediction (LP) model that is implicitly assumed in many existing subspace filters. Alignment of ECG cycles of different length is required for MCP modeling and was handled by a dynamic time warping (DTW) algorithm. A run-time procedure was designed for automatically determining the signal space dimension adaptively. To validate the new filter in a quantitative way, 12 clean realistic ECG segments with different degrees of heart rate variability generated using the ECGSyn program were mixed with different realizations of EMG noise in the MIT-BIH Noise Stress Test Database and locally acquired EMG at a typical 10-dB signal-to-noise ratio. The performance of the proposed method was compared to three existing ECG enhancement algorithms and achieved encouraging results. In addition, various ECG recordings from MIT-Arrythmia database were also mixed with EMG noise and subjected to the same four filters resulting in a qualitative comparison of them.     

The chronaxie for myocardium and motor nerve in the dog withchest-surface electrodes

The chronaxie (i.e., the duration for a stimulating current having twice the rheobasic, or minimum, value) was determined for ventricular myocardium in 12 pentobarbital-anesthetized dogs. Current was applied transthoracically via chest-surface electrodes located at the optimal axillary site for producing inspiration by stimulation of the phrenic nerve (electroventilation). In four dogs the chronaxie for motor-nerve was determined using electrodes at the same location. After using hand-held electrodes to identify the optimal stimulation site for electroventilation, 4.1 cm diameter electrodes were applied bilaterally to the optimal site on the thorax. In 12 dogs, the threshold current for producing ventricular ectopic beats was determined for single rectangular current pulses ranging from 0.1-10 ms in duration. From these data, strength-duration curves were determined and the average chronaxie for ventricular myocardium was found to be 1.82 ms. In four dogs the relationship between inspired volume and maximum stimulus intensity was determined using a 0.8 s burst of stimuli (60/s) with pulse durations ranging from 20-500 microseconds. From these data, strength-duration curves for current were constructed and the average chronaxie for motor-nerve was found to be 0.17 ms. The results of this study show that, because of the differing chronaxies, the current required to produce inspiration with short-duration stimuli is much less than that required to evoke an ectopic heart beat.     

Localization of the site of origin of cardiac activation by means of a heart-model-based electrocardiographic imaging approach

We have developed a new approach to solve the inverse problem of electrocardiography in terms of heart model parameters. The inverse solution of the electrocardiogram (ECG) inverse problem is defined, in the present study, as the parameters of the heart model, which are closely related to the physiological and pathophysiological status of the heart, and is estimated by using an optimization system of heart model parameters, instead of solving the matrix equation relating the body surface ECGs and equivalent cardiac sources. An artificial neural network based preliminary diagnosis system has been developed to limit the searching space of the optimization algorithm and to initialize the model parameters in the computer heart model. The optimal heart model parameters were obtained by minimizing the objective functions, as functions of the observed and model-generated body surface ECGs. We have tested the feasibility of the newly developed technique in localizing the site of origin of cardiac activation using a pace mapping protocol. The present computer simulation results show that, the present approach for localization of the site of origin of ventricular activation achieved an averaged localization error of about 3 mm [for 5-muV Gaussian white noise (GWN)] and 4 mm (for 10-muV GWN), with standard deviation of the localization errors of being about 1.5 mm. The present simulation study suggests that this newly developed approach provides a robust inverse solution, circumventing the difficulties of the ECG inverse problem, and may become an important alternative to other ECG inverse solutions.     

A dynamic action potential model analysis of shock-inducedaftereffects in ventricular muscle by reversible breakdown of cellmembrane

To elucidate the subcellular mechanism underlying the aftereffects of high-intensity dc shocks, a small pore, which mimics reversible breakdown of the cell membrane (electroporation), was incorporated into the phase-2 Luo-Rudy (L-R) model of ventricular action potentials. The pore size was set to occupy 0.15%-4.25% of the total cell membrane during the 10-ms shock. The pore was assumed to decrease after the shock exponentially with a time constant of 100-1,400 ms to simulate resealing process. In normal myocytes, the pore formation results in a delay of repolarization of the shocked action potential, which is followed by prolonged depolarization and oscillation of membrane potential like early afterdepolarization (EAD). Time- and voltage-dependent changes in the delayed rectifier K+ currents (IKr, IKs) in combination with those of L-type Ca2+ current (ICa,(L)) and ion flux through the pore (I(pore)) are responsible for the potential changes. Spontaneous excitation from the oscillation depends on activation of ICa,(L). In myocytes overloaded with Na+ and Ca2+ secondary to 90% inhibition of Na+-K+ pump, the pore formation results in a delay of repolarization of the shocked action potential, which is followed by slower cyclic depolarization in response to spontaneous release of Ca2+ from the sarcoplasmic reticulum (SR). This delayed afterdepolarization-type oscillation is abolished by complete block of Ca2+ release from the SR. These findings suggest that high-intensity electric field application will cause arrhythmogenic responses through a transient rupture of sarcolemma with different subcellular events in ventricular cells under normal and pathological conditions.     

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.     

A novel methodology for assessing the bounded-input bounded-output instability in QT interval dynamics: application to clinical ECG with ventricular tachycardia

The goal of this paper is to present a new methodology for assessing the bounded-input bounded-output (BIBO) stability in QT interval (QTI) dynamics from clinical ECG. The ECG recordings were collected from 15 patients who experienced ventricular tachycardia (VT). Ten-minute-long ECG recordings extracted immediately before the onset of a chosen VT, one per patient, were assembled into a VT group, while the control group comprised 10-min-long ECGs extracted 1 h before VT onset and at least 1 h after any prior arrhythmic event. Each 10-min recording was subdivided into 1-min ECG recordings (minECGs). The QTI dynamics of each minECG was defined as a function of several prior QTIs and RR intervals; the BIBO stability of this function was then assessed in the z -domain. The number of minECGs with unstable QTI dynamics (N (us)) and the frequency of premature activations (PA), f (PA) , were counted for each ECG recording and were compared between the VT and control groups. The results show that the present methodology successfully captured the instability in QTI dynamics leading to VT onset in the studied population. Significantly larger N (us) was found in the VT group compared against the control and a positive correlation between N (us) and f (PA) was identified in both groups.     

Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias

The analysis and characterization of atrial tachyarrhythmias requires, in a previous step, the extraction of the atrial activity (AA) free from ventricular activity and other artefacts. This contribution adopts the blind source separation (BSS) approach to AA estimation from multilead electrocardiograms (ECGs). Previously proposed BSS methods for AA extraction-e.g., independent component analysis (ICA)-exploit only the spatial diversity introduced by the multiple spatially-separated electrodes. However, AA typically shows certain degree of temporal correlation, with a narrowband spectrum featuring a main frequency peak around 3.5-9 Hz. Taking advantage of this observation, we put forward a novel two-step BSS-based technique which exploits both spatial and temporal information contained in the recorded ECG signals. The spatiotemporal BSS algorithm is validated on simulated and real ECGs from a significant number of atrial fibrillation (AF) and atrial flutter (AFL) episodes, and proves consistently superior to a spatial-only ICA method. In simulated ECGs, a new methodology for the synthetic generation of realistic AF episodes is proposed, which includes a judicious comparison between the known AA content and the estimated AA sources. Using this methodology, the ICA technique obtains correlation indexes of 0.751, whereas the proposed approach obtains a correlation of 0.830 and an error in the estimated signal reduced by a factor of 40%. In real ECG recordings, we propose to measure performance by the spectral concentration (SC) around the main frequency peak. The spatiotemporal algorithm outperforms the ICA method, obtaining a SC of 58.8% and 44.7%, respectively.     

Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation

A new method for QRST cancellation is presented for the analysis of atrial fibrillation in the surface electrocardiogram (ECG). The method is based on a spatiotemporal signal model which accounts for dynamic changes in QRS morphology caused, e.g., by variations in the electrical axis of the heart. Using simulated atrial fibrillation signals added to normal ECGs, the results show that the spatiotemporal method performs considerably better than does straightforward average beat subtraction (ABS). In comparison to the ABS method, the average QRST-related error was reduced to 58 percent. The results obtained from ECGs with atrial fibrillation agreed very well with those from simulated fibrillation signals.     

Extraction and analysis of T waves in electrocardiograms during atrial flutter

Analysis of T waves in the ECG is an essential clinical tool for diagnosis, monitoring, and follow-up of patients with heart dysfunction. During atrial flutter, this analysis has been so far limited by the perturbation of flutter waves superimposed over the T wave. This paper presents a method based on missing data interpolation for eliminating flutter waves from the ECG during atrial flutter. To cope with the correlation between atrial and ventricular electrical activations, the CLEAN deconvolution algorithm was applied to reconstruct the spectrum of the atrial component of the ECG from signal segments corresponding to TQ intervals. The locations of these TQ intervals, where the atrial contribution is presumably dominant, were identified iteratively. The algorithm yields the extracted atrial and ventricular contributions to the ECG. Standard T-wave morphology parameters (T-wave amplitude, T peak-T end duration, QT interval) were measured. This technique was validated using synthetic signals, compared to average beat subtraction in a patient with a pacemaker, and tested on pseudo-orthogonal ECGs from patients in atrial flutter. Results demonstrated improvements in accuracy and robustness of T-wave analysis as compared to current clinical practice.     

A robust method for ECG-based estimation of the respiratory frequency during stress testing

A robust method is presented for electrocardiogram (ECG)-based estimation of the respiratory frequency during stress testing. Such ECGs contain highly nonstationary noise and exhibit changes in QRS morphology which, when combined with the dynamic nature of the respiratory frequency, make most existing methods break down. The present method exploits the oscillatory pattern of the rotation angles of the heart's electrical axis as induced by respiration. The series of rotation angles, obtained from least-squares loop alignment, is subject to power spectral analysis and estimation of the respiratory frequency. Robust techniques are introduced to handle the nonstationary properties of exercise ECGs. The method is evaluated by means of both simulated signals, and ECG/airflow signals recorded from 14 volunteers and 20 patients during stress testing. The resulting respiratory frequency estimation error is, for simulated signals, equal to 0.5% +/- 0.2%, mean +/- SD (0.002 +/- 0.001 Hz), whereas the error between respiratory frequencies of the ECG-derived method and the airflow signals is 5.9% +/- 4% (0.022 +/- 0.016Hz). The results suggest that the method is highly suitable for analysis of noisy ECG signals recorded during stress testing.     

Computer Analysis of Electrocardiograms Using a Linear Regression Technique

The paper describes some results of statistical analysis and classification of electrocardiograms (ECG) on a digital computer. The linear regression method is chosen for the analysis mainly because of its conceptual and mathematical simplicity. The analysis is based on the ECG waveform itself rather than the special criteria established by the experience of physicians. Three-lead ECGs for each subject were used. Each ECG was approximated efficiently by 12 orthonormal exponentials. Nine direction cosines were used to describe the spatial orientation of the vectorcardiographic loop. These 45 parameters (3×12+9=45) were regarded as coordinates in a 45-dimensional vector space, and the statistical analysis was aimed at relating certain portions of the vector space with the pathological categories. This procedure is intended primarily as a research tool. With ECGs of 53 subjects, the method has been quite promising. However, demonstration of clinical significance will require additional experimental investigation.     

T-wave alternans found in preventricular tachyarrhythmias in CCU patients using a wavelet transform-based methodology

Ventricular tachyarrhythmias are potentially lethal cardiac pathologies and the commonest cause of sudden cardiac death. Efforts to predict the onset of such events are based on feature extraction from the surface ECG. T-wave alternans (TWAs) are considered a marker of abnormal ventricular function that may be associated with ventricular tachycardia (VT) and ventricular fibrillation. A novel TWA detection algorithm utilizing the continuous wavelet transform is described in this paper. Simulated ECGs containing artificial TWA were used to test the algorithm that achieved a sensitivity of 91.40% and a specificity of 94.00%. The algorithm was subsequently used to analyze the ECGs of eight patients prior to the onset of VT. Of these, the algorithm indicated that five patients exhibited TWA prior to the onset of the tachyarrhythmic events, while the remaining three patients did not exhibit identifiable TWA. Healthy individuals were also studied in which one short TWA episode was detected by the algorithm. However, closer visual inspection of the data revealed this to be a likely false positive result.      

Late potential recognition by artificial neural networks

Ventricular late potentials (LPs) are high-frequency low-amplitude signals obtained from signal-averaged electrocardiograms (ECGs) [SAECGs]. LPs are useful in identifying patients prone to ventricular tachycardia (VT), spontaneous or inducible during electrophysiology testing. A combination of self-organizing and supervised artificial neural network (ANN) models was developed to identify patients with a positive electrophysiology (PEP) test for inducible ventricular tachycardia from patients with a negative electrophysiology (NEP) test using LPs. We have added morphology information of vector magnitude waveform to an original set of three time-domain features of LPs, which are total QRS duration (TQRSD), high-frequency low-amplitude signal duration (HFLAD), and root-mean-square voltage (RMSV). Pattern recognition results from an ANN model with this combination feature set are superior to the results from Bayesian classification model based on conventional three time-domain features of SAECG. In order to increase the robustness of the recognition, a filtered QRS offset point is randomly shifted ±8 ms to form a fuzzy training set, which was to simulate the possible error in detecting QRS offset point of filtered SAECG. We also found that nonlinear transformation through the hidden layer of developed ANN model could increase Euclidean distance between PEP and NEP patterns