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Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from the 12-lead ECG. The 12-lead ECGs of 30 patients in atrial fibrillation were analyzed. Atrial activity was extracted by three algorithms, Spatiotemporal QRST cancellation (STC), principal component analysis (PCA), and independent component analysis (ICA). The amplitude and frequency characteristics of the extracted atrial signals were compared between algorithms and against reference data. Mean (standard deviation) amplitude of QRST segments of VI was 0.99 (0.54) mV, compared to 0.18 (0.11) mV (STC), 0.19 (0.13) mV (PCA), and 0.29 (0.22) mV (ICA). Hence, for all algorithms there were significant reductions in the amplitude of the ventricular activity compared with that in VI. Reference atrial signal amplitude in VI was 0.18 (0.11) mV, compared to 0.17 (0.10) mV (STC), 0.12 (0.09) mV (PCA), and 0.18 (0.13) mV (ICA) in the extracted atrial signals. PCA tended to attenuate the atrial signal in these segments. There were no significant differences for any of the algorithms when comparing the amplitude of the reference atrial signal with that of the extracted atrial signals in segments in which ventricular activity had been removed. There were no significant differences between algorithms in the frequency characteristics of the extracted atrial signals. There were discrepancies in amplitude and frequency characteristics of the atrial signal in only a few cases resulting from notable residual ventricular activity for PCA and ICA algorithms. In conclusion, the extracted atrial signals from these algorithms exhibit very similar amplitude and frequency characteristics. Users of these algorithms should be observant of residual ventricular activities which can affect the analysis of the fibrillatory waveform in clinical practice. Index Terms--Atrial fibrillation, atrial signal, comparative study, independent component analysis, principal component analysis, spatiotemporal QRST cancellation.

Atrial tachy-arrhytmias, such as atrial fibrillation (AF), are characterised by irregular electrical activity in the atria, generally associated with erratic excitation underlain by re-entrant scroll waves, fibrillatory conduction of multiple wavelets or rapid focal activity. Epidemiological studies have shown an increase in AF prevalence in the developed world associated with an ageing society, highlighting the need for effective treatment options. Catheter ablation therapy, commonly used in the treatment of AF, requires spatial information on atrial electrical excitation. The standard 12-lead electrocardiogram (ECG) provides a method for non-invasive identification of the presence of arrhythmia, due to irregularity in the ECG signal associated with atrial activation compared to sinus rhythm, but has limitations in providing specific spatial information. There is therefore a pressing need to develop novel methods to identify and locate the origin of arrhythmic excitation. Invasive methods provide direct information on atrial activity, but may induce clinical complications. Non-invasive methods avoid such complications, but their development presents a greater challenge due to the non-direct nature of monitoring. Algorithms based on the ECG signals in multiple leads (e.g. a 64-lead vest) may provide a viable approach. In this study, we used a biophysically detailed model of the human atria and torso to investigate the correlation between the morphology of the ECG signals from a 64-lead vest and the location of the origin of rapid atrial excitation arising from rapid focal activity and/or re-entrant scroll waves. A focus-location algorithm was then constructed from this correlation. The algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation with a spatial resolution of 40 mm, respectively. The general approach allows its application to any multi-lead ECG system. This represents a significant extension to our previously developed algorithms to predict the AF origins in association with focal activities. [ABSTRACT FROM AUTHOR]

A novel method of quantifying the effectiveness of the suppression of ventricular activity from electrocardiograms (ECGs) in atrial fibrillation is proposed. The temporal distribution of the energy of wavelet coefficients is quantified by wavelet entropy at each ventricular beat. More effective ventricular activity suppression yields increased entropies at scales dominated by the ventricular and atrial components of the ECG. Two studies are undertaken to demonstrate the efficacy of the method: first, using synthesised ECGs with controlled levels of residual ventricular activity, and second, using patient recordings with ventricular activity suppressed by an average beat template subtraction algorithm. In both cases wavelet entropy is shown to be a good measure of the effectiveness of ventricular beat suppression. [ABSTRACT FROM AUTHOR]

Rapid atrial arrhythmias such as atrial fibrillation (AF) predispose to ventricular arrhythmias, sudden cardiac death and stroke. Identifying the origin of atrial ectopic activity from the electrocardiogram (ECG) can help to diagnose the early onset of AF in a cost-effective manner. The complex and rapid atrial electrical activity during AF makes it difficult to obtain detailed information on atrial activation using the standard 12-lead ECG alone. Compared to conventional 12-lead ECG, more detailed ECG lead configurations may provide further information about spatio-temporal dynamics of the body surface potential (BSP) during atrial excitation. We apply a recently developed 3D human atrial model to simulate electrical activity during normal sinus rhythm and ectopic pacing. The atrial model is placed into a newly developed torso model which considers the presence of the lungs, liver and spinal cord. A boundary element method is used to compute the BSP resulting from atrial excitation. Elements of the torso mesh corresponding to the locations of the placement of the electrodes in the standard 12-lead and a more detailed 64-lead ECG configuration were selected. The ectopic focal activity was simulated at various origins across all the different regions of the atria. Simulated BSP maps during normal atrial excitation (i.e. sinoatrial node excitation) were compared to those observed experimentally (obtained from the 64-lead ECG system), showing a strong agreement between the evolution in time of the simulated and experimental data in the P-wave morphology of the ECG and dipole evolution. An algorithm to obtain the location of the stimulus from a 64-lead ECG system was developed. The algorithm presented had a success rate of 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations, and involved a general approach relevant to any multi-lead ECG system. This represents a significant improvement over previously developed algorithms. [ABSTRACT FROM AUTHOR]

An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs. The respiratory-induced variability of ECG features, P waves, QRS complexes, and T waves are described by the PCA. We assessed which ECG features and which principal components yielded the best surrogate for the respiratory signal. Twenty subjects performed controlled breathing for 180 s at 4, 6, 8, 10, 12, and 14 breaths per minute and normal breathing. ECG and breathing signals were recorded. Respiration was derived from the ECG by three algorithms: the PCA-based algorithm and two established algorithms, based on RR intervals and QRS amplitudes. ECG-derived respiration was compared to the recorded breathing signal by magnitude squared coherence and cross-correlation. The top ranking algorithm for both coherence and correlation was the PCA algorithm applied to QRS complexes. Coherence and correlation were significantly larger for this algorithm than the RR algorithm (p < 0.05 and p < 0.0001, respectively) but were not significantly different from the amplitude algorithm. PCA provides a novel algorithm for analysis of both respiratory and nonrespiratory related beat-to-beat changes in different ECG features. [ABSTRACT FROM AUTHOR]

SMITH, F.E., et al.: Comparison of Automatic Repolarization Measurement Techniques in the Normal Magnetocardiogram. Multichannel MCG noninvasively measures cardiac magnetic field strength from many sites at the body surface, potentially providing useful regional information about ventricular repolarization. Previous work on ECGs has shown that automatic techniques for repolarization measurement are better than manual measurement at discriminating patients with cardiac conditions from normal subjects. Although automatic repolarization measurement techniques have been quantified for ECGs, no comparative data exists for the MCG. In this study four different automatic repolarization (QT) interval techniques for detecting T wave end in the MCG were compared. The influence of MCG filtering on the automatic algorithms was also quantified. MCGs were obtained at 49 sites over the heart from 23 normal subjects. Automatic measurements of the repolarization (QT) interval were made following the addition of different high pass (0.25, 0.5, 1 Hz) and low pass (100, 60, 40, 30 Hz) filters. There were consistent differences between automatic techniques in the unfiltered data amounting to greatest mean difference of 52.3 ms. Low pass filtering significantly increased the automatic repolarization (QT) interval relative to unfiltered measurement by 6.5 (3.2) ms (mean SD) for 100 Hz, 6.0 (3.0) ms for 60 Hz, 8.1 (3.2) ms for 40 Hz, and 8.8 (3.1) ms for 30 Hz across all techniques. High pass filtering significantly decreased the value by −2.6 (6.0) ms for 0.25 Hz, −5.5 (5.3) ms for 0.5 Hz, and −17.1 (7.8) ms for 1 Hz. Automatic measurements of repolarization (QT) in the MCG differ between techniques and are influenced by filtering. These effects should be considered when comparing results. (PACE 2003; 26:2096–2102) [ABSTRACT FROM AUTHOR]

T wave shape is in creasingly used to provide insights into cardiac repolarization and, although shape is known to change as heart rate changes, there are no published quantitative clinical data. The aim of this study was to quantify these changes. Heart rate (HR), T wave amplitude, and two measures of T wave symmetry (SRarea-ratio of areas about the peak, SRtime-ratio of centrality of peak), were quantified over a period of 360 seconds following exercise in 20 healthy human subjects. As HR decreased, in all subjects the T wave became more asymmetrical (SRarea 20/20, P < 0.0001). Resting reference HR and symmetry rations were (Mean ± SD), HR 63 ± 10 bent/min, SRarea 1.68 ± 0.25, and SRtime 2.13 ± 0.39. Fifty seconds postexercise, HR was significantly less at SRarea 1.04 ± 0.15, SRtime, 1.24 ± 0.36 (P < 0.0001). Significant differences in HR and both symmetry ratios remained at 300 seconds postexercise. Amplitude increases had returned to their reference values at 300 seconds. T wave shape was significantly more symmetrical at higher HRs. These findings confirm qualitative reports of shape changes. [ABSTRACT FROM AUTHOR]

To investigate the effect of different lead exclusion criteria for the manual measurement of QT dispersion (QTd). Simultaneous 12-lead ECGs from three groups of 25 subjects were studied; healthy normal subjects, subjects with a myocardial infarction, and subjects with arrhythmias. Leads were excluded with (1) small absolute T wave amplitudes, (2) small relative T wave amplitudes, and (3) small and/or large relative QT measurements. QTd was calculated as the QT range and assessed for its ability to differentiate between the normal and pathological groups. With exclusion of no leads or low absolute amplitude T waves (< 50 μV) significant differences were observed only between normal and myocardial infarct groups (P < 0.05). Significant differences between normal and both pathological groups were observed when excluding the lead with the smallest amplitude T wave or shortest QT (P < 0.05), or when two leads of either type were excluded (P < 0.005). There was good agreement between leads excluded by amplitude or QT (P < 0.01). Lead exclusion for QTd is important. Exclusion of the two smallest amplitude or two shortest QT leads from each subject produced the greatest differences between the normal and pathological groups. [ABSTRACT FROM AUTHOR]

QT interval dispersion may provide little information about repolarization dispersion. Some clinical measurements demonstrate an association bet ween high QT interval dispersion and high morbidity and mortality, but what is being measured is not clear. This study was designed to help resolve this dilemma. We compared the association between different clinical measures of QT interval dispersion and the £CG lead amplitudes derived from a heart vector model of repolarization with no repolarization dispersion whatsoever. We compared our clinical QT interval dispersion data obtained from 25 subjects without cardiac disease with similar data from published studies, and correlated these QT dispersion results with the distribution of lead amplitudes den vs from the projection of the heart vector onto the body surface during repolarization. Published results were available for mean relative QT intervals and mean differences from the maximum QT interval. The leads were derived from Uijen and Dower lead vector data. Using the Uijen lead vector data, the correlation between measurements of dispersion and derived lead amplitudes ranged from 0.78 to 0.99 for limb leads, and using the Dower values ranged from 0.81 to 0.94 for the precordial leads. These results show a clear association between the measured QT interval dispersion and the variation in ECG lead amplitudes derived from a simple heart vector model of repolarization with no regional information. Therefore, measured QT dispersion is related mostly to a projection effect and is not a true measure of repolarization dispersion. Our existing interpretation of QT dispersion must be reexamined, and other measurements that provide true repolarization dispersion data investigated. [ABSTRACT FROM AUTHOR]