Martin Stridh

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.

Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis

A new method for characterization of atrial arrhythmias is presented which is based on the time-frequency distribution of an atrial electrocardiographic signal. A set of parameters are derived which describe fundamental frequency, amplitude, shape, and signal-to-noise ratio. The method uses frequency-shifting of an adaptively updated spectral profile, representing the shape of the atrial waveforms, in order to match each new spectrum of the distribution. The method tracks how well the spectral profile fits each spectrum as well as if a valid atrial signal is present. The results are based on the analysis of a learning database with signals from 40 subjects, of which 24 have atrial arrhythmias, and an evaluation database with 211 patients diagnosed with atrial fibrillation. It is shown that the method robustly estimates fibrillation frequency and amplitude and produces spectral profiles with narrower peaks and more discernible harmonics when compared to the conventional power spectrum. The results suggest that a rather strong correlation exist between atrial fibrillation frequency and f wave shape. The developed set of parameters may be used as a basis for automated classification of different atrial rhythms.

Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties

Time-frequency analysis is considered for characterizing atrial fibrillation in the surface electrocardiogram (ECG). Variations in fundamental frequency of the fibrillatory waves are tracked by using different time-frequency distributions which are appropriate to short- and long-term variations. The cross Wigner-Ville distribution is found to be particularly useful for short-term analysis due to its ability to handle poor signal-to-noise ratios. In patients with chronic atrial fibrillation, substantial short-term variations exist in fibrillation frequency and variations up to 2.5 Hz can be observed within a few seconds. Although time-frequency analysis is performed independently in each lead, short-term variations in fibrillation frequency often exhibit a similar pattern in the leads V/sub 1/, V/sub 2/ and V/sub 3/. Using different techniques for short- and long-term analysis, it is possible to reliably detect subtle long-term changes in fibrillation frequency, e.g., related to an intervention, which otherwise would have been obscured by spontaneous variations in fibrillation frequency.

Comparison of atrial signal extraction algorithms in 12-lead ECGs with atrial fibrillation

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 V1 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 V1. Reference atrial signal amplitude in V1 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.

Echocardiographic and Electrocardiographic Predictors for Atrial Fibrillation Recurrence Following Cardioversion

Introduction: Identification of suitable candidates for cardioversion currently is not based on individual electrical and mechanical atrial remodeling. Therefore, this study analyzed the meaning of atrial fibrillatory rate obtained from the surface ECG (as a measure of electrical remodeling) and left atrial size (as measure of mechanical remodeling) for prediction of early atrial fibrillation (AF) recurrence following cardioversion.

Validation and clinical application of time-frequency analysis of atrial fibrillation electrocardiograms

Introduction: Fibrillatory rates can reliably be obtained from surface ECGs during atrial fibrillation (AF) and correspond with right atrial (RA) and coronary sinus (CS) rates, while both the relation with pulmonary venous (PV) rates and determinants of fibrillatory waveform are unknown. Class III antiarrhythmic drugs prolong atrial refractoriness and decrease its dispersion, effects that may be reflected in ECG parameters. Consequently, this study sought (1) to investigate the relation between ECG fibrillatory rate and waveform characteristics with intraatrial/PV fibrillatory activity and (2) to noninvasively monitor class III antiarrhythmic drug effects in patients with AF.

Frequency Measures Obtained from the Surface Electrocardiogram in Atrial Fibrillation Research and Clinical Decision‐Making

Introduction: Frequency analysis of fibrillation (FAF) and time‐frequency analysis (TFA) were developed recently in order to quantify atrial electrical remodeling in atrial fibrillation (AF) from the surface ECG. This article describes the experience with these two different frequency analysis techniques in consecutive AF patients and discusses possible applications in AF research and clinical decision‐making.

Frequency Tracking of Atrial Fibrillation Using Hidden Markov Models

A hidden Markov model (HMM) is employed to improve noise robustness when tracking the dominant frequency of atrial fibrillation (AF) in the electrocardiogram (ECG). Following QRST cancellation, a sequence of observed frequency states is obtained from the residual ECG, using the short-time Fourier transform. Based on the observed state sequence, the Viterbi algorithm retrieves the optimal state sequence by exploiting the state transition matrix, incorporating knowledge on AF characteristics, and the observation matrix, incorporating knowledge of the frequency estimation method and signal-to-noise ratio (SNR). The tracking method is evaluated with simulated AF signals to which noise, obtained from ECG recordings, has been added at different SNRs. The results show that the use of HMM improves performance considerably by reducing the rms error associated with frequency tracking: at 4-dB SNR, the rms error drops from 0.2 to 0.04 Hz.

Detection and feature extraction of atrial tachyarrhythmias

Analysis of atrial tachyarrhythmias requires that an atrial signal has first been extracted from the electrocardiogram (ECG), resulting in the so-called residual ECG. This article introduces a novel method for atrial rhythm analysis that condenses different signal parameters into three rhythm features, reflecting signal structure, frequency regularity, and waveform type. A three-stage method for atrial signal analysis is introduced and comprises atrial signal extraction, atrial signal characterization, and atrial rhythm feature analysis. The present study is example based and illustrates the potential of the method. A quantitative evaluation of the performance of the method will be done in a future study

Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management.

The analysis of atrial fibrillation in non-invasive ECG recordings has received considerable attention in recent years, spurring the development of signal processing techniques for more advanced characterization of the atrial waveforms than previously available. The present paper gives an overview of different approaches to the extraction of atrial activity in the ECG and to the characterization of the resulting atrial signal with respect to its spectral properties. So far, the repetition rate of the atrial waves is the most studied parameter and its significance in clinical management is briefly considered, including the identification of pathomechanisms and prediction of therapy efficacy.