Computer Discrimination of Atrial Fibrillation and Regular Atrial Rhythms from Intra-Atrial Electrog (original) (raw)
Related papers
Pacing and Clinical Electrophysiology, 1988
SLOCUM, J., ET AL.: Computer discrimination of atrial fibrillation and regular atrial rhythms from intra-atrial electrograms. Heliahle detection of atrial fihriilation from intra-atrial data is an important requirement for automatic impJantable anti-tachycardia devices. Simultaneous filtered and unfiltered intra-atrial electrograms were recorded from patients in regular rhythms (12 sinus rhythms and six regular atrial tachycardias) and atrial fibriJiation fnine rhythms). Each rhythm was broken down into consecutive 4-second data segments for analysis by atrial rate caicuiation, power spectrum analysis and amplitude probability density function generation. Significant diferences were found between regular rhythms and atriaJ fibrillation for atrial rate, for the percentage of the total power in the 4-9 hertz band and for amplitude probability density close to the isoelectric region. There was no overlap for any of these three parameters. For each method of analysis, algorithms were generated to discriminate individual data segments from regular rhythms and atriai fibrillation with high sensitivity and specificity. Comparable results were found when sinus rhythm was excluded from the analysis. Characteristics of intra-atrial recordings during atrial fibrillation were remarkably similar to previously published reports of intra-ventricular recordings during ventricular ^briilation. Each of the three n\ethods of analysis may provide an algorithm for accurate detection of atrial fibrillation by anti-tachycardia devices.
Data processing techniques for the characterization of atrial fibrillation
Atrial fibrillation is the most common sustained cardiac rhythm disturbance, increasing in prevalence with age. During the past 20 years, there has been a 66% increase in hospital admissions related to atrial fibrillation. Neither the natural history of atrial fibrillation nor its response to therapy is sufficiently predictable from clinical and echocardiographic parameters. Treatment of atrial fibrillation is mainly based on trial and error. Thus, it seems appropriate to develop tests that quantify the state of the disease and guide its management. Standard 12-lead electrocardiogram recordings are commonly required for clinical evaluation. Therefore, possible prognostic information contained within the electrocardiogram provide a great interest. The goal of this thesis is to help clinicians treat atrial fibrillation by developing information from the standard 12-lead electrocardiogram on atrial fibrillation substrates, dynamics, and to predict the success of different treatments.
Provisional chapter Applications of Signal Analysis to Atrial Fibrillation
2013
Currently, atrial fibrillation (AF) guidelines are intended to assist physicians in clinical decision making by describing a range of generally acceptable approaches for the diagnosis and management of AF. However, these guidelines provide no recommendations that takes into account other aspects of the arrhythmia related with its computational analysis. For example, the proper application of spectral analysis, how to quantify different AF patterns in terms of organization, or how to deal with ventricular contamination before AF analysis are some aspects that could provide an improved scenario to the physician in the search of useful clinical information [1].
IEEE Transactions on Biomedical Engineering, 2000
This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a featureextraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic system was applied on 100 intracardiac AF signal strips and on a selection of 11 representative features, demonstrating: a) the possibility to properly identify the most significant features for the discrimination of AF types; b) higher accuracy (97.7% using the seven most informative features) than the traditional maximum likelihood classifier; and c) effectiveness in AF classification also with few training samples (accuracy = 88.3% with only five training signals). Finally, the system identifies a combination of indices characterizing changes of morphology of atrial activation waves and perturbation of the isoelectric line as the most effective in separating the AF types.
Journal of Electrocardiology, 2000
Computerized time-domain analysis of the QRST-subtracted i2lead electrocardiogram (ECG) has been used successfully to determine several atrial activity patterns. These time-domain methods are particularly useful for low-frequency signals such as those originating at the sinus node. However, high frequency atrial fibrillation (AFIB) and atrial flutter (AFL) waves can be better estimated by using spectral methods. In this study, we investigated the use of spectral entropy (SE) and spectral peak detection to distinguish fibrillatory from flutter activity in the QRST-subtracted ECG. In a set of 4,172 cardiologist-overread ECGs, a computerized ECG analysis program (12SL MAC-Rhythm, GE-Marquette Medical Systems, Milwaukee, WI) detected 270 AFIB rhythms and 100 AFL rhythms. Compared to the cardiologist's reading, the AFIB versus AFL miss-classification error was 5.6%. The Fourier Transform was used to estimate the power spectral density of the QRST-subtracted ECG data. Individual lead spectra were then averaged and SE was computed for each of the ECGs originally called AFIB or AFL by the computer program. Additional criteria that included SE, spectral peak frequencies, and timedomain measures of atrial activity were then applied to discriminate between the 2 rhythms. Employing these criteria resulted in a decrease of missclassification error to 2.5%.
Computers in Cardiology, 2002
One of the greatest challenges in analysis of the atrial rhythm from the ECG is to distinguish the atrial component from the large ventricular components. Our aim was to compare three techniques of atrial rhythm extraction from three groups working on this problem. 12-lead ECG data from 7 patients in atrial fibrillation were analysed. For extraction of the atrial rhythm, spatiotemporal QRST cancellation was performed by the Lund group, blind source separation by the Valencia group, and principal component analysis by the Newcastle group. Peak atrial frequency was determined by Fourier transform of the signal with the largest atrial activity. All algorithms were successful in distinguishing the atrial rhythm from the low frequency ventricular rhythm. The mean (range) atrial frequency was ) Hz (Valencia) and 6.5 (5.9 -8.2) Hz (Newcastle). There were no significant differences between the atrial frequencies estimated by each of the techniques.
Atrial Electrograms and the Characterization of Atrial Fibrillation
Journal of Electrocardiology, 1991
An improved method of calculating magnitude-squared coherence spectra on pairs of short-duration electrogram recordings is discussed. This method is based on adaptive signal processing techniques and yields spectra with higher resolution than those obtained using a straightforward direct method. The high-resolution spectra will make it possible to examine the timevarying relationship between activity at two sites during atrial fibrillation and may be useful for quick rhythm characterization by implanted devices or for constructingcoherence maps in researchstudies. Example high-resolution spectra for sinus rhythm, atrial flutter, and atrial fibrillation are presented.
Europace, 2006
Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy is sufficiently predictable by clinical and echocardiographic parameters. The purpose of this article is to describe technical aspects of novel electrocardiogram (ECG) analysis techniques and to present research and clinical applications of these methods for characterization of both the fibrillatory process and the ventricular response during AF. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface ECG using digital signal processing (extraction of atrial signals and spectral analysis). This measurement shows large inter-individual variability and correlates well with intra-atrial cycle length, a parameter which appears to have primary importance in AF maintenance and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously and responds better to antiarrhythmic drugs or cardioversion, whereas high-rate AF is more often persistent and refractory to therapy. Ventricular responses during AF can be characterized by a variety of methods, which include analysis of heart rate variability, RR-interval histograms, Lorenz plots, and non-linear dynamics. These methods have all shown a certain degree of usefulness, either in scientific explorations of atrioventricular (AV) nodal function or in selected clinical questions such as predicting response to drugs, cardioversion, or AV nodal modification. The role of the autonomic nervous system for AF sustenance and termination, as well as for ventricular rate responses, can be explored by different ECG analysis methods. In conclusion, non-invasive characterization of atrial fibrillatory activity and ventricular response can be performed from the surface ECG in AF patients. Different signal processing techniques have been suggested for identification of underlying AF pathomechanisms and prediction of therapy efficacy.
Development of a Toolbox for ECG Based Interpretation of Atrial Fibrillation
Background: Atrial fibrillation (AF) develops as a consequence of an underlying heart disease such as fibrosis, inflammation, hyperthyroidism, elevated intra-atrial pressures, and/or atrial dilatation. The arrhythmia is initiated by, or depends on, ectopic focal activity. Autonomic dysfunction may also play a role. However, in most patients, the actual cause of AF is difficult to establish, which hampers the selection of the optimal mode of treatment. This study aims to develop tools for assisting the physician's decision-making process. Methods: Signal analytical methods have been developed for optimizing the assessment of the complexity of AF in all of the standard 12-lead signals. The development involved an evaluation of methods for reducing the signal components stemming from the electric activity of the ventricles (QRST suppression). The methods were tested on simulated recordings, on clinical recordings on patients in AF, and on patients exhibiting atrial flutter (AFL) and atrial tachycardia. The results have been published previously. Subsequently, the implementation of the algorithms in a commercially available electrocardiogram (ECG) recorder, an implementation referred to as its AF-Toolbox, has been carried out. The performance of this implementation was tested against those observed during the development stage. In addition, an improved visualization of the specific ECG components was implemented. This was enabled by providing a separate view on ventricular and atrial activity, which resulted from the steps implied in the QRST suppression. Furthermore, a search was initiated for identifying meaningful features in the cleaned up atrial signals. Results: When testing the implementation of the previously developed methods in the Toolbox on simulated and clinical data, the suppression of ventricular activity in the ECG produced residuals down to the level of physiologic background noise, in agreement with those reported on previously. The QRST suppression resulted in a better visualization of the atrial signals in AF, atrial AFL, sinus rhythm in the presence of atrioventricular blocks, or ectopic beats. Classifiers for AF and AFL that have been defined so far include the distinct spectral components (multiple basic frequencies), exhibiting distinct dominance in specific leads. The annotations of ventricular and atrial activities, ventricular and atrial trigger, as well as ratio between atrial and ventricular rates were greatly facilitated. The time diagram of ventricular and atrial triggers provides an additional view on rhythm disturbances. Conclusions: The AF-Toolbox that is currently developed for clinical applications has the potential of reliably detecting and classifying AF, as well as to correctly describe atrioventricular conduction, propagation blocks and/or ectopic beats. Based on the results obtained, a first industrial prototype has been built, which will be used to assess its performance in a routine clinical environment. The availability of this tool will facilitate the search for meaningful signal features for identifying the source of AF in individual patients.