Classification of electrocardiographic p-wave morphology

Carlson, Jonas; Johansson, Rolf; Olsson, Sten‐Erik

doi:10.1109/cdc.2000.912124

Cited by 11 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over recent years filtered techniques of P-wave signal averaging have been most extensively studied. Their combination with unfiltered P-wave analysis seems, however, perspective in identifying concealed conduction defects which are of importance for AF inducibility and sustenance [58,67]. Analysis of unfiltered P-waves identified two features to be characteristic for lone AF: (1) double-peaked P-wave morphology in spatial magnitude and (2) biphasic P-waves in the Z-lead [58,67].…”

Section: P-wave Signal Averaged Ecgmentioning

confidence: 94%

Analysis of the Surface Electrocardiogram for Monitoring and Predicting Antiarrhythmic Drug Effects in Atrial Fibrillation

Husser

Stridh

Sörnmo

et al. 2004

Cardiovasc Drugs Ther

View full text Add to dashboard Cite

Specific antiarrhythmic therapy with class I and III drugs for atrial fibrillation (AF) conversion and prevention of its recurrence is frequently utilized in clinical practice. Besides being only moderate effective, the utilization of antiarrhythmic drugs may be associated with serious side effects. In the clinical setting it is difficult to directly evaluate the effects of antiarrhythmic drugs on the individual patient's atrial electrophysiology, thereby predicting their efficacy in restoring and maintaining sinus rhythm. Analysis of the surface electrocardiogram in terms of P-wave signal averaged ECG during sinus rhythm and spectral characterization of fibrillatory waves during AF for evaluation of atrial antiarrhythmic drug effects is a new field of investigation. Both techniques provide reproducible parameters for characterizing atrial electrical abnormalities and seem to contain prognostic information regarding antiarrhythmic drug efficacy. Further research is needed which elucidates the most challenging clinical questions in AF management whom to place on antiarrhythmic drug treatment and what antiarrhythmic drug to prescribe. Analysis of the surface ECG might have the potential to answer these questions.

show abstract

Section: P-wave Signal Averaged Ecgmentioning

confidence: 94%

Analysis of the Surface Electrocardiogram for Monitoring and Predicting Antiarrhythmic Drug Effects in Atrial Fibrillation

Husser

Stridh

Sörnmo

et al. 2004

Cardiovasc Drugs Ther

View full text Add to dashboard Cite

show abstract

“…Changes in P-wave polarity as well as subtle differences in P-wave morphology are believed to reflect abnormal activation patterns in the atria [3] [4]. Irregularities in the orthogonal P-wave morphology have been detected in AF patients and associated with local inter-atrial conduction delay [2].…”

Section: Introductionmentioning

confidence: 99%

“…Irregularities in the orthogonal P-wave morphology have been detected in AF patients and associated with local inter-atrial conduction delay [2]. Different shapes of P-wave have been shown to represent the presence or absence of an underlying pathophysiological condition in patients prone to AF attacks [4]. In the presence of a partial inter-atrial conduction block, P-wave shape changes have been demonstrated to depend on the direction of the activation wavefront [5].…”

Section: Introductionmentioning

confidence: 99%

Morphological analysis of P-wave in patients prone to atrial fibrillation

Censi¹,

Calcagnini²,

Mattei³

et al. 2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Aim of this study was to present a P-wave model, based on a linear combination of Gaussian functions, to quantify morphological aspects of P-wave in patients prone to atrial fibrillation. Five minutes ECG recordings were performed in 25 patients with permanent dual chamber pacemakers set at 40/min in order to have spontaneous beats. ECG signals were acquired using a 32-lead mapping system for high-resolution biopotential measurement (ActiveTwo, Biosemi, The Netherlands, sample frequency 2 kHz, 24 bit resolution). Four healthy subjects were also recorded as a control group. Up to 8 Gaussian models have been computed for each averaged P-wave extracted from every lead. The Pwave morphology is then evaluated by the following parameters: best model orders @ Degrees of Freedom Adjusted R-Square (AdjRsq ) =97.5%; minimum ( min) and maximum ( max) standard deviation of the gaussians included in the model, number of relative maxima and minima (max+min), and zeroes of the fit. Significant differences in the best model order were obtained between the control group and patients group. Accordingly, the number of relative maxima and minima was higher in the patient group. These parameters might all be markers of the fractionated electrical activity that characterizes paroxysmal AF patients in sinus rhythm.

show abstract

“…Low variance is a strong indicator for sudden cardiac failure. The second common digital signal processing medical application is classifying heartbeats as either normal or associated with a cardiac malady (Kundu et al, 2000;Vila et al, 2000;Carlson et al, 2001). This latter process is an application of supervised classification.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

View full text Add to dashboard Cite

This paper reviews the emerging research into exploitation of heartbeat data as a biometric for human identification. A variety of methods have been proposed for acquiring heartbeat signatures and a range of processing methods has been examined. We approach the biometric identification and verification problem by characterising the three major factors affecting performance: individual variants, environmental variants, and sensor variants. The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors. Within each component, we have identified the relevant research. Where possible, we have tied these research papers to practical examples using high resolution ECG data. The research indicates that the heartbeat contains rich information about the individual, their level of anxiety, and the cardiac state.Keywords: biometrics; heartbeat; ECG; sensing system; cardiovascular function; human identification. areas of interest include novel biometrics, signal and image processing, quantification and modelling of data quality, and cognitive modeling. He was a Principal Investigator for DARPA's Human Identification at a Distance Program (HumanID). He serves on planning committees for IEEE and SPIE, and has served on several advisory panels for the Department of Defence and the Department of Energy. He has authored over a hundred journal and conference papers and holds a PhD in Mathematical Statistics from Yale University.

show abstract

Classification of electrocardiographic p-wave morphology

Cited by 11 publications

References 13 publications

Analysis of the Surface Electrocardiogram for Monitoring and Predicting Antiarrhythmic Drug Effects in Atrial Fibrillation

Analysis of the Surface Electrocardiogram for Monitoring and Predicting Antiarrhythmic Drug Effects in Atrial Fibrillation

Morphological analysis of P-wave in patients prone to atrial fibrillation

Heartbeat biometrics: a sensing system perspective

Contact Info

Product

Resources

About