What does this research mean for the field?

Atrial period (AP), its coefficient of variation (CV), number of points in the baseline (NO), and correlation waveform analysis (CWA) are the most effective parameters for discriminating between type I and type III atrial fibrillation from intra-atrial electrograms, though type II remains difficult to identify. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To compare signal analysis methods and identify the best electrogram parameters for classifying distinct types of atrial fibrillation according to Wells' criteria.

November 27, 2002

A comparison of methods for the classification of atrial fibrillation from intra-atrial electrograms

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Why the study?

Which signal processing parameters best discriminate and classify atrial fibrillation types from intra-atrial electrograms?

Key Points

To compare signal analysis methods and identify the best electrogram parameters for classifying distinct types of atrial fibrillation according to Wells' criteria.
Analyzed intra-atrial electrograms recorded from individuals with chronic atrial fibrillation and subjects with electrically induced atrial fibrillation.
Evaluated atrial period, coefficient of variation, amplitude probability density function metrics (baseline points and Shannon entropy), correlation waveform analysis, and power spectrum density indexes.
Assessed parameter classification performance using ANOVA, Student's t-test, and cluster analysis.
Atrial period, its coefficient of variation, baseline points, and correlation waveform analysis yielded the best discriminative capacity among the evaluated methods.
Cluster analysis incorporating the top-performing parameters satisfactorily separated Wells type I and type III atrial fibrillation electrograms.
Type II atrial fibrillation electrograms remained difficult to differentiate reliably across all evaluated parameter combinations.

Structured PICO

Which signal processing parameters best discriminate and classify atrial fibrillation types from intra-atrial electrograms?

Population

Intra-atrial recordings from chronic atrial fibrillation (AF) patients and from subjects with electrically induced AF

Intervention

Signal processing parameters for AF rhythm classification (atrial period [AP] and its coefficient of variation [CV], number of points in the baseline [NO] and Shannon entropy [ENTR] from amplitude probability density function, correlation coefficient from correlation waveform analysis [CWA], and indexes from power spectrum density [PSD])

Comparator

Comparison among the different signal processing parameters

Outcome

Discrimination and classification of AF rhythm according to Wells' types (AF1, AF2, AF3)surrogate

A specific combination of signal processing parameters (AP, CV, NO, and CWA) can effectively discriminate type I and III atrial fibrillation from intra-atrial electrograms, which is relevant for the development of implantable atrial defibrillators.

Implication

May aid defibrillator algorithm development; leaves open reliable Type II AF classification.

Abstract

Reliable classification of atrial fibrillation from intra-atrial signals is a central task in the development of implantable atrial defibrillators. Methods that satisfactorily discriminate normal sinus rhythms, atrial tachycardia and atrial fibrillation include atrial period (AP) amplitude probability density function (APDF), correlation waveform analysis (CWA) and power spectrum density (PSD). Nevertheless their sensitivity in discriminating among and classifying AF types has been seldom addressed. We compared parameters obtained by the following methods for the classification of the AF rhythm, according to Wells' types: AP and its coefficient of variation (CV), number of points in the baseline (NO) and Shannon entropy (ENTR) from APDF, correlation coefficient from CWA and indexes obtained from PSD. To evaluate and compare the proposed methods ANOVA and Student's test were used. Cluster analysis was also used to define the best set of parameters to be used and to test the overall performances. We used data from intra-atrial recordings from chronic AF patients and from subjects with electrically induced AF. The best discriminating parameters were AP, CV, NO and CWA. Cluster analysis using this set satisfactorily discriminated AF1 and AF3 electrograms. Type II AF is still difficult to identify.