Principal component analysis of atrial fibrillation: Inclusion of posterior ECG leads does not improve correlation with left atrial activity

Raine, Daniel; Langley, Philip; Shepherd, Ewen; Lord, Sue; Murray, Steven R.; Murray, Alan; Bourke, John

doi:10.1016/j.medengphy.2014.12.008

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…PCA is a very well-known unsupervised method often employed in ECG signal processing [7,8]. The main objective of PCA consists in expressing the information contained in a dataset by a smaller number of variables called principal components.…”

Section: Pattern Recognition Methodsmentioning

confidence: 99%

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

Haddi¹,

Ananou²,

Trardi³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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The aim of this work is to develop an efficient diagnosis method for atrial fibrillation (AF) arrhythmia based on inter-beat interval time series analysis and relevance vector machine (RVM) classifier. Automatic and fast AF diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. The published results do not show satisfactory detection accuracy especially for brief duration as short as one minute. Although RVM has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt RVM for AF diagnosis. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR interval window and then three specific features were calculated. The RVM classifier was trained on 2000 randomly selected samples from the merged database. The results showed that the RVM model performed better than do existing algorithms, with 99.20% for both sensitivity and specificity.

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Section: Pattern Recognition Methodsmentioning

confidence: 99%

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

Haddi¹,

Ananou²,

Trardi³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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“…in mV 2 , where m V 1,r is the contribution of the r th source to lead V1 and s r. is the r th source in time domain. The P (r) of an AA source is expected to be relatively strong (> 10 −4 mV 2 ), since lead V1 is the one that typically best reflects AA in AF ECGs, as this lead strongly correlates with the AA from the right atrium and moderately correlates with that from the left atrium [24].…”

Section: A Signal Quality Measurement In Af Episodesmentioning

confidence: 99%

Blind Source Separation in Persistent Atrial Fibrillation Electrocardiograms Using Block-Term Tensor Decomposition With Löwner Constraints

Deoliveira¹,

Goulart

Fernandes³

et al. 2022

IEEE J. Biomed. Health Inform.

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The estimation of the atrial activity (AA) signal from electrocardiogram (ECG) recordings is an important step in the noninvasive analysis of atrial fibrillation (AF), the most common sustained arrhythmia encountered in clinical practice. This problem admits a blind source separation (BSS) formulation that has been recently posed as a tensor factorization, using the Hankelbased block term decomposition (BTD), which is particularly well-suited to the estimation of exponential models like AA during AF. However, persistent forms of AF are characterized by short R-R intervals and very disorganized (or weak) AA, making it difficult to model AA directly and perform its successful extraction through Hankel-BTD. To overcome this drawback, the present work proposes a tensor approach to estimate QRS complexes and subtract them from the ECG, resulting in a signal that, ideally, only contains the AA component. Such an approach tackles the problem of blind separation of rational functions, which models QRS complexes explicitly. The data tensor admitting a BTD is built from Löwner matrices generated from each lead of the observed ECG. To this end, this paper formulates a variant of the recently proposed constrained alternating group lasso (CAGL) algorithm that imposes Löwner structure on the decomposition blocks. This is done by performing an orthogonal projection, which we explicitly derive, at each iteration of CAGL. Results from experiments with synthetic data show the consistency of the proposed Löwner-constrained AGL (LCAGL) in extracting the desired sources. Experimental results obtained on a population of 20 patients suffering from persistent AF show that the proposed variant outperforms other tensor-based methods in terms of atrial signal estimation quality from ECG records as short as a single heartbeat.

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“…The blind source separation process aims to separate the atrial and ventricular activities into different components considering all recorded leads. Here we consider principal component analysis as the blind source separation algorithm [7,15,32]. …”

Section: Application To Multi-lead Ecgmentioning

confidence: 99%

Wavelet Entropy as a Measure of Ventricular Beat Suppression from the Electrocardiogram in Atrial Fibrillation

Langley

2015

Entropy

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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.

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Principal component analysis of atrial fibrillation: Inclusion of posterior ECG leads does not improve correlation with left atrial activity

Cited by 5 publications

References 13 publications

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

Blind Source Separation in Persistent Atrial Fibrillation Electrocardiograms Using Block-Term Tensor Decomposition With Löwner Constraints

Wavelet Entropy as a Measure of Ventricular Beat Suppression from the Electrocardiogram in Atrial Fibrillation

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