Atrial Fibrillation Electrocardiogram Research Articles

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm.

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to -16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%.

Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning

There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching >85% accuracy for various cardiac pathologies. Despite the high accuracy at individual institutions, challenges remain when it comes to multi-center deployment. Transfer learning (TL) is a technique in which a model trained for a specific task is repurposed for another related task, in this case ECG ML model trained at one institution is fine-tuned to be utilized to classify ECGs at another institution. Models trained at one institution, however, might not be generalizable for accurate classification when deployed broadly due to differences in type, time, and sampling rate of traditional ECG acquisition. In this study, we evaluate the performance of time domain (TD) and frequency domain (FD) convolutional neural network (CNN) classification models in an inter-institutional scenario leveraging three different publicly available datasets. The larger PTB-XL ECG dataset was used to initially train TD and FD CNN models for atrial fibrillation (AFIB) classification. The models were then tested on two different data sets, Lobachevsky University Electrocardiography Database (LUDB) and Korea University Medical Center database (KURIAS). The FD model was able to retain most of its performance (>0.81 F1-score), whereas TD was highly affected (<0.53 F1-score) by the dataset variations, even with TL applied. The FD CNN showed superior robustness to cross-institutional variability and has potential for widespread application with no compromise to ECG classification performance.

Prediction of Correlation Length From the Surface Electrocardiogram in Atrial Fibrillation

Prediction of Correlation Length From the Surface Electrocardiogram in Atrial Fibrillation

Development of an AI-Driven QT Correction Algorithm for Patients in Atrial Fibrillation

BackgroundProlongation of the QTc interval is associated with the risk of torsades de pointes. Determination of the QTc interval is therefore of critical importance. There is no reliable method for measuring or correcting the QT interval in atrial fibrillation (AF). ObjectivesThe authors sought to evaluate the use of a convolutional neural network (CNN) applied to AF electrocardiograms (ECGs) for accurately estimating the QTc interval and ruling out prolongation of the QTc interval. MethodsThe authors identified patients with a 12-lead ECG in AF within 10 days of a sinus ECG, with similar (±10 ms) QRS durations, between October 23, 2001, and November 5, 2021. A multilayered deep CNN was implemented in TensorFlow 2.5 (Google) to predict the MUSE (GE Healthcare) software-generated sinus QTc value from an AF ECG waveform, demographic characteristics, and software-generated features. ResultsThe study identified 6,432 patients (44% female) with an average age of 71 years. The CNN predicted sinus QTc values with a mean absolute error of 22.2 ms and root mean squared error of 30.6 ms, similar to the intrinsic variability of the sinus QTc interval. Approximately 84% and 97% of the model’s predictions were contained within 1 SD (±30.6 ms) and 2 SD (±61.2 ms) from the sinus QTc interval. The model outperformed the AFQTc method, exhibiting narrower error ranges (mean absolute error comparison P < 0.0001). The model performed best for ruling out QTc prolongation (negative predictive value 0.82 male, 0.92 female; specificity 0.92 male, 0.97 female). ConclusionsA CNN model applied to AF ECGs accurately predicted the sinus QTc interval, outperforming current alternatives and exhibiting a high negative predictive value.

Abstract 14688: Identification of Patients With Atrial Fibrillation Using an Electrocardiogram in Sinus Rhythm: External Reproduction and Validation of a Deep Neural Network Algorithm

Introduction: Atrial fibrillation (AF) can occur asymptomatically and can only be diagnosed while the arrhythmia is present. Hence, it is underdetected. An artificial intelligence (AI) algorithm has been developed by Attia et al. to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm (SR). We aim to reproduce these findings in an unrelated patient population and unrelated clinical center using the same deep neural network (DNN) architecture. Methods: All patients older than 18 years with at least one ECG in SR performed at our center between 10/1/2002 and 8/31/2022 were included in the study. Diagnostic labels were given by the GE-Marquette ECG system. For patients without an ECG in AF, all ECGs in SR were included. For patients with one or more ECGs in AF, all ECG in SR starting 31 days before the AF event were included. The ECGs were randomly allocated to a training, internal validation, and testing dataset in a 7:1:2 ratio. A DNN following the architecture of Attia et al. was trained to discriminate between patients with and without AF. Results: The dataset consisted of 494.042 ECGs in SR from 142.310 patients. AF was diagnosed in 8.9%. Testing the model on the first ECG of each patient (main analysis) resulted in a sensitivity of 78.9% (95% CI, 77.6 - 78.5), specificity of 78.0% (95% CI, 77.5 - 78.5), accuracy of 78.1 (95% CI, 77.6 - 78.5), area under the receiver operating curve (AUC) of 0.87 (95% CI, 0.86 - 0.87), and F1 score of 39.3% (95% CI, 38.1 - 40.6). Testing the model on all ECGs in the first 31 days and selecting the maximum probability of AF (secondary analysis) increased the sensitivity to 81.1 (95% CI, 78.4 - 83.7), specificity to 79.8 (95% CI, 79.3 - 80.3), accuracy to 79.8 (95% CI, 79.4 - 80.3), AUC to 0.88 (95% CI, 0.87 - 0.89), but lowered the F1 score to 20.8% (95% CI, 19.51 - 22.0). Conclusion: An AI-enabled ECG algorithm for the identification of patients with AF on a SR-ECG can be reproduced with external data in an external center at clinical grade accuracy.

Prioritising electrocardiograms for manual review to improve the efficiency of atrial fibrillation screening.

Screening for atrial fibrillation (AF) could reduce the incidence of stroke by identifying undiagnosed AF and prompting anticoagulation. However, screening may involve recording many electrocardiograms (ECGs) from each participant, several of which require manual review which is costly and time-consuming. The aim of this study was to investigate whether the number of ECG reviews could be reduced by using a model to prioritise ECGs for review, whilst still accurately diagnosing AF. A multiple logistic regression model was created to estimate the likelihood of an ECG exhibiting AF based on the mean RR-interval and variability in RR-intervals. It was trained on 1,428 manually labelled ECGs from 144 AF screening programme participants, and evaluated using 11,443 ECGs from 1,521 participants. When using the model to order ECGs for review, the number of reviews for AF participants was reduced by 74% since no further reviews are required after an AF ECG is identified; however, it did not impact the number of reviews in non-AF participants (the vast majority of participants), so the overall number of reviews was reduced by 3% with no missed AF diagnoses. When using the model to also exclude ECGs from review, the overall number of reviews was reduced by 28% with no missed AF diagnoses, and by 53% with only 4% of AF diagnoses missed. In conclusion, the workload can be reduced by using a model to prioritise ECGs for review. Ordering ECGs alone only provides only a moderate reduction in workload. The additional use of a threshold to exclude ECGs from review provides a much greater reduction in workload at the expense of some missed AF diagnoses. Clinical Relevance-This shows the potential benefit of using a model to prioritise electrocardiograms for review in order to reduce the manual workload of AF screening.

Classification of Fibrillation Organisation Using Electrocardiograms to Guide Mechanism-Directed Treatments.

Background: Atrial fibrillation (AF) and ventricular fibrillation (VF) are complex heart rhythm disorders and may be sustained by distinct electrophysiological mechanisms. Disorganised self-perpetuating multiple-wavelets and organised rotational drivers (RDs) localising to specific areas are both possible mechanisms by which fibrillation is sustained. Determining the underlying mechanisms of fibrillation may be helpful in tailoring treatment strategies. We investigated whether global fibrillation organisation, a surrogate for fibrillation mechanism, can be determined from electrocardiograms (ECGs) using band-power (BP) feature analysis and machine learning.Methods: In this study, we proposed a novel ECG classification framework to differentiate fibrillation organisation levels. BP features were derived from surface ECGs and fed to a linear discriminant analysis classifier to predict fibrillation organisation level. Two datasets, single-channel ECGs of rat VF (n = 9) and 12-lead ECGs of human AF (n = 17), were used for model evaluation in a leave-one-out (LOO) manner.Results: The proposed method correctly predicted the organisation level from rat VF ECG with the sensitivity of 75%, specificity of 80%, and accuracy of 78%, and from clinical AF ECG with the sensitivity of 80%, specificity of 92%, and accuracy of 88%.Conclusion: Our proposed method can distinguish between AF/VF of different global organisation levels non-invasively from the ECG alone. This may aid in patient selection and guiding mechanism-directed tailored treatment strategies.

Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network

Photoplethysmogram (PPG) is one of the most widely measured biosignals alongside electrocardiogram (ECG). Due to the simplicity of measurement and the advent of wearable devices, there have been growing interest in using PPG for a variety of healthcare applications such as cardiac function estimation. However, unlike ECG, there are not many large databases available for clinically significant analyses of PPG. To overcome this issue, a Generative Adversarial Network-based model to generate PPG using ECG as input is proposed. The network was trained using a large open database of biosignals measured from surgical patients and was externally validated using an alternative database sourced from another hospital. The generated PPG was compared with the reference PPG using percent root mean square difference (PRD) and Pearson correlation coefficient to evaluate the morphological similarity. Additionally, heart rate measured from the reference ECG, reference PPG, and generated PPG, and compared through repeated measure analysis of variance to test for any significant differences. The mean PRD was 32± 10% and the mean correlation coefficient was 0.95± 0.05 in the test dataset. The HR from the three biosignals showed no significant difference with a $p$ -value of 0.473. When the optimized GAN model was tested on atrial fibrillation ECG from a third dataset, the mean correlation coefficient between the generated PPG heart rate and the ECG heart rate was 0.94± 0.15, with paired t-test resulting in $p$ -value of 0.64. The results indicate that the proposed method may provide a valuable alternative to augmenting biosignal databases that are abundant in one signal while lacking in another.

Abstract WP255: Can Normal Electrocardiogram Rule Out Newly Diagnosed Atrial Fibrillation in Ischemic Stroke Patients

Introduction: Electrocardiogram (ECG) findings of premature atrial contraction and prolonged PR interval are associated with risk of onset atrial fibrillation (AF) in cryptogenic stroke. We sought to see if normal ECG and AF incidence is incompletely understood. Methods: From a prospective single-hospital stroke registry from 2018, we identified ischemic stroke patients who had ECG done on admission for review. We excluded patients with AF on admission ECG, history of AF, and implanted device with cardiac monitoring capability. Normal ECG was interpreted based on the standardized reporting guidelines for ECG studies evaluating risk stratification of emergency department patients. Stroke subtype was diagnosed according to the TOAST classification: large artery atherosclerosis (LAA), small vessel occlusion (SVO), cardioembolism, others/undetermined and embolic stoke of undetermined source (ESUS) criteria. We compared the incidence of newly diagnosed AF during hospitalization and from outpatient cardiac event monitoring between normal and abnormal ECG. Results: Of the 558 consecutive acute ischemic stroke patients, we excluded 135 with AF on admission ECG or history of AF and 9 with implanted devices. Of the remaining 414 patients that were included in the study, ESUS (31.2%) was the most frequent stroke subtype, followed by LAA (30.0%), SVO (14.0%), others/undetermined (15.7%), and cardioembolism (9.2%). Normal ECG was observed in 125 patients (30.2%). Cardioembolic subtype was less frequent in the normal versus abnormal ECG group (1.6% vs. 12.5%, p&lt;0.001). New AF was detected in 17/414 patients (4.1%) during hospitalization. Of these 17 patients, none had normal ECG (0/125) and all had abnormal ECG (17/289, 5.9%) (p=0.002). After discharge, of 111 patients undergoing 4-week outpatient cardiac monitoring, new AF was detected in 16 (14.4%). Of these 16 patients, only 1 had a normal ECG (1/35, 2.9%) while 15 had abnormal ECG (15/76, 19.7%) (p=0.02). Conclusions: Normal ECG at admission for acute ischemic stroke is associated with low likelihood of detection of new atrial fibrillation in either the inpatient or outpatient setting.

Intermittent vs continuous electrocardiogram event recording for detection of atrial fibrillation—Compliance and ease of use in an ambulatory elderly population

BackgroundThere are many atrial fibrillation (AF) screening devices available. Validation studies have mainly been performed in optimal settings in the young population.HypothesisWe aim to compare the yield of AF detection, compliance, and patient‐based experience in an ambulatory elderly population by using intermittent electrocardiogram (ECG) recordings and continuous event recording simultaneously.MethodsThe study participants were part of the STROKESTOP II study, a Swedish screening study for AF. All participants were 75/76 years of age, were clinically free of AF, and had N‐terminal pro b‐type natriuretic peptides levels ≥125 ng/L. AF screening was performed in parallel during a 2‐week period, using a continuous event recording device (R‐test 4; Novacor) and 30‐second intermittent recordings using a handheld ECG device (Zenicor II) four times daily. Participants were asked to fill out a questionnaire with regard to compliance and ease of use of the devices.ResultsDuring continuous event recording, 6% (n = 15/269) were diagnosed with AF and intermittent ECG detected AF in 2% (n = 5/269) of the participants (P = .002). No new cases of AF were detected using intermittent ECG monitoring only, but some episodes were detected in parallel for patients. On a graded ordinal scale of 1 to 5, with 1 reflecting “very easy to use”, continuous monitoring was graded 2 (interquartile range [IQR]: 1‐3) compared to intermittent 1 (IQR: 1‐1) (P < .001).ConclusionContinuous event recording detected three times more AF compared to intermittent ECG in an elderly ambulatory population. Compliance and user‐friendliness were rated higher for the intermittent ECG device.

Block Term Decomposition of ECG Recordings for Atrial Fibrillation Analysis: Temporal and Inter-Patient Variability

Responsible for 25% of strokes and 1/3 of hospitalizations due to cardiac related disturbances, atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice, considered as the last great frontier of cardiac electrophysiology. Its mechanisms are not completely understood, and a precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary to better understand this challenging cardiac condition. Recently, the block term decomposition (BTD) has been proposed as a powerful tool to noninvasively extract AA in AF ECG signals. However, this tensor factorization technique was performed only in short ECG recordings and illustrated in single patients. To assess its performance and variability through different subjects, BTD is applied to a population of 10 AF patients in this paper. Also, its time variability is evaluated by means of long segments of AF ECG with varying observation window size. Experimental results show the consistency of BTD as an AA extraction tool, outperforming two well-known matrix-based methods in most of the observed cases for long and short AF ECG recordings.

Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review

Arrhythmia is a type of disorder that affects the pattern and rate of the heartbeat. Among the various arrhythmia conditions, atrial fibrillation (AF) is the most prevalent. AF is associated with a chaotic, and frequently fast, heartbeat. Moreover, AF increases the risk of cardioembolic stroke and other heart-related problems such as heart failure. Thus, it is necessary to screen for AF and receive proper treatment before the condition progresses. To date, electrocardiogram (ECG) feature analysis is the gold standard for the diagnosis of AF. However, because it is time-varying, AF ECG signals are difficult to interpret. The ECG signals are often contaminated with noise. Further, manual interpretation of ECG signals may be subjective, time-consuming, and susceptible to inter-observer variabilities. Various computer-aided diagnosis (CADx) methods have been proposed to remedy these shortcomings. In this paper, different CADx systems developed by researchers are discussed. Also, the potentials of the CADx system are highlighted.

Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks.

Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1 s electrocardiogram (ECG) segments to time-frequency images, and then, the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation Database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp), and the area under the ROC curve (AUC) results are 74.96%, 86.41%, and 0.88, respectively. When excluding an extremely poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp, and AUC values of 79.05%, 89.99%, and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode.

Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The electrocardiogram (ECG) signals are widely used to diagnose the AF. Automated diagnosis of AF can aid the clinicians to make a more accurate diagnosis. Hence, in this work, we have proposed a decision support system for AF using a novel nonlinear approach based on flexible analytic wavelet transform (FAWT). First, we have extracted 1000 ECG samples from the long duration ECG signals. Then, log energy entropy (LEE), and permutation entropy (PEn) are computed from the sub-band signals obtained using FAWT. The LEE and PEn features are extracted from different frequency bands of FAWT. We have found that LEE features showed better classification results as compared to PEn. The LEE features obtained maximum accuracy, sensitivity, and specificity of 96.84%, 95.8%, and 97.6% respectively with random forest (RF) classifier. Our system can be deployed in hospitals to assist cardiac physicians in their diagnosis.

AUTOMATED DETECTION OF ATRIAL FIBRILLATION ECG SIGNALS USING TWO STAGE VMD AND ATRIAL FIBRILLATION DIAGNOSIS INDEX

Atrial fibrillation (AF) is a common atrial arrhythmia occurring in clinical practice and can be diagnosed using electrocardiogram (ECG) signal. The conventional diagnostic features of ECG signal are not enough to quantify the pathological variations during AF. Therefore, an automated detection of AF pathology using the new diagnostic features of ECG signal is required. This paper proposes a novel method for the detection of AF using ECG signals. In this work, we are using a novel nonlinear method namely, the two-stage variational mode decomposition (VMD) to analyze ECG and deep belief network (DBN) for automated AF detection. First, the ECG signals of both normal sinus rhythm (NSR) and AF classes are decomposed into different modes using VMD. The first mode of VMD is decomposed in the second stage as this mode captures the atrial activity (AA) information during AF. The remaining modes of ECG captures the ventricular activity information. The sample entropy (SE) and the VMD estimated center frequency features are extracted from the sub-modes of AA mode and ventricular activity modes. These extracted features coupled with DBN classifier is able to classify normal and AF ECG signals with an accuracy, sensitivity and specificity values of 98.27%, 97.77% and 98.67%, respectively. We have developed an atrial fibrillation diagnosis index (AFDI) using selected SE and center frequency features to detect AF with a single number. The system is ready to be tested on huge database and can be used in main hospitals to detect AF ECG classes.

A Missense Variant in PLEC Increases Risk of Atrial Fibrillation

BackgroundGenome-wide association studies (GWAS) have yielded variants at >30 loci that associate with atrial fibrillation (AF), including rare coding mutations in the sarcomere genes MYH6 and MYL4. ObjectivesThe aim of this study was to search for novel AF associations and in doing so gain insights into the mechanisms whereby variants affect AF risk, using electrocardiogram (ECG) measurements. MethodsThe authors performed a GWAS of 14,255 AF cases and 374,939 controls, using whole-genome sequence data from the Icelandic population, and tested novel signals in 2,002 non-Icelandic cases and 12,324 controls. They then tested the AF variants for effect on cardiac electrical function by using measurements in 289,297 ECGs from 62,974 individuals. ResultsThe authors discovered 2 novel AF variants, the intergenic variant rs72700114, between the genes LINC01142 and METTL11B (risk allele frequency = 8.1%; odds ratio [OR]: 1.26; p = 3.1 × 10−18), and the missense variant p.Gly4098Ser in PLEC (frequency = 1.2%; OR: 1.55; p = 8.0 × 10−10), encoding plectin, a cytoskeletal cross-linking protein that contributes to integrity of cardiac tissue. The authors also confirmed 29 reported variants. p.Gly4098Ser in PLEC significantly affects various ECG measurements in the absence of AF. Other AF variants have diverse effects on the conduction system, ranging from none to extensive. ConclusionsThe discovery of a missense variant in PLEC affecting AF combined with recent discoveries of variants in the sarcomere genes MYH6 and MYL4 points to an important role of myocardial structure in the pathogenesis of the disease. The diverse associations between AF variants and ECG measurements suggest fundamentally different categories of mechanisms contributing to the development of AF.

QRS residual removal in atrial activity signals extracted from single lead: a new perspective based on signal extrapolation

Atrial activity (AA) signal must first be extracted from atrial fibrillation electrocardiogram (AF ECG) before it is used to characterise AF. However, extracting AA signal is not an easy task, especially from single-lead ECG recording. The AA signals within QRS intervals extracted by the existing single-lead extraction methods are often heavily distorted due to the existence of large QRS residuals. In this study, the authors focus on reducing the QRS residuals in the extracted AA signals, and propose a novel signal extrapolation based method. AA signal is assumed to be band-limited, and a dedicated extrapolation formula is derived. Based on this extrapolation formula, the AA samples within QRS interval are reconstructed by using the ones in the adjacent SQ segments. The experiments with simulated AF ECGs showed that, after using the proposed method, the normalised mean square error of the AA signal extracted by average beat subtraction method decreased by 26–50%, 15–36%, 12–40 and 42–63% for simulated AF ECGs in lead I, II, V1 and V6, respectively. Experiments with real AF ECG also proved that the proposed method is able to greatly reduce the ventricular residuals of the extracted AA signal.

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

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.

Atrial signal extraction in atrial fibrillation electrocardiograms using a tensor decomposition approach.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice and remains a major challenge in cardiology. The noninvasive analysis of AF usually requires the estimation of the atrial activity (AA) signal in surface electrocardiogram (ECG) recordings. The present contribution puts forward a tensor decomposition approach for noninvasive AA extraction in AF ECG recordings. As opposed to the matrix approach, tensor decompositions are generally unique under mild conditions and have the potential to perform source separation in scenarios with a limited number of electrodes. An experimental study on a synthethic signal model and a real AF ECG recording evaluates the performance of the so-called block term tensor decomposition approach as compared to matrix techniques such as principal component analysis and independent component analysis.