Atrial Fibrillation Signals Research Articles

Complexity AF score as a novel marker of atrial fibrillation instability.

Atrial fibrillation (AF) is a heart disease affecting millions of Americans. Clinicians evaluate AF-related risk by assessing the temporal pattern, variation, and severity of AF episodes through AF burden (AFB). However, existing prognostic tools based on these metrics are suboptimal, as they do not account for electrical complexity of AF signals. This study introduced Electrical Burden (EB) as a new marker to assess electrical instability and complexity of AF. We also developed a Complexity AF score that incorporates AFB, EB, and Poincaré analysis to assess the severity of AF. Electrocardiogram (ECG) from 50 AF patients in the Long-term AF database were analyzed. EB was calculated using four metrics and combined with AFB and Poincaré metrics to derive the Complexity AF score for each patient. Our results show that AFB, EB, and Poincaré metrics are independent markers, each describing different aspects of AF complexity. The Complexity AF score effectively distinguished between terminated (2.82 ± 1.29, 17 patients) and non-terminated AF groups (4 ± 1.46, 33 patients) (p-value < 0.05). This study emphasizes the importance of EB and Poincaré analysis as an indicator of electrical complexity of AF and highlights the utility of the Complexity AF score in accurately characterizing and stratifying AF to guide management.

IMC-ResNet: Atrial fibrillation detection method based on interlayer multiscale coupling

Atrial fibrillation(AF) is one of the most common cardiac arrhythmias, and reliable detection of it is of significant importance. This study proposes a multilayer convolutional neural network for AF detection, which introduces the Multiscale Information Interaction module composed of a Detail Enhancer and a Context Information Extractor to achieve the extraction and coupling of multiscale information between layers. The Detail Enhancer is composed of multiscale transposed convolutions to encode waveform details in the electrocardiogram (ECG). The Context Information Extractor is composed of multiscale dilated convolutions to encode the morphological information of the ECG. Furthermore, the Multiscale Information Interaction module is used in conjunction with residual blocks to model the surface feature correlation and intrinsic abstract structure of AF signals. This approach extracts implicit information of AF from ECG signals, achieving high-precision and robust detection of AF. Five-fold cross-validation on the AFDB dataset resulted in an accuracy of 99.56% and an F1-score of 99.65%. Inter-patient testing on the clinical SPHDB database yielded an accuracy of 96.17% and an F1-score of 96.14%. In addition, this study conducted experiments to evaluate the model’s robustness against noise, yielding favorable results across four types of noise tests. The results indicate that the proposed method has great potential for application in clinical auxiliary diagnosis based on wearable devices.

Downregulation of miR-568 in Atrial Fibrillation Leads to Increased Expression of NAPMT and TRMP7

Aim: Atrial fibrillation (AF) is known as the most common permanent cardiac arrhythmia worldwide with its incidence and prevalence gradually increase with age and cause significant morbidity and mortality. However, the epigenetic alterations underlying the development of this disease remains less understood. MicroRNAs (miRNAs), as one of the epigenetic regulators, are small non-coding RNAs that can target multiple genes to modulate proteins in different signaling pathways. Current studies have demonstrated that miRNAs, which are pivotal regulators of gene expression, may be involved in the pathophysiology of AF. The current study aims to clarify the miRNA regulated cellular signaling in atrial fibrillation. Material and Method: An AF model was generated by providing external electrical stimulation to the HL-1 mouse cardiomyocyte cell line for 24 hours in this study. To understand the molecular mechanisms of miRNAs underlying the AF model, miRNA microarray analysis was performed. The gene sets obtained from the microarray analysis and the bioinformatically obtained putative targets were intersected and pathway enrichment analysis was performed. qRT-PCR was performed for validation of the selected miRNAs and potential targets. Results: miRNA expression profile changes between the control group without external stimulation and the samples at the end of 3-, 6-, 12- and 24-hour stimulation were compared with microarray analysis. In particular, our transcriptomic analysis showed 5 distinctively expressed miRNAs (DEmiRNAs) whose target genes are associated with cardiovascular development within the stimulated groups in HL-1 cells. Additionally, our bioinformatics analysis revealed that targets of these miRNAs are concentrated in biological processes associated with cardiovascular development: smooth muscle cell proliferation, muscle cell proliferation, cell morphogenesis involved in differentiation and regulation of cell differentiation. Specifically, qPCR-based analyses confirmed the inverse correlation of miR-568 and potential targets of this miRNA. While miR-568 expression decreased with prolonged stimulation, expression of its potential targets, NAMPT and TRPM7, increased during prolonged stimulation. Conclusion: This study supported the potential regulative role of miRNAs and their targets in the development of AF.

Focus on the Role of Inflammation as a Bridge between Ferroptosis and Atrial Fibrillation: A Narrative Review and Novel Perspective.

In this comprehensive review, we examine the intricate interplay between inflammation, ferroptosis, and atrial fibrillation (AF), highlighting their significant roles in AF pathophysiology and pathogenesis. Augmented inflammatory responses are pivotal to AF, potentially leading to atrial remodeling and reentry phenomena by impacting calcium channels and atrial tissue fibrosis. A strong correlation exists between inflammatory cytokines and AF, underscoring the importance of inflammatory signaling pathways, such as NOD-like receptor thermal protien domain associated protein 3 (NLRP3) inflammasome, Nuclear Factor kappa B (NF- B) signaling, and Tumor necrosis factor- (TNF- ) signaling in AF development. Ferroptosis, a non-apoptotic regulated mode of cell death, has been widely studied in relation to cardiovascular diseases including heart failure, myocardial infarction, cardiomyopathy, and reperfusion injury. The interaction between ferroptosis and inflammation is complex and mutually influential. While significant progress has been made in understanding the inflammation-AF relationship, the role of inflammation as a conduit linking ferroptosis and AF remains underexplored. The specific pathogenesis and key molecules of atrial fibrosis caused by ferroptosis are still not fully understood. Here we review the role of inflammatory signaling in ferroptosis and AF. We elucidated the association between ferroptosis and AF, aiming to unveil mechanisms for targeted inhibition of atrial cell fibrosis and to propose novel therapeutic strategies for AF. This exploration is vital for advancing our knowledge and developing more effective interventions for AF, a condition deeply intertwined with inflammatory processes and ferroptotic pathways.

A novel atrial fibrillation automatic detection algorithm based on ensemble learning and multi-feature discrimination.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia disorder that necessitates long-time electrocardiogram (ECG) data for clinical diagnosis, leading to low detection efficiency. Automatic detection of AF signals within short-time ECG recordings is challenging. To address these issues, this paper proposes a novel algorithm called Ensemble Learning and Multi-Feature Discrimination (ELMD) for the identification and detection of AF signals. Firstly, a robust classifier, BSK-Model, is constructed using ensemble learning. Subsequently, the ECG R-waves are detected, and the ECG signals are segmented into consecutive RR intervals. Time domain, frequency domain, and nonlinear features are extracted from these intervals. Finally, these features are fed into the BSK-Model to discriminate AF. The proposed methodology is evaluated using the MIT-BIH AF database. The results demonstrate that when RR intervals are employed as classification units, the specificity and accuracy of AF detection in long-time ECG data exceed 99%, showcasing a significant improvement over traditional single-model classification. Additionally, the sensitivity and accuracy achieved by testing cardiac segments are both above 96%. With a minimum requirement of only four cardiac segments, AF events can be accurately identified, thereby enabling rapid discrimination of short-time single-lead ECG AF events. Consequently, this approach is suitable for real-time and accurate AF detection using low-computational-power ECG diagnostic analysis devices, such as wearable devices.

Incidence and delays of a new clinical atrial fibrillation diagnosis following an atrial fibrillation detection signal on a personal electrocardiogram device available to consumers

Abstract Background Patient and consumer-led detection of atrial fibrillation (AF) using medical devices available to consumers such as personal electrocardiogram (ECG) devices is becoming increasingly common. However, the AF patient journey after an AF signal observed on the personal ECG device is variable and poorly defined. Purpose To report the incidence and temporality of a new clinical AF diagnosis among individuals with a device-detected AF signal. Methods This retrospective cohort study was conducted using the OMNY Health open-source claims database linked to data recorded from a medical grade, United States Food and Drug Administration (FDA)-cleared, personal ECG device available to consumers. Adults (≥18 years) with ≥1 device-detected AF signal were selected between July 1, 2017, and December 31, 2020. The date of the first device-detected AF signal was defined as the "index date." Individuals were required to have continuous healthcare activity for 12 months before (baseline period) and 12 months after (follow-up period) the index date, and ≥2 device recordings (of AF or not) during a 12-month follow-up period. Individuals with a history of AF diagnosis in the baseline period were excluded. Outcomes evaluated during follow-up included: 1) New clinical AF diagnosis (defined as ≥1 non-diagnostic medical claim with an AF diagnosis [ICD-10 I48.0-I48.2, I48.91] in any position); 2) Time from index date to new clinical AF diagnosis; and 3) Number of device-detected AF signals. Results The study included 32,484 individuals with ≥1 device-detected AF signal (mean 11.8, SD: 23.7 signals). Mean age was 68.7 (SD: 11.9) years; 71.4% were 65 years or older; and 43.1% were female. The mean baseline CHA2DS2-VASc risk score was 2.0 (SD: 1.3). Overall, about a quarter (24.1%; n=7,822) of individuals had a new clinical AF diagnosis within 1 year of index date (Figure 1). The likelihood of a new clinical AF diagnosis was lower for younger age groups (age 18-44 = 8.0%; age 45-54 = 16.6%; age 55-64 = 23.0%; age 65-74 = 26.1%; age 75+: 26.2%) (Figure 2), and individuals with certain CHA2DS2-VASc risk factors (congestive heart failure: 26.4%; hypertension: 26.3%) had a slightly higher likelihood of a new clinical AF diagnosis than the overall population. In those with a new AF diagnosis, this was delayed until after 90 days from the index date in 46.7% of the patients (Figure 1). Conclusions This observational study offers unique real-world insights into the use of personal ECG devices and their potential ability to lead to clinician-diagnosed AF. Only a quarter of patients had a new clinical AF diagnosis within a year of their first device-detected AF signal, and diagnosis was delayed by &amp;gt;90 days in almost half of cases. This may relate to false positives of the device AF diagnosis, and/or poorly defined AF pathway for patient- or consumer-led AF detection.

Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform

The work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormous number of blood vessels and developing filtering techniques for the most accurate signal extraction from the heart is a challenging task. In this paper, we provide evidence of prediction and analysis of Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Long-term monitoring of diseases such as AF and VF occurrences is very important, as these will lead to occurrence of ischemic stroke, cardiac arrest and complete heart failure. The AF and VF signal classification accuracy are much higher when processed on a Graphics Processor Unit (GPU) than Central Processing Unit (CPU) or traditional Holter machines. The classifier COMMA-Z filter is applied to the highly-sensitive industry certified Bio PPG sensor placed at the earlobe and computed on GPU.

Automatic screening of patients with atrial fibrillation from 24-h Holter recording using deep learning.

As the demand for atrial fibrillation (AF) screening increases, clinicians spend a significant amount of time identifying AF signals from massive amounts of data obtained during long-term dynamic electrocardiogram (ECG) monitoring. The identification of AF signals is subjective and depends on the experience of clinicians. However, experienced cardiologists are scarce. This study aimed to apply a deep learning-based algorithm to fully automate primary screening of patients with AF using 24-h Holter monitoring. A deep learning model was developed to automatically detect AF episodes using RR intervals and was trained and evaluated on 23 621 (2297 AF and 21 324 non-AF) 24-h Holter recordings from 23 452 patients. Based on the AF episode detection results, patients with AF were automatically identified using the criterion of at least one AF episode lasting 6 min or longer. Performance was assessed on an independent real-world hospital-scenario test set (19 227 recordings) and a community-scenario test set (1299 recordings). For the two test sets, the model obtained high performance for the identification of patients with AF (sensitivity: 0.995 and 1.000; specificity: 0.985 and 0.997, respectively). Moreover, it obtained good and consistent performance (sensitivity: 1.000; specificity: 0.972) for an external public data set. Using the criterion of at least one AF episode of 6 min or longer, the deep learning model can fully automatically screen patients for AF with high accuracy from long-term Holter monitoring data. This method may serve as a powerful and cost-effective tool for primary screening for AF.

D2AFNet: A dual-domain attention cascade network for accurate and interpretable atrial fibrillation detection

Atrial fibrillation is one of the common and potentially dangerous persistent cardiac arrhythmias that are generally associated with the risk of stroke and heart failure. Manual electrocardiography diagnosis is the gold standard for the clinical detection of atrial fibrillation, but it has some drawbacks, such as being time-consuming and prone to misclassification due to inter-patient variability. Due to the powerful ability of deep learning to learn and extract rich features from huge datasets, end-to-end deep learning models are generally designed to detect abnormal atrial fibrillation signals automatically. However, these approaches usually ignore the key factors that feature maps from different channels and sequences may contribute differently to atrial fibrillation detection, making it challenging to implement accurate and interpretable models with better generalization performance. To tackle this challenge, we develop a dual-domain attention cascade D2AFNet for accurate and interpretable atrial fibrillation detection by cascading attention-based bidirectional gated recurrent units and densely connected networks embedded with channel-spatial information fusion modules. The D2AFNet can take full advantage of channel-spatial features to enhance the feature representation in the spatial domain, and then combine with the time series features in the temporal domain to form spatial–temporal fusion attention mechanisms to mine discriminative atrial fibrillation patterns. Besides, the D2AFNet can profoundly explore the different contributions of different spatial and temporal segments of feature maps for excellent interpretation. The proposed D2AFNet method is performed ten-fold cross-validation on the publicly available CPSC 2018 dataset, and achieves the accuracies of 99.49% and 99.28% in the two-class and three-class classification tasks, outperforming cutting-edge atrial fibrillation detection methods. In addition, the powerful generalization performance and inference efficiency of the D2AFNet method are also proved on another publicly available MIT-BIH dataset. The advantages of high performance and interpretability indicate that the D2AFNet method has huge potential in the computer-aided diagnosis of atrial fibrillation.

Abstract 15072: Targeted Atrial Inhibition of Parasympathetic Signaling With GiGo Inhibitory Peptides Attenuates Electrical Remodeling and Development of Persistent Atrial Fibrillation in a Canine Model

Background: While vagal contribution to paroxysmal atrial fibrillation (AF) is well demonstrated, a role for parasympathetic signaling in persistent AF is less clear. We have shown marked atrial parasympathetic hyperinnervation in a canine rapid atrial pacing (RAP) model of AF. However, the role of these new nerves in AF development is unknown. Hypothesis: RAP-induced parasympathetic hyperinnervation contributes to the onset and maintenance of AF, and a gene therapy approach inhibiting muscarinic M2 signaling (Gα i and Gα o - GiGo) in the atria can prevent persistent AF. Methods: Plasmids expressing GiGo inhibitory peptides (GiGo_ct) were injected in the atria of 7 dogs followed by electroporation to facilitate gene delivery. RAP was performed for up to 12 weeks. At terminal EP study, residual AF was recorded in the posterior left atrium (PLA), left atrial free wall (LAFW) and left atrial appendage (LAA). Regional atrial ERP was measured. Results: Controls developed ≥30 minutes of AF after a median of 4 days, and persistent AF (≥8 hours) after a median of 14 days. GiGo_ct delayed onset of AF of ≥30 minutes, and attenuated development of persistent AF. ERP was markedly shortened in the PLA and LAA of controls, and this was attenuated by GiGo_ct (p&lt;0.001). Residual AF was slower (lower dominant frequency; PLA 9.4±0.6Hz Vs 11.0±0.7Hz; LAFW 7.8±0.3Hz Vs 10.4±0.7Hz; LAA 8.9±0.6Hz Vs 9.9±0.5Hz p&lt;0.01), less fractionated (longer fractionation interval; PLA 70.0.4±5.2ms Vs 68.1±5.2ms; LAFW 98.8±4.1ms Vs 70.9±3.1ms; LAA 88.9±7.8ms Vs 80.3±2.0ms p&lt;0.01) and more organized (lower Shannon's entropy; p&lt;0.05). Conclusion: Newly sprouted atrial parasympathetic nerves play a functional role in development of persistent AF. Gene-based, selective M2 signaling inhibition by GiGo_ct attenuates electrical remodeling and prevents initiation and maintenance AF. Future optimization of gene therapy targeting autonomic remodeling may lead to novel, mechanism-guided treatments for AF.

A comparative study of the performance of methods for f-wave extraction

Objective. This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods. Approach. We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features. Main results. The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

An improved deep learning approach based on exponential moving average algorithm for atrial fibrillation signals identification

Atrial fibrillation (AF) is one of the leading causes of heart diseases worldwide. An accurate AF detection method for timely treatment is attracting great attention by widespread scientific and clinical research in recent years. However, routine AF detection using the visual examination of electrocardiogram data is strenuous and relatively subjective. In this study, an improved Elman neural network (IENN) model is specially designed for automated AF signals identification. Inspired by the exponential moving average (EMA) algorithm, a weight strategy is installed into the existing ENN network that provides more effective and comprehensive discrimination ability to the model for ECG signals recognition. Besides, our IENN network is embedded into a convolutional network architecture and the existing multi-layer perceptron and ENN networks are considered as control subjects to evaluate the validity on two public databases. The results exhibit that the proposed model yields the accuracy, specificity and sensitivity of 97.9%, 97.8% and 98.0% on the MIT-BIH AF database and 97.1%, 97.1% and 97.2% on the MIT-BIH arrhythmia database, respectively. These superior performances enable the proposed IENN model to have considerable potential as an effective tool to aid cardiologists in detecting AF signals accurately.

Classification of Arrhythmia Using Machine Learning Algorithm

Detection of Atrial fibrillation (AF) is more complex as compared to other cardiac diseases. It requires lengthy ECG signals and more time for visual inspection and analysis by the physicians. Automatic detection of AF using an expert system is essential for the investigation of ECG signals. In this study, the Physionet challenge 2017 dataset is used for the detection and classification of AF versus other signals. In this paper, ECG signals are segmented into a sample size of 250 samples for the detection of Wavelet Packet Decomposition (WPD) and approximate entropy (ApEn) features for classification. In addition to WPD and ApEn, statistical features were derived from ECG signals. The Principal Component Analysis (PCA) has been used to reduce the dimensionality of the features based on the rank. Ensemble classifiers such as AdaBoost, XGBoost and Random Forest (RF) are used for classification. The accuracy of 62.91%, 70.33% and 89% for the AdaBoost, XGBoost and Random Forest respectively. We found RF classifier is suitable for classifying AF, normal rhythm and other non-AF related abnormal heart rhythms.

Alterations of gut viral signals in atrial fibrillation: complex linkage with gut bacteriome.

The gut microbiota has a known complex association with atrial fibrillation (AF) progression, but the association of gut viruses with AF is undefined. Metagenomic data in a cohort of 50 AF patients and 50 matched controls were examined to profile the gut viral signals and determine their associations with intestinal bacteria and the AF phenotype. The gut viral alterations were examined, and the marked elevation of viral diversity, including increased Simpson, Shannon, and Pielou index, was revealed in AF patients. The specific alteration of the intestinal viral population, such as overgrowth of Streptococcus virus DT1 and Pseudomonas phage, as well as imbalanced gut viral function, dominated by integral component of the membrane, and metal ion binding were detected in AF patients. Moreover, regarding co-occurrence networks connecting viruses and bacterial organisms, increasingly disordered virus-bacteria linkages were seen in AF cases with severe AF progression. Notably, the associations of Synechococcus phage S−SM1 and Cronobacter phage CR5 with bacterial species were very tight in control individuals but markedly dampened in AF cases. Furthermore, the viral score built by the selected discriminative taxa between AF cases with or without recurrence after ablation was still significantly associated with recurrence (HR = 2.959, P = 0.0085), with a survival AUC of 0.878. We demonstrated for the first time that gut viral signatures are associated with AF, and suppressed viral-bacterial associations in AF suggest the gut virus might participate in AF progression, which has a potential value in predicting ablation outcomes.

A novel bidirectional LSTM network based on scale factor for atrial fibrillation signals classification

Atrial fibrillation (AF) is a common arrhythmia worldwide. The visual examination for electrocardiogram is the major diagnosis method, which is generally burdensome and inefficient. In this work, an improved bidirectional long short term memory (IB-LSTM) frame is specially designed for intelligence-based AF signals classification. Based on the existing B-LSTM architecture, a scale factor is installed into IB-LSTM network that enables the model system to effectively reallocate information representation and therefore learn better representation. After several pre-processing steps such as denoising, these results demonstrate consistent performance improvements with the accuracy of 98.2% and 97.5% against multiple existing approaches and lower computation costs than B-LSTM with the two public MIT-BIH AF and arrhythmia databases. The proposed IB-LSTM network is capable of trading off between model accuracy and computing resources. In particular, this research provides the first empirical exploration of redesign architecture of B-LSTM to alleviate high computation cost and information redundancy, which shows great prospects as efficient auxiliary tools for medical diagnosis.

A Classification Scheme for ECG Signals Based on Bidirectional LSTM Model

The application of ECG to diagnose cardiovascular diseases is a common method in clinical medicine, so the use of deep learning tools to achieve automatic analysis and classification of ECG has been a research direction for a wide range of researchers. This paper proposes a classification model for ECG signals based on a bidirectional LSTM model which is trained and tested using the dataset used for the PhysioNet 2017 computational cardiology challenge. The data are normalized and then processed by feature extraction. After passing a bidirectional LSTM layer, a fully connected layer, a softmax layer, and a classification layer in the model, and finally achieve the binary classification of normal signals and atrial fibrillation signals. In this process, the feature of bidirectional LSTM that can integrate contextual information is fully utilized. The experiments show that the classification accuracy of the model reaches 94.1%, demonstrating a good classification result.

A Novel Bidirectional LSTM Network Based on Scale Factor for Atrial Fibrillation Signals Classification

A Novel Bidirectional LSTM Network Based on Scale Factor for Atrial Fibrillation Signals Classification

An intelligent computer-aided approach for atrial fibrillation and atrial flutter signals classification using modified bidirectional LSTM network

Atrial fibrillation (AF) and atrial flutter (AFL) are the most common arrhythmias. Due to the similar clinical symptoms, both are one of the main causes of misdiagnosis for physicians. The visual inspection of electrocardiogram (ECG) signals is the most traditional detection strategy, however, it is often laborious and time-consuming. Thus, we specially propose a modified bidirectional long short-term memory (MB-LSTM) network for AF and AFL signals recognition. In this network, inspired by illustrious Squeeze-and-Excitation network, a feature recalibration strategy is performed on existing B-LSTM network so that this can enable the model to adaptively reallocate feature representation and thus alleviate the problem of information redundancy in B-LSTM to a certain extent. Further, we embed the proposed network into a convolutional network frame with attention mechanism and use existing LSTM, B-LSTM networks as control groups to evaluate its effectiveness with a subject-independent validation strategy on the two publicly available databases. The result shows that the model yields superior classification performance with an accuracy of 99.1% and 98.4% than several state-of-the-art methods while confirming the effectiveness of MB-LSTM. Particularly, the qualitative analysis is provided to elaborate on the mechanism of performance improvement, showing promising model practicability as an intelligent and efficient tool to assist physicians.

An intelligent computer-aided diagnosis approach for atrial fibrillation detection based on multi-scale convolution kernel and Squeeze-and-Excitation network

Atrial fibrillation (AF) is a most common arrhythmia with high morbidity and mortality. However, the conventional detection of AF is time-consuming and laborious because it is mainly completed by physician's visual inspection of electrocardiogram (ECG). Thus, it is essential to build the intelligent computer-aided diagnosis system strategy for AF detection. In this work, we present a novel intelligent approach based on the multi-scale convolution kernel (MCK) and Squeeze-and-Excitation network (SENet) for AF detection. The model not only is able to overcome the limitations that exist in the single-scale convolution kernel of traditional convolution neural network (CNN), but also explicitly establish the inter-dependences between the extracted feature channels and screen out the critical ECG features for AF signals recognition, thus improving the model performance. The results demonstrate that the proposed model achieves noticeable performance improvements with the accuracy of 98.3% and 97.5% using a subject-independent validation scheme on the two public databases. Besides, the corresponding ablation experiments show the effectiveness of the proposed MCK strategy. To our knowledge, this is the first time to redesign the convolution kernel in traditional CNN for AF detection, while showing its great potential as an auxiliary tool to help physicians.

WNT signaling in atrial fibrillation.

The Wnt signaling pathway regulates physiological processes such as cell proliferation and differentiation, cell fate decisions, and stem cell maintenance and, thus, plays essential roles in embryonic development, but also in adult tissue homeostasis and repair. The Wnt signaling pathway has been associated with heart development and repair and has been shown to be crucially involved in proliferation and differentiation of progenitor cells into cardiomyocytes. The investigation of the role of the Wnt signaling pathway and the regulation of its expression/activity in atrial fibrillation has only just begun. The present minireview (I) provides original data regarding the expression of Wnt signaling components in atrial tissue of patients with atrial fibrillation or sinus rhythm and (II) summarizes the current state of knowledge of the regulation of Wnt signaling components' expression/activity and the contribution of the various levels of the Wnt signal transduction pathway to the processes of the development, maintenance, and progression of atrial fibrillation.