Automatic Arrhythmia Detection Research Articles

Feasibility of using chest strap and dry electrode system for longer term cardiac arrhythmia monitoring: Results from a pilot observational study

Background and aimCardiac arrhythmia diagnostic yield improves with increased duration of monitoring. We investigated patient comfort, diagnostic quality of ECG, and arrhythmia diagnostic yield using a single lead longer term external cardiac monitor (ECM). MethodsThe observational ECM feasibility study enrolled patients with increased risk of cardiac arrhythmia. The ECM investigational prototype was designed using a chest strap with dry electrodes connected to module capable of triggered loop recording of ECG, and automatic detection of arrhythmia. In group-A of study (24-h inpatient), patients wore ECM and Holter that recorded ECG from the ECM and adhesive electrodes. In group-B of study (12-weeks ambulatory), at monthly follow-ups patients filled out a comfort survey and device stored arrhythmia episodes were reviewed. ResultsThe study enrolled 34 patients (38 % females, average age 57.5 years, 65 % had palpitations, 12 % had syncope). Diagnostic quality ECG was recorded on 76.5 % of the monitoring duration in 12 of 20 patients with reviewable data in group-A, with motion artifacts causing loss in ECG signal for 18.7 % of the time. In 14 patients in group-B, 94.9 % of the survey responses indicated that ECM was comfortable to wear. Cardiac arrhythmia was observed in 4 of 17 patients (24 %) in group-A and 9 of 14 patients (64 %) in group-B in device recorded episodes. All ECM detected pause and tachycardia were inappropriate detections due to motion artifacts and temporary device removal. ConclusionThe chest strap-based ECM device was mostly comfortable to wear and recorded diagnostic quality ECG in three-fourth of monitoring period. Cardiac arrhythmia was observed in 64 % of patients over 3-month monitoring along with large number of motion artifact induced inappropriate detections.

An Untethered Heart Rhythm Monitoring System with Automated AI‐Based Arrhythmia Detection for Closed‐Loop Experimental Application

AbstractThe heart produces bioelectrical signals, which can be measured as an electrocardiogram (ECG) for the detection of rhythm disturbances. Rapid and precise detection of these arrhythmias is crucial for their termination by closed‐looped therapeutic interventions to counteract detrimental effects. However, there is a current lack of such systems tailored for experimental cardiovascular applications. This hampers not only in‐depth mechanistic studies but also translational testing of new therapeutic strategies, especially in an untethered manner in awake animal models. To break new ground, recent advances to develop a non‐invasive AI‐supported heart rhythm monitoring system for untethered automated arrhythmia detection in a continuous manner is combined. This system is housed in a lightweight jacket for mobile use and includes an on‐skin ECG sensor, a low‐power microprocessor unit, a massive data storage unit, and a power‐management system. By implementing a novel hybrid algorithm based on so‐called heart rate (R‐R) variability and a case‐specific AI model, 100% sensitivity and 95% specificity is achieved in detecting atrial arrhythmias within 2 s upon initiation in adult rats. Thereby, the novel system sets the stage for advanced mechanistic studies and therapeutic testing, including closed‐loop applications aiming for the termination of a broad range of atrial arrhythmias.

A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection.

Arrhythmia is an important indication of underlying cardiovascular diseases (CVD) and is prevalent worldwide. Accurate diagnosis of arrhythmia is crucial for timely and effective treatment. Electrocardiogram (ECG) plays a key role in the diagnosis of arrhythmia. With the continuous development of deep learning and machine learning processes in the clinical field, ECG processing algorithms have significantly advanced the field with timely and accurate diagnosis of arrhythmia. In this study, we combined the wavelet time-frequency maps with the novel Swin Transformer deep learning model for the automatic detection of cardiac arrhythmias. In specific practice, we used the MIT-BIH arrhythmia dataset, and to improve the signal quality, we removed the high-frequency noise, artifacts, electromyographic noise and respiratory motion effects in the ECG signals by the wavelet thresholding method; we used the complex Morlet wavelet for the feature extraction, and plotted wavelet time-frequency maps to visualise the time-frequency information of the ECG; we introduced the Swin Transformer model for classification and achieve high classification accuracy of ECG signals through hierarchical construction and self attention mechanism, and combines windowed multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) to comprehensively utilise the local and global information. To enhance the confidence of the experimental results, we evaluated the performance using intra-patient and inter-patient paradigm analyses, and the model classification accuracies reached 99.34% and 98.37%, respectively, which are better than the currently available detection methods. The results reveal that our proposed method is superior to currently available methods for detecting arrhythmia ECG. This provides a new idea for ECG based arrhythmia diagnosis.

A multi-branch multi-scale convolutional neural network using automatic detection of fetal arrhythmia

A multi-branch multi-scale convolutional neural network using automatic detection of fetal arrhythmia

A multimodal deep learning tool for detection of junctional ectopic tachycardia in children with congenital heart disease

Junctional ectopic tachycardia (JET) is a prevalent life-threatening arrhythmia in children with congenital heart disease. It has a marked resemblance to normal sinus rhythm, often leading to delay in diagnosis and management. The study sought to develop a novel multimodal automated arrhythmia detection tool that outperforms existing JET detection tools. This is a cohort study performed on 40 patients with congenital heart disease at Texas Children's Hospital. Electrocardiogram and central venous pressure waveform data produced by bedside monitors are captured by the Sickbay platform. Convolutional neural networks (CNNs) were trained to classify each heartbeat as either normal sinus rhythm or JET based only on raw electrocardiogram signals. Our best model improved the area under the curve from 0.948 to 0.952 and the true positive rate at 5% false positive rate from 71.8% to 80.6%. Using a 3-model ensemble further improved the area under the curve to 0.953 and the true positive rate at 5% false positive rate to 85.2%. Results on a subset of data show that adding central venous pressure can significantly improve area under the receiver-operating characteristic curve from 0.646 to 0.825. This study validates the efficacy of deep neural networks to notably improve JET detection accuracy. We have built a performant and reliable model that can be used to create a bedside alarm that diagnoses JET, allowing for precise diagnosis of this life-threatening postoperative arrhythmia and prompt intervention. Future validation of the model in a larger cohort is needed.

A Smartphone-Based M-Health Monitoring System for Arrhythmia Diagnosis.

Deep learning technology has been widely adopted in the research of automatic arrhythmia detection. However, there are several limitations in existing diagnostic models, e.g., difficulties in extracting temporal information from long-term ECG signals, a plethora of parameters, and sluggish operation speed. Additionally, the diagnosis performance of arrhythmia is prone to mistakes from signal noise. This paper proposes a smartphone-based m-health system for arrhythmia diagnosis. First, we design a cycle-GAN-based ECG denoising model which takes real-world noise signals as input and aims to produce clean ECG signals. In order to train its two generators and two discriminators simultaneously, we explore an unsupervised pre-training strategy to initialize the generator and accelerate the convergence speed during training. Second, we propose an arrhythmia diagnosis model based on the time convolution network (TCN). This model can identify 34 common arrhythmia events using eight-lead ECG signals, and we deploy such a model on the Android platform to develop an at-home ECG monitoring system. Experimental results have demonstrated that our approach outperforms the existing noise reduction methods and arrhythmia diagnosis models in terms of denoising effect, recognition accuracy, model size, and operation speed, making it more suitable for deployment on mobile devices for m-health monitoring services.

Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach

Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.

HA-ResNet: Residual Neural Network With Hidden Attention for ECG Arrhythmia Detection Using Two-Dimensional Signal.

Arrhythmia is an abnormal heart rhythm, a common clinical problem in cardiology. Long-term or severe arrhythmia may lead to stroke and sudden cardiac death. The electrocardiogram (ECG) is the most commonly used tool to diagnose arrhythmia. However, the traditional diagnosis relies on experts for manual interpretation, which is time-consuming and laborious. In recent years, many automatic arrhythmia detection methods have emerged due to advancements in deep learning. These methods can reduce manual intervention and improve diagnostic efficiency. However, extracting useful features from raw ECG signals for arrhythmia detection is still challenging due to the low frequency of ECG signals and noise distribution. In this paper, we propose a novel hidden attention residual network (HA-ResNet) for automated arrhythmia classification. In this model, the one-dimensional ECG signals are first converted into two-dimensional images and fed into an embedding layer to obtain the relevant shallow features in ECG. Then, a hidden attention layer combining Squeeze-and-Excitation (SE) block and Bidirectional Convolutional LSTM (BConvLSTM) is used to further capture the deep Spatio-temporal features. We evaluate our HA-ResNet on two public datasets and achieve F1 scores of 96.0%, 96.7%, and 87.6% on 2s segments, 5s segments, and 10s segments, respectively, which significantly outperform the existing state-of-the-art approaches. The experimental results demonstrate the effectiveness and generalization of our method.

Study and analysis on detection, classification, and prediction of cardiac arrhythmia using soft computing tool

Cardiac Arrhythmia (CA) is a disorder of heartbeat or rhythm, that happens when the electrical signals that synchronize the heartbeats do not function properly. The Electro Cardio Gram (ECG) is the electrical realization of the expanding and contracting action of the heart and can be registered easily with the electrodes placed near the chest. Hence, due to the complexity of analyzing the huge number of signals in ECG records, it has become one of the major challenges to cardiologists to make early and accurate diagnoses and prognoses. Therefore, there is an essential need for accurate automatic arrhythmia classification. According to the records of the World Health Organization (WHO), 4.5 million CA patients are reporting alone in the United States. Therefore, it is stated as one of the most common reasons for death worldwide and it is very essential to the early diagnosis and prevention of CA. Hence, this research article mainly focuses to analyse the various methods used for the classification, early diagnosis, and prevention of CA. This research presents the overview of a few research articles suggesting different methods based on various fields like IOT, Machine Learning (ML) approaches, Deep Learning (DL) approaches, and so on for the automatic detection of Cardiac Arrhythmia. The literature work mainly focuses on various early Detection, prediction, and classification techniques for CA. The research gaps were also analyzed from these papers and elaborated for further research work which can be helpful for society.

Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion.

Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into two-dimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices.

IM-ECG: An interpretable framework for arrhythmia detection using multi-lead ECG

Multi-lead electrocardiogram (ECG) is a fundamental and reliable diagnostic tool for the detection of heart arrhythmias. An increasing number of deep neural network models have been proposed for automatic arrhythmia detection using multi-lead ECGs. However, most of the existing models focus on intra-lead features while neglecting the inter-lead ones and fail to explain the reasons behind decisions due to their black-box nature. To address these challenges, we propose an interpretable multi-lead ECG detection framework that tackles these limitations in two key areas. First, a flexible double-kernel residual block (DKR-block) is proposed to effectively extract inter-lead and intra-lead features. In addition, a new two-dimensional ECG classification model is built based on the DKR-block for accurate arrhythmia detection. Second, a visualization method based on gradient-weighted class activation mapping is used to visualize the indicative features of the model’s decisions. The proposed model achieves an AUCmacro of 0.929 on the PTB-XL data, a F1micro of 0.932 on the HFECGIC dataset, and an F1macro of 0.989 on the LUDB dataset. The results show that the proposed model outperforms several state-of-the-art methods, and the visualization features of its classification decision are consistent with the medical background knowledge.

Target-oriented augmentation privacy-protection domain adaptation for imbalanced ECG beat classification

Computer aided diagnosis (CAD) systems based on ECG signals have become indispensable tools in the automatic detection of Arrhythmia, significantly reducing human effort. The rapid advancement in deep learning has ushered in a new era of such systems, showcasing promising results in ECG beat classifications. However, these systems grapple with domain shifts across different patients. Although Unsupervised Domain Adaptation (UDA) methods have shown potential in mitigating these shifts, they necessitate access to the source domain data, which poses a problem as ECG signals often contain sensitive patient information. This makes the need to enhance the performance of ECG-based arrhythmia detection CAD systems, while simultaneously respecting patient privacy, a pressing concern in clinical settings. Recently, source free domain adaptation (SFDA) methods, which exclusively use pre-trained models, have emerged as a solution to this privacy issue. Nevertheless, previous SFDA methods tend to overlook the problem of class imbalance in this setting. In response, a Target-oriented Augmentation Privacy-protection Domain Adaptation (TAPDA) framework has been developed. This method introduces a class-balance pseudo-label strategy, which selects an equal proportion of confident samples from each category. Data augmentation techniques are then applied to counteract class imbalance issues. The augmented data is provided with pseudo-labels. The selected and augmented data is used to fine-tune the pre-trained model. Then a two-step self-training process is employed to extract target-specific knowledge from the pseudo-label dataset. Numerical experiments confirm the effectiveness of our proposed method, surpassing other state-of-the-art SFDA methods.

An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention.

The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia based on ECG plays a critical role in the early prevention and diagnosis of CVDs. In recent years, numerous studies have focused on using deep learning methods to address arrhythmia classification problems. However, the transformer-based neural network in current research still has a limited performance in detecting arrhythmias for the multi-lead ECG. In this study, we propose an end-to-end multi-label arrhythmia classification model for the 12-lead ECG with varied-length recordings. Our model, called CNN-DVIT, is based on a combination of convolutional neural networks (CNNs) with depthwise separable convolution, and a vision transformer structure with deformable attention. Specifically, we introduce the spatial pyramid pooling layer to accept varied-length ECG signals. Experimental results show that our model achieved an F1 score of 82.9% in CPSC-2018. Notably, our CNN-DVIT outperforms the latest transformer-based ECG classification algorithms. Furthermore, ablation experiments reveal that the deformable multi-head attention and depthwise separable convolution are both efficient in extracting features from multi-lead ECG signals for diagnosis. The CNN-DVIT achieved good performance for the automatic arrhythmia detection of ECG signals. This indicates that our research can assist doctors in clinical ECG analysis, providing important support for the diagnosis of arrhythmia and contributing to the development of computer-aided diagnosis technology.

Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram.

Cardiovascular disease is one of the main causes of death worldwide. Arrhythmias are an important group of cardiovascular diseases. The standard 12-lead electrocardiogram signals are an important tool for diagnosing arrhythmias. Although 12-lead electrocardiogram signals provide more comprehensive arrhythmia information than single-lead electrocardiogram signals, it is difficult to effectively fuse information between different leads. In addition, most of the current researches working on automatic diagnosis of cardiac arrhythmias are based on modeling and analysis of single-mode features extracted from one-dimensional electrocardiogram sequences, ignoring the frequency domain features of electrocardiogram signals. Therefore, developing an automatic arrhythmia detection algorithm based on 12-lead electrocardiogram with high accuracy and strong generalization ability is still challenging. In this paper, a multimodal feature fusion model based on the mechanism is developed. This model utilizes a dual channel deep neural network to extract different dimensional features from one-dimensional and two-dimensional electrocardiogram time-frequency maps, and combines attention mechanism to effectively fuse the important features of 12-lead, thereby obtaining richer arrhythmia information and ultimately achieving accurate classification of nine types of arrhythmia signals. This study used electrocardiogram signals from a mixed dataset to train, validate, and evaluate the model, with an average of F1 score and average accuracy reached 0.85 and 0.97, respectively. Experimental results show that our algorithm has stable and reliable performance, so it is expected to have good practical application potential.

Real-time Automatic Arrhythmia Detection System based on Extreme Gradient Boosting and Neural Network Algorithm

Arrhythmia and other diseases are puzzling more and more people. Accurate detection is the key to realizing intelligent diagnosis of electrocardiogram(ECG) monitoring systems. It can prevent heart disease and effectively reduce mortality. An efficient and accurate arrhythmia detection method is urgent. In this work, a real-time automatic arrhythmia detection technology based on extreme gradient boosting (XGboost) and convolutional neural network (CNN) algorithm were developed. First, ECG signals in the MIT-BIH Arrhythmia database are preprocessed: 1) EMG interference filtering; 2) Power frequency interference suppression; 3) Baseline drift correction. Secondly, We use the cyclic singular spectrum (CISSA) algorithm to decompose the ECG after pretreatment. From the original ECG and the 7 simple signals obtained from decomposition, 23 features about the time domain, frequency domain, nonlinear dynamics and statistics are extracted respectively. Finally, XGboost and CNN algorithms are used to build a classification model, and the extracted features are classified, trained and recognized to achieve automatic detection of arrhythmia. The experimental results show that XGboost and CNN algorithms can automatically detect 98.40%, 95.65% and 97.60%, 95.12% of Category 2 and Category 4 arrhythmias, respectively.

Two-Stage Classifier for Resource Constrained On-Board Cardiac Arrhythmia Detection

Early automatic detection of cardiac arrhythmia in an ambulatory health monitoring setup can reduce the mortality rates. In the existing approaches on multiclass arrhythmia detection (MCAD) techniques, adopting either time-domain morphological features (TDMFs), fails to extract hidden pattern from the single-lead electrocardiogram (ECG) resulting in poor accuracy (Ac), or adopting deep-learning (DL) techniques becomes too complex or heavy in architecture, thus hindering their easy implementation in low resource architecture for wearable applications. This work presents a judicious hybridization of both approaches in an end-to-end MCAD using two-stage classifier to achieve higher Ac and low latency while detecting 15 classes of cardiac rhythms [14 types of arrhythmia beats or normal sinus rhythms (NSRs)]. The memory resource saving is achieved through the beat skeletonization process and two-stage feature minimization. The stage#1 classifier uses 14 TDMF features fed to a random forest (RF) regressor and provides probability values for NSR, atrial, or ventricular conduction problem. The stage#2 classifier, another RF, accepts stacked autoencoder (SAE) features, TDMF features, and output of stage#1 to achieve a blind test Ac of 99.21% using only 13 features while evaluated with full Massachusetts Institute of Technology-Beth Israel (MIT-BIH) arrhythmia database (mitdb) records. A stand-alone on-board application of the two-stage MCAD using ARMv6 single-core microcontroller yields a detection latency of 782 ms and consumes 390 kB of memory per cardiac cycle, thus proving a feasible solution for wearable cardiac monitors. It also provides the competitive performance with the State of the art (SOTA) methods and outperforms the limited available research on hardware implementation in terms of number of classes handled and Ac.

Simple, efficient, and generalized ECG signal quality assessment method for telemedicine applications

Electrocardiogram (ECG) signals recorded by paramedics in an unsupervised environment are prone to errors and noise due to factors such as electrode misplacements and daily activities during prolonged monitoring. Signal Quality Assessment (SQA) systems can prevent false diagnoses by automated arrhythmia detection systems and reduce the workload of cardiologists. However, current SQA methods are quite complex and/or exhibit poor performances in presence of certain arrhythmias. This study proposed a novel SQA method consisting of three main steps: (1) feasibility conditions check, (2) average beat correlation algorithm, and (3) beat clustering algorithm. The system uses empirical probability functions to automatically adapt the quality score according to the presence of pathological beats and/or noises. We evaluated the proposed method on three datasets and considered both three-quality classes ('Bad', 'HR' and 'Diagnostic') and two-quality classes ('Acceptable' and 'Unacceptable'). For the three-class classification, we obtained an average accuracy of 81.42 % while for the two-class, our algorithm achieved an average sensitivity of 94.59 %, a specificity of 98.38 %, and an accuracy of 97.10 %. The results show that our method achieves high accuracy, specificity and generalizability, outperforming the simple heuristic rule, machine learning, and deep learning methods. Additionally, the algorithm requires less computational time, making it suitable for telemedicine applications.

A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network

Electrocardiogram (ECG) signal is informative as well as non-invasive clinical tool to diagnose cardiac diseases of human heart. However, the diagnosis requires professionals’ clarification and is also time-consuming. To make the diagnosis proficient, a novel convolutional neural network (CNN) has been proposed for automatic arrhythmia detection. In this work, the ECG data collected from the MIT-BIH database have been preprocessed, and segmented in short ECG segments of 60 s. Then, all these segments have been transformed into scalogram images obtained from time-frequency analysis using continuous wavelet transform (CWT). Finally, these scalogram images have been exploited as an input for our designed CNN classifier to classify cardiac arrhythmia. In this approach, the overall accuracy, sensitivity, and specificity are 99.39%, 98.79%, and 100% respectively. Proposed CNN model has significant advantages, and it can be used to differentiate the healthy and arrhythmic patients effectively.

A novel deep neural network heartbeats classifier for heart health monitoring

The electrocardiogram (ECG) is a very useful diagnostic tool to examine the functioning of the heart and to detect myocardial infarction (MI) and arrhythmias. It contains the records of the electrical signal of the heart and it is an investigation tool to check the heart's rhythm and thereby analyze heartbeats. Automatic detection of arrhythmia is possible by analyzing a patient's abnormal heartbeats and has become a major research area in recent years, as the manual examination of heart activity is time-consuming and prone to errors. Nowadays, the deployment of artificial intelligence (AI) - based algorithms to predict abnormal heartbeats categorized into five classes namely, non-ectopic (N), supra ventricular ectopic (S), ventricular ectopic (V), fusion (F) and unknown beats (Q) has drawn more attention in detecting arrhythmias. The use of intuitive hand-crafted features with shallow feature learning architectures is one of the key drawbacks of machine learning (ML) techniques. So, we present a novel deep neural network heartbeat classifier to extract and classify the heartbeat signals. The novel one-dimensional convolution neural network (1D CNN) model is developed by modifying the LENET architecture for the classification of heat beats (MIT-BIH Arrhythmia Database) and has attained an accuracy of 97.37%. This model's performance is also enhanced by the implementation of smote oversampling technique and gained an accuracy of 98.41%. Finally, the proposed model's performance is compared with other pre-existing models and various oversampling methods are deployed for analysis.

An end-to-end model for ECG signals classification based on residual attention network

Arrhythmia is the most threatening disease among cardiovascular diseases, and in the last few years, the automatic detection of arrhythmia using neural networks have been intensely focused by physicians. In our work, we propose an effective method to automatically classify electrocardiogram (ECG) signals utilizing residual attention network (RA-NET). RA-NET combines the residual structure and attention mechanism, which can not only generate the attention weight of atrial fibrillation (AF) category to enhance the effective information, but also avoid the network degradation problem in the deep network. Besides, a novel filling algorithm for filling sample values of other recordings with the same category is presented, which is combined with RA-NET to validate model on the PhysioNet Challenge 2017 dataset. According to the comparison with other relevant classification models and filling methods, the experimental results demonstrate that the model we proposed achieves an excellent classification performance, the average of F1-score and sensitivity reach 0.8289 and 0.8955, respectively. For AF category, the precision, F1-score and specificity achieve 0.8763, 0.8835 and 0.9858, separately. With its preeminent performance, the proposed model is capable to play an important auxiliary role in single-lead AF detection.