Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN

Wang, Mou; Rahardja, Sylwan; Fränti, Pasi; Rahardja, Susanto

doi:10.1016/j.bspc.2022.104067

Cited by 18 publications

(9 citation statements)

References 35 publications

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“…Different label classifications comprising 41, 20, and 5 classes are also reported in Feyisa et al 46 with the use of PTB-XL dataset which is a 12-lead database with various types of arrhythmia. Wang et al 48 used the CinC-2017 database and applied a data augmentation with the Mix-Up operation in the training stage to reduce the data imbalance and thus the overfitting; the method generates more training data without extra computational resources.…”

Section: Methodsmentioning

confidence: 99%

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

Boulif

Ananou

Ouladsine

et al. 2023

Bioinform Biol Insights

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In the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia’s occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis.

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Section: Methodsmentioning

confidence: 99%

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

Boulif

Ananou

Ouladsine

et al. 2023

Bioinform Biol Insights

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“…Faust et al Faust et al (2018) utilized RNNs, specifically the long short-term memory (LSTM) architecture, to analyze ECGs from the MIT-BIH Atrial Fibrillation Database, achieving an accuracy rate of 99.77% for AF detection. Wang et al Wang et al (2023a) proposed a dual-path RNN which includes the intra- and inter-RNN modules to study the global and local aspects for end-to-end AF recognition. They used the PhysioNet/Cinc 2017 dataset to validate their model and achieved an F1 score of 0.842.…”

Section: Research Backgroundmentioning

confidence: 99%

Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet

Xie,

Stavrakis,

Yao

2024

Front. Physiol.

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Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes. Moreover, human error is inevitable, as even experienced medical professionals can overlook or misinterpret subtle signs of AF. As such, it is of critical importance to develop an advanced analytical model that can automatically interpret ECG signals and provide decision support for AF diagnostics.Methods: In this paper, we propose an innovative deep-learning method for automated AF identification using single-lead ECGs. We first extract time-frequency features from ECG signals using continuous wavelet transform (CWT). Second, the convolutional neural networks enhanced with residual learning (ReNet) are employed as the functional approximator to interpret the time-frequency features extracted by CWT. Third, we propose to incorporate a multi-branching structure into the ResNet to address the issue of class imbalance, where normal ECGs significantly outnumber instances of AF in ECG datasets.Results and Discussion: We evaluate the proposed Multi-branching Resnet with CWT (CWT-MB-Resnet) with two ECG datasets, i.e., PhysioNet/CinC challenge 2017 and ECGs obtained from the University of Oklahoma Health Sciences Center (OUHSC). The proposed CWT-MB-Resnet demonstrates robust prediction performance, achieving an F1 score of 0.8865 for the PhysioNet dataset and 0.7369 for the OUHSC dataset. The experimental results signify the model’s superior capability in balancing precision and recall, which is a desired attribute for ensuring reliable medical diagnoses.

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“…BiLSTM is able to further processed the results of the CNN, and the time features of experimental data are extracted with the memory function. BiLSTM is developed from the Recurrent neural network (RNN) [ 35 ], which can enter data into the network for calculation at every time point, and every hidden layer sends its output immediately to the following time point and the next layer of the network. Its structure is shown in Fig 3 .…”

Section: Correlation Theorymentioning

confidence: 99%

“…There are four gated units in each LSTM computing unit, they are input gate ( i t ), output gate( o t ), control gate( c t )and forget gate( f t ), and M is the network module. The relationship between the various gates are as follow [ 34 , 35 ]:…”

Section: Correlation Theorymentioning

confidence: 99%

The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system

Su,

Ma,

2024

PLoS ONE

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In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it’s effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries.

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Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN

Cited by 18 publications

References 35 publications

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet

The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system

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