Atrial Fibrillation Detection Algorithm Based on Graph Convolution Network

Ma, Hua; Xia, Lingnan

doi:10.1109/access.2023.3291352

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…The majority of AF detection models are based on DL and ECG data (Figure 2). Convolutional neural networks (CNNs) are commonly employed to extract features as well as perform classification from original ECG signal data (Biton et al, 2023; Christopoulos et al, 2022; Fernández‐Ruiz, 2019; Lai et al, 2020; Ma & Xia, 2023; Mittal et al, 2021; Mousavi et al, 2021; Orchard et al, 2016; Rahul & Sharma, 2022; Seo et al, 2021; M. U. Yang et al, 2022). In their model for detecting paroxysmal AF, Pourbabaee et al (2018) applied deep CNNs on raw ECG time‐series signals to learn representative features in the time‐domain using only one fully connected layer.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

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Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence

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