A novel IRBF-RVM model for diagnosis of atrial fibrillation

Kong, Dongdong; Zhu, Junjiang; Wu, Shangshi; Duan, Chaoqun; Lu, Lixin; Chen, Dongxing

doi:10.1016/j.cmpb.2019.05.028

Cited by 27 publications

(6 citation statements)

References 25 publications

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“…Kong et al [ 126 ] proposed a machine learning method for rapid modeling and accurate diagnosis of atrial fibrillation. For this study, the electrical activity of the whole heart of the patients with atrial fibrillation and synchronous 12-Lead ECG signals was collected from atrial fibrillation patients and healthy people.…”

Section: Resultsmentioning

confidence: 99%

“… The overall performances did not compete with related work on neural network for premature ventricular contractions detection reaching accuracy of 98 and 99%. Kong et al [ 126 ] The results demonstrated that the predictive performance of the proposed method was comparable to SVM, and the RVM was more suitable for online diagnosis since RVM was sparser than SVM. The proposed method had a better performance than other methods.…”

Section: Discussionmentioning

confidence: 99%

“… ECG sensors LSTM Patient-independent Arrhythmia Hoang et al [ 125 ] 2019 Hsinchu (Taiwan) Conference Proceedings 32 subjects (75 records) from Physionet St. Petersburg Institute of Cardiological Technics [ 110 ] A multi-leads ECG premature ventricular contraction detection method using tensor decomposition and Convolutional Neural Network was proposed. 6-Lead ECG, 12-Lead ECG CNN Patient-specific Premature Ventricular Contraction Kong et al [ 126 ] 2019 Shanghai (China) Journal article 1056 AF patients and 904 healthy people In this study, an improved machine learning method was proposed for rapid modeling and accurate diagnosis of AF. Inno-12-U ECG ML, SVM Patient-specific Atrial Fibrillation Mahmood et al [ 127 ] 2019 Khartoum (Sudan) Conference Proceedings Physionet Computing in Cardiology Challenge 2017 dataset [ 128 ] Comparison study with the purpose to propose an approach for selecting the best classifier for diagnoses of atrial fibrillation (AF) in coronary heartbeats.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Denysyuk¹,

Pinto²,

Silva³

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Denysyuk¹,

Pinto²,

Silva³

et al. 2023

Heliyon

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“…Faust et al (2018) applied a long short-term memory network with RR interval signals for automated detection of AF. Kong et al (2019) constructed an AF classification method based on deep CNNs using a waveform dataset and assessed its efficiency and generalizability on different devices and data sources. Mannhart et al (2023) compared the performance of manufacturers' algorithms and a DL-based algorithm for AF detection on single-lead ECG from five smartwatches.…”

Section: Atrial Fibrillation Detection With Deep Learningmentioning

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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“…Various intelligent methods have seen substantial progress in ECG signals recognition and some of them are machine learning (ML) approaches [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An improved SR-LSTM network for atrial fibrillation and atrial flutter signals discrimination

Wang,

Guo

2024

Preprint

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Atrial fibrillation (AF) and atrial flutter (AFL) are the most common and easily confused arrhythmias. However, the existing AF and AFL detection approaches depend on visual examination of electrocardiogram (ECG), which is taxing and time-consuming. Motivated by state refinement module for LSTM (SR-LSTM), we investigate an improved SR-LSTM (ISR-LSTM) that is capable of adaptively refining the current states of sample points in ECG leveraging a sample point-wise attention and a representation gate mechanism for automated AF and AFL signals detection. To confirm the effectiveness, we conducted ablation experiments across two public databases. The results demonstrate the effectiveness of ISR-LSTM while achieving a significant improvement than state-of-the-art algorithms with an accuracy of 99.2% and 98.5%. To our knowledge, ISR-LSTM is the first work that integrates the interaction in ECG into deep network frame providing interpretability.

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A novel IRBF-RVM model for diagnosis of atrial fibrillation

Cited by 27 publications

References 25 publications

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

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

An improved SR-LSTM network for atrial fibrillation and atrial flutter signals discrimination

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