The influence of atrial flutter in automated detection of atrial arrhythmias - are we ready to go into clinical practice?”

Domazetoski, Viktor; Gligorić, Goran; Marinković, Milan; Shvilkin, Alexei; Krsic, Jelena; Kocarev, Ljupčo; Ivanović, Milan

doi:10.1016/j.cmpb.2022.106901

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…Most ML models used ECG input signal for AF detection (Agliari et al, 2020; Alhusseini et al, 2020; Buscema et al, 2020; Domazetoski et al, 2022; K. He et al, 2022; Huang et al, 2021; Nickelsen et al, 2017; Pérez‐Valero et al, 2019; Rad et al, 2021; Rouhi et al, 2021; Schaefer et al, 2014; Wesselius et al, 2022; Yao et al, 2021). Abdul‐Kadir et al (2016) developed an AF recognition method based on dynamic ECG features extracted using the concept of a second‐order dynamic system.…”

Section: Resultsmentioning

confidence: 99%

Section: Atrial Fibrillation Detection With Machine Learningmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Atrial Fibrillation Detection With Machine 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

View full text Add to dashboard Cite

show abstract

“…Several studies have proposed models for the diagnosis of arrhythmias using Chapman data also used in the present study. The accuracy of DNN [ 43 ], CNN Trees [ 44 ], Meta CNN Trees [ 45 ], Single Classifier [ 46 ], 1-D CNN [ 47 ], RNN [ 48 ], XGBoost [ 49 ], Teacher and Student [ 50 ] models was 92.24%, 97.60%, 98.29%, 92.89%, 94.01%, 96.21%, 89.40%, 98.96%, and 98.13%, respectively. While our proposed GCN-MI had a higher accuracy (99.71%) this may be due to the consideration of the structure of leads.…”

Section: Discussionmentioning

confidence: 99%

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals

Andayeshgar

Abdali-Mohammadi

Sepahvand

et al. 2022

IJERPH

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Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) benefitting from mutual information (MI) indices extracted from the ECG leads. In this research, for the first time, the relationships of 12 ECG leads measured using MI as an adjacency matrix were illustrated by the developed GCN and included in the ECG-based diagnostic method. Cross-validation methods were applied to select both training and testing groups. The proposed methodology was validated in practice by applying it to the large ECG database, recently published by Chapman University. The GCN-MI structure with 15 layers was selected as the best model for the selected database, which illustrates a very high accuracy in classifying different types of rhythms. The classification indicators of sensitivity, precision, specificity, and accuracy for classifying heart rhythm type, using GCN-MI, were computed as 98.45%, 97.89%, 99.85%, and 99.71%, respectively. The results of the present study and its comparison with other studies showed that considering the MI index to measure the relationship between cardiac leads has led to the improvement of GCN performance for detecting and classifying the type of arrhythmias, in comparison to the existing methods. For example, the above classification indicators for the GCN with the identity adjacency matrix (or GCN-Id) were reported to be 68.24%, 72.83%, 95.24%, and 92.68%, respectively.

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Automated atrial arrhythmia classification using 1D-CNN-BiLSTM: A deep network ensemble model

Prasanna Venkatesh,

Pradeep Kumar,

Chakravarthy Neelapu

et al. 2024

Biomedical Signal Processing and Control

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The influence of atrial flutter in automated detection of atrial arrhythmias - are we ready to go into clinical practice?”

Cited by 4 publications

References 42 publications

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

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

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals

Automated atrial arrhythmia classification using 1D-CNN-BiLSTM: A deep network ensemble model

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