ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions

Bachi, Lorenzo; Halvaei, Hesam; Pérez, Cristina Fernández; Martín-Yebra, Alba; Petrėnas, Andrius; Sološenko, Andrius; Johnson, Lorraine; Marozas, Vaidotas; Martínez, Juan Pablo; Pueyo, Esther; Stridh, Martin; Laguna, Pablo; Sörnmo, Leif

doi:10.1109/tbme.2023.3288701

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…To improve the sensitivity without decreasing the PPV calls for a structural change of the detector, for example, by using deep learning to identify SBSs directly from the raw ECG. Another possibility could be to use another database better suited for training, either created by the more advanced, recently developed ECG simulator described in [33] or composed of real ECGs; however, as already noted, annotated public databases are unfortunately lacking. To improve the PPV without decreasing the sensitivity, the approach presented in [32] may be used to differentiate nsSVT from PACs.…”

Section: Discussionmentioning

confidence: 99%

Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening

Halvaei,

Hygrell,

Svennberg

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT. Methods: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable. Results: The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. Conclusion:The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector.Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.

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Section: Discussionmentioning

confidence: 99%

Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening

Halvaei,

Hygrell,

Svennberg

et al. 2024

IEEE J. Transl. Eng. Health Med.

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“…Noseworthy et al (2022) and Golovchiner et al (2022) combined ML and DL in their models, which detected AF using vocal features analysis, and attained high accuracy and reliability in identifying AF episodes. Bachi et al (2023) developed an ECG simulator with time-varying signal characteristics for modeling of arrhythmias, noise, and other factors relevant to AF analysis. The study demonstrated the usefulness of the simulator in data augmentation for AI models, as well as in evaluating AF detection performance.…”

Section: Atrial Fibrillation Detection With Hybrid Methodsmentioning

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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Efficient electrocardiogram generation based on cardiac electric vector simulation model

Que,

Bian,

Chen

et al. 2024

Computers in Biology and Medicine

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ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions

Cited by 4 publications

References 46 publications

Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening

Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening

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

Efficient electrocardiogram generation based on cardiac electric vector simulation model

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