Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Rodrigo, Miguel; Alhusseini, Mahmood; Rogers, Albert J.; Krittanawong, Chayakrit; Thakur, Sumiran; Feng, Ruibin; Ganesan, Prasanth; Narayan, Sanjiv M.

doi:10.1016/j.compbiomed.2022.105451

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…Rodrigo et al created a deep learning algorithm to distinguish AF from other tachycardias based on intracardiac electrogram (EGM) features. 46 This deep learning algorithm demonstrated excellent performance, with an AUC of 0.95-0.97, depending on unipolar or bipolar EGM, compared with traditional single EGM features, which demonstrated an AUC of 0.67-0.75. These results support the continued evaluation of deep learning as a tool to better identify AF from other arrhythmias using EGMs from cardiac implantable electronic devices.…”

Section: Artificial Intelligence For Af Using Intracardiac Signalsmentioning

confidence: 87%

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

Harmon

Sehrawat

Maanja

et al. 2023

Arrhythm Electrophysiol Rev

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AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.

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Section: Artificial Intelligence For Af Using Intracardiac Signalsmentioning

confidence: 87%

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

Harmon

Sehrawat

Maanja

et al. 2023

Arrhythm Electrophysiol Rev

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“…The dataset utilized in this study was generously provided by Rodrigo et al in (2022). This dataset consisted of a meticulously curated patient cohort sourced from the COMPARE registry (ClinicalTrials.gov Identifier: NCT02997254), comprising individuals diagnosed with AF who were prospectively enrolled during ablation procedures for symptomatic AF unresponsive to at least one anti-arrhythmic medication.…”

Section: Methodsmentioning

confidence: 99%

“…The dataset utilized in this study was a subset of the dataset originally provided by Rodrigo et al (2022). Specifically, the analysis included N=2,817 EGM signals, with N=1,738 classified as AF and N=1,079 as AT.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Patient Weight from Intracardiac Electrograms: A Study in Electrophysiological Signal Analysis

Alagöz

2024

Preprint

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The analysis of electrophysiological signals from the human body has become increasingly crucial, especially given the widespread adoption of wearable technologies and the growing trend of remote and online monitoring. In situations where demographic patient data is unavailable, the evaluation of such information from electrophysiological signals becomes imperative for making well-informed diagnostic and therapeutic decisions, particularly in ambulatory and urgent cases. This study underscores the significance of this necessity by utilizing intracardiac electrograms to predict patient weight.Intracardiac electrograms were recorded from 44 patients (14 female, with an average age of 59.2±11.5 years) using a 64-pole basket catheter over a duration of 60 seconds. A dataset comprising 2,816 unipolar electrogram signal segments, each lasting 4 seconds, was utilized. Weight, considered as a continuous variable, underwent discretization into k bins with uniformly distributed widths, where various values of k were experimented with. As the value of k increases, class imbalance also increases.The state-of-the-art time series classification algorithm, Minirocket, was employed alongside the popular machine learning algorithm eXtreme Gradient Boosting (XGBoost). Minirocket consistently demonstrates superior performance compared to XGBoost across all class number scenarios and across all evaluation metrics, such as accuracy, F1 score, and Area Under the Curve (AUC) values, achieving scores of approximately 0.96. Conversely, XGBoost shows signs of overfitting, particularly noticeable in scenarios with higher class imbalance. Tuning probability thresholds for classes could potentially mitigate this issue. Additionally, XGBoost’s performance improves with reduced bin numbers, emphasizing the importance of balanced classes. This study provides novel insights into the predictive capabilities of these algorithms and their implications for personalized medicine and remote health monitoring.

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“…Furthermore, Luongo et al have applied machine learning to predict AF ablation targets, but used 12-lead ECG data instead of medical imaging ( Luongo et al, 2021 ). Other studies have also leveraged the power of AI in AF by using DL with ECG data to estimate atrial fibrosis and to classify AF from atrial flutter or tachycardia ( Nagel et al, 2021 ; Rodrigo et al, 2022 ). Zololotarev et al applied AI to identify AF drivers from multi-electrode mapping, with the AI model then validated using optical mapping; the results were comparable to the state-of-the-art with higher computational efficiency ( Zolotarev et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation

Ogbomo-Harmitt

Muffoletto²,

Zeidan³

et al. 2023

Front. Physiol.

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Background: Radiofrequency catheter ablation (RFCA) therapy is the first-line treatment for atrial fibrillation (AF), the most common type of cardiac arrhythmia globally. However, the procedure currently has low success rates in dealing with persistent AF, with a reoccurrence rate of ∼50% post-ablation. Therefore, deep learning (DL) has increasingly been applied to improve RFCA treatment for AF. However, for a clinician to trust the prediction of a DL model, its decision process needs to be interpretable and have biomedical relevance.Aim: This study explores interpretability in DL prediction of successful RFCA therapy for AF and evaluates if pro-arrhythmogenic regions in the left atrium (LA) were used in its decision process.Methods: AF and its termination by RFCA have been simulated in MRI-derived 2D LA tissue models with segmented fibrotic regions (n = 187). Three ablation strategies were applied for each LA model: pulmonary vein isolation (PVI), fibrosis-based ablation (FIBRO) and a rotor-based ablation (ROTOR). The DL model was trained to predict the success of each RFCA strategy for each LA model. Three feature attribution (FA) map methods were then used to investigate interpretability of the DL model: GradCAM, Occlusions and LIME.Results: The developed DL model had an AUC (area under the receiver operating characteristic curve) of 0.78 ± 0.04 for predicting the success of the PVI strategy, 0.92 ± 0.02 for FIBRO and 0.77 ± 0.02 for ROTOR. GradCAM had the highest percentage of informative regions in the FA maps (62% for FIBRO and 71% for ROTOR) that coincided with the successful RFCA lesions known from the 2D LA simulations, but unseen by the DL model. Moreover, GradCAM had the smallest coincidence of informative regions of the FA maps with non-arrhythmogenic regions (25% for FIBRO and 27% for ROTOR).Conclusion: The most informative regions of the FA maps coincided with pro-arrhythmogenic regions, suggesting that the DL model leveraged structural features of MRI images to identify such regions and make its prediction. In the future, this technique could provide a clinician with a trustworthy decision support tool.

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Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Cited by 7 publications

References 19 publications

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

Predicting Patient Weight from Intracardiac Electrograms: A Study in Electrophysiological Signal Analysis

Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation

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