scholarly journals Identifying patients with atrial fibrillation during sinus rhythm on ECG: confirming the utility of artificial intelligence algorithm in a small-scale cohort without structural heart diseases

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
S Suzuki ◽  
J Motogi ◽  
W Matsuzawa ◽  
T Takayanagi ◽  
T Umemoto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Time Course ◽  
Heart Diseases ◽  
Validation Dataset ◽  
Small Scale ◽  
Internal Validation ◽  
Testing Dataset ◽  
Structural Heart Diseases

Abstract Background Detection of atrial fibrillation (AF) out of electrocardiograph (ECG) on sinus rhythm (SR) using artificial intelligence (AI) algorithm has been widely studied within recent couple of years. Generally, it is believed that a huge number of ECGs are necessary for developing an AI-enabled ECG to be adequate to correspond to a lot of minor variations of ECGs. For example, structural heart diseases have typical ECG characteristics, but they could be a noise for the purpose of detecting the small signs of electrocardiographic signature of AF. We hypothesized that when patients with structural heart diseases are excluded, AI-enabled ECG for identifying patients with AF can be developed with a small number of ECGs. Methods We developed an AI-enabled ECG using a convolutional neural network to detect the electrocardiographic signature of AF present during normal sinus rhythm (NSR) using a digital, standard 10-second, 12-lead ECGs. We included all patients who newly visited the Cardiovascular Institute with at least one NSR ECG between Feb 1, 2010, and March 31, 2018. We classified patients with at least one ECG with a rhythm of AF as positive for AF (AF label) and others as negative for AF (SR label). We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operating characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the sensitivity, specificity, F1 score, and accuracy with two-sided 95% confidence intervals (CIs). Results We totally included 19170 patients with 12-lead ECG. After excluding patients with structural heart diseases, 12825 patients with NSR ECGs at the initial visit were identified (1262 were clinically diagnosed as AF anytime during the time course and 11563 were never diagnosed as AF). Of 11563 non-AF patients, 1818 patients who were followed over 1095 days were selected for the analysis with the SR label, to secure the robustness for maintaining SR. Of 1262 AF patients, 251 patients were selected for the analysis with the AF label, of whom a NSR ECG within 31 days before or after the index AF ECG (the first AF ECG during the time course) could be obtained. In the patients with AF label, the NSR ECG of which the date was the nearest to the index AF ECG was selected for the analysis. The AI-enabled ECG showed an AUC of 0.88 (0.84–0.92) with sensitivity 81% (72–88), specificity 80% (77–83), F1 score 50% (43–57), and overall accuracy 80% (78–83). Conclusion An AI-enabled ECG acquired during NSR allowed identification of patients with AF in a small population without structural heart diseases. FUNDunding Acknowledgement Type of funding sources: None.

scholarly journals Prediction of atrial fibrillation from normal ECG using artificial intelligence in patients with unexplained stroke

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
Y.S Baek ◽  
S.C Lee ◽  
W.I Choi ◽  
D.H Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Statistical Significance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Normal Sinus ◽  
Group 2

Abstract Background Stroke related to embolic and of undetermined source constitute 20 to 30% of ischemic strokes. Many of these strokes are related to atrial fibrillation (AF), which might be underdetected due to its paroxysmal and silent nature. Purpose The aim of our study was to predict AF during normal sinus rhythm in a standard 12-lead ECG to train an artificial intelligence to train deep neural network in patients with unexplained stroke (embolic stroke of undetermined source; ESUS). Methods We analyzed digital raw data of 12-lead ECGs using artificial intelligence (AI) recurrent neural network (RNN) to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 12-lead ECGs. We included 2,585 cases aged 18 years or older with multiple ECGs at our university hospital between 2005 and 2017 validated by crossover analysis of two electrophysiologists. We defined the first recorded AF ECG as the index ECG and the first day of the window of interest as 14 days before the date of the index ECG. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated recall, F1 score, and the area under the curve (AUC) of the receiver operatoring characteristic curve (ROC) for the internal validation dataset to select a probability threshold. We applied this developed AI program to 169 ESUS patients who has been diagnosed and had standard 12-lead ECGs in our hospital. Results We acquired 1,266 NSR ECSs from real normal subjects and 1,319 NSR ECGs form paroxysmal AF patients. RNN AI-enabled ECG identified atrial fibrillation with an AUC of 0.79, recall of 82%, specificity of 78%, F1 score of 75% and overall accuracy of 72.8% (Figure). ESUS patients were divided into three groups according to calculated probabilities of AF using AI guided RNN program: group 1 (35 patients with probability of 0–25% of paroxysmal AF), group 2 (86 patients with probability of 25–75% of paroxysmal AF) and group 3 (48 patients with probability of 75–100% of paroxysmal AF). In Kaplan-Meier estimates, Group 2 and 3 (more than 25% of PAF probabilities) tended to have higher AF incidence although it did not reach statistical significance (log-rank p 0.678) (Figure). Conclusion AI may discriminate subtle changes between real and paroxysmal NSR and can also be helpful in patients with ESUS to identify if AF is the underlying cause of the stroke. Further studies are needed in order to evaluate their possible use in future prognostic models. Funding Acknowledgement Type of funding source: None

Atrial Appendage Transcriptional Profile in Patients with Atrial Fibrillation with Structural Heart Diseases

2006 ◽  
Vol 1091 (1) ◽  
pp. 205-217 ◽  
Author(s):  
MARIA S. KHARLAP ◽  
ANGELICA V. TIMOFEEVA ◽  
LUDMILA E. GORYUNOVA ◽  
GEORGE L. KHASPEKOV ◽  
SERGEY L. DZEMESHKEVICH ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Diseases ◽  
Transcriptional Profile ◽  
Atrial Appendage ◽  
Structural Heart Diseases

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

The Lancet ◽  
2019 ◽  
Vol 394 (10201) ◽  
pp. 861-867 ◽  
Author(s):  
Zachi I Attia ◽  
Peter A Noseworthy ◽  
Francisco Lopez-Jimenez ◽  
Samuel J Asirvatham ◽  
Abhishek J Deshmukh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Retrospective Analysis ◽  
Outcome Prediction

Inherited Structural Heart Diseases With Potential Atrial Fibrillation Occurrence

2015 ◽  
Vol 27 (2) ◽  
pp. 242-252 ◽  
Author(s):  
ROBERTA MANUGUERRA ◽  
SERGIO CALLEGARI ◽  
DOMENICO CORRADI
Keyword(s):  
Atrial Fibrillation ◽  
Heart Diseases ◽  
Structural Heart Diseases

Abstract 13747: Investigation of the Probability of Spontaneous Conversion From Atrial Fibrillation to Sinus Rhythm Utilizing Continuous Rhythm Monitoring

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Paul D Ziegler ◽  
Efstratios I Charitos
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Time Course ◽  
Hierarchical Regression ◽  
Related Factors ◽  
Blue Line ◽  
Spontaneous Conversion ◽  
Af Recurrence ◽  
Red Line

Introduction: Atrial fibrillation (AF) recurrence or spontaneous conversion to sinus rhythm (SR) are regarded as random events. Hypothesis: We hypothesized that the probability of spontaneous conversion to SR decreases as more time is spent in AF. We attempt to quantify this phenomenon and investigate factors that can affect this probability. Methods: Cardiac rhythm histories of 1195 patients (age 73.0 ± 10.1, follow-up: 349 ± 40 days; 14533 AF episodes) with implantable devices were reconstructed and analyzed. No patients received cardioversion, AF ablation, or any obvious AF therapies during follow-up. Patients with no AF recurrence or continuous AF during follow up were excluded. Hierarchical regression methods were employed to investigate the time course of the probability of rhythm change and factors that influence it. Results: Probabilities of spontaneous conversion from AF to SR (solid blue line) and recurrence of AF in patients with SR (solid red line) are shown in the Figure. For patients in AF , spontaneous conversion probability significantly decreases with time spent in AF and plateaus after ~7 days (dotted blue line). Similarly for patients in SR, increasing time in SR reduces the probability of developing AF (solid red line) and plateaus after ~7 days (dotted red line). Patient age (p<0.001), LVEF (p<0.05) and presence of coronary artery disease (p<0.01) significantly influence the spontaneous conversion probabilities independent from AF burden. Conclusions: Spontaneous SR conversion or AF recurrence diminishes with increasing time spent in AF or SR, respectively, and are influenced by several patient-related factors. These findings suggest that patients should be closely monitored after AF recurrence or SR conversion.

scholarly journals A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong-Soo Baek ◽  
Sang-Chul Lee ◽  
Wonik Choi ◽  
Dae-Hyeok Kim
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Sinus Rhythm ◽  
Learning Algorithm ◽  
Normal Sinus Rhythm ◽  
Characteristic Curve ◽  
External Validation ◽  
Digital Data ◽  
Internal Validation ◽  
Optimal Interval

AbstractAtrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

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