Abstract P708: Artificial Intelligence Enabled-Electrocardiography for the Detection of Cerebral Infarcts in Patients With Atrial Fibrillation

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Erika Weil ◽  
Peter Noseworthy ◽  
Alejandro Rabinstein ◽  
Paul Friedman ◽  
Camden Lopez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Continuous Measure ◽  
Cerebral Infarcts ◽  
Normal Sinus ◽  
History Of ◽  
Ecg Score ◽  
Age And Sex

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score > 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score > 0.5 was associated with infarcts ( p < 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

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

scholarly journals TO DETERMINE THE ROLE OF RANOLAZINE FOR RHYTHM CONTROL STRATEGY IN PATIENTS WITH NON- VALVULAR ATRIAL FIBRILLATION- AN OBSERVATIONAL STUDY FROM A TERTIARY CARE CENTER. (RANOVA STUDY)

2020 ◽  
pp. 48-53
Author(s):  
Praveen Shukla ◽  
Awadhesh Kumar Sharma ◽  
Biswajit Majumder ◽  
Pritam Kumar Chatterjee ◽  
Vinay Krishna ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Control Strategy ◽  
Normal Sinus Rhythm ◽  
Rhythm Control ◽  
Normal Sinus ◽  
History Of ◽  
Group A ◽  
Group B

Objectives – Non- valvular atrial fibrillation (NVAF) is the most commonly occurring arrhythmia worldwide .Ranolazine is an emerging drug with a ray of hope in the management of NVAF. This is the first large observational study with longer follow up of one year. Methods - It is a hospital based observational prospective study. A total of 100 patients was recruited for the study .The primary objective was to determine the efficacy of ranolazine in converting NVAF to sinus rhythm & the secondary objective was to study epidemiological aspects of NVAF. Results –After 1 month of follow up conversion to normal sinus rhythm was 12% in group A & 6% in group B (6%), it was not significant statistically (Z=1.48p=0.13). After 6 months, conversion to normal sinus rhythm was increased from 12% to 18% in group A which was preserved at 12 months of follow up and statistically significant and higher than that of group B (6.0%) (Z=2.61p=0.009). In predisposing risk factors & other co-morbidities HTN was present in 61%, obesity together with overweight in 37%, smoking in 44%, history of moderate amount of alcohol intake in 35%, history of CVA/TIA in 13%, DM in 11%, CKD in 4%, CAD in 30%, COPD in 20% and congestive heart failure in 15% of the patients. Conclusion- Ranolazine is an effective option when used for rhythm control strategy in NVAF. HTN is the predominant predisposing risk factor.

Congenital Left Atrial Aneurysm

1993 ◽  
Vol 1 (4) ◽  
pp. 180-183
Author(s):  
Naresh Trehan ◽  
Zile Singh Meharwal ◽  
Vijay Kumar Sharma
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Left Atrial ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
History Of

A 13–year-old boy, who presented with a 4-year history of breathlessness and palpitation, was admitted with atrial fibrillation. Left atrial aneurysm was diagnosed with echocardiography and angiography. After excision of the aneurysm, the patient became asymptomatic and was in normal sinus rhythm.

scholarly journals An Atrial Fibrillation with Normal Limits using Signal Processing- A Review

2018 ◽  
Vol 7 (2.24) ◽  
pp. 453
Author(s):  
S. Sathish ◽  
K Mohanasundaram
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
P Wave ◽  
Wave Duration ◽  
Normal Limits ◽  
Normal Sinus ◽  
P Wave Duration ◽  
Standard Lead ◽  
Wave Morphology

Atrial fibrillation is an irregular heartbeat (arrhythmia) that can lead to the stroke, blood clots, heart failure and other heart related complications. This causes the symptoms like rapid and irregular heartbeat, fluttering, shortness of breath etc. In India for every around 4000 people eight of them are suffering from Atrial Fibrillation. P-wave Morphology.  Abnormality of P-wave (Atrial ECG components) seen during sinus rhythm are associated with Atrial fibrillation. P-wave duration is the best predictor of preoperative atrial fibrillation. but the small amplitudes of atrial ECG and its gradual increase from isometric line create difficulties in defining the onset of P wave in the Standard Lead Limb system (SLL).Studies shows that prolonged P-wave have duration in patients (PAF) In this Study, a Modified Lead Limb (MLL) which solves the practical difficulties in analyzing the P-ta interval for both in healthy subjects and Atrial Fibrillation patients. P-Ta wave interval and P-wave duration can be estimated with following proposed steps which is applicable for both filtered and unfiltered atrial ECG components which follows as the clinical database trials. For the same the p-wave fibrillated signals that escalates the diagnosis follows by providing minimal energy to recurrent into a normal sinus rhythm.  

scholarly journals TREMORS MIMICKING ATRIAL FLUTTER IN AN ELDERLY LADY

2022 ◽  
Vol 54 (4) ◽  
pp. 370-372
Author(s):  
Intisar Ahmed ◽  
Hunaina Shahab ◽  
Aamir Hameed Khan
Keyword(s):  
Chest Pain ◽  
Sinus Rhythm ◽  
Hospital Stay ◽  
Atrial Flutter ◽  
Normal Sinus Rhythm ◽  
Correct Diagnosis ◽  
Normal Sinus ◽  
Inappropriate Treatment ◽  
History Of

A 77 -year-old lady with history of hypertension and Parkinson`s disease was admitted with cough and fever and diagnosed as pneumonia. On second day of admission, she started having chest pain, initial ECG was interpreted as atrial flutter. When her ECG was reviewed by a cardiologist, ECG features were found to be consistent with artifacts due to tremors. A repeat 12 leads ECG clearly demonstrated normal sinus rhythm and the patient remained completely asymptomatic throughout the hospital stay. Tremor induced artifacts can be mistaken for arrhythmias. Correct diagnosis is important, in order to avoid inappropriate treatment and unnecessary interventions.

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