scholarly journals ECG Restitution Analysis and Machine Learning to Detect Paroxysmal Atrial Fibrillation: Insight from the Equine Athlete as a Model for Human Athletes

Function ◽  
2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Ying H Huang ◽  
Vadim Alexeenko ◽  
Gary Tse ◽  
Christopher L -H Huang ◽  
Celia M Marr ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Majority Vote ◽  
Characteristic Curve ◽  
Simple Majority ◽  
K Nearest Neighbor ◽  
Automated Method ◽  
Arrhythmogenic Substrate ◽  
Individual Record

Abstract Atrial fibrillation is the most frequent arrhythmia in both equine and human athletes. Currently, this condition is diagnosed via electrocardiogram (ECG) monitoring which lacks sensitivity in about half of cases when it presents in paroxysmal form. We investigated whether the arrhythmogenic substrate present between the episodes of paroxysmal atrial fibrillation (PAF) can be detected using restitution analysis of normal sinus-rhythm ECGs. In this work, ECG recordings were obtained during routine clinical work from control and horses with PAF. The extracted QT, TQ, and RR intervals were used for ECG restitution analysis. The restitution data were trained and tested using k-nearest neighbor (k-NN) algorithm with various values of neighbors k to derive a discrimination tool. A combination of QT, RR, and TQ intervals was used to analyze the relationship between these intervals and their effects on PAF. A simple majority vote on individual record (one beat) classifications was used to determine the final classification. The k-NN classifiers using two-interval measures were able to predict the diagnosis of PAF with area under the receiving operating characteristic curve close to 0.8 (RR, TQ with k ≥ 9) and 0.9 (RR, QT with k ≥ 21 or TQ, QT with k ≥ 25). By simultaneously using all three intervals for each beat and a majority vote, mean area under the curves of 0.9 were obtained for all tested k-values (3–41). We concluded that 3D ECG restitution analysis can potentially be used as a metric of an automated method for screening of PAF.

Artificial intelligence-enabled detection of paroxysmal atrial fibrillation from normal sinus ECGs in patients with coronary microvascular dysfunction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Ahmad ◽  
M Corban ◽  
T Toya ◽  
Z.I Attia ◽  
P Noseworthy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Functional Assessment ◽  
Paroxysmal Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Area Under The Curve ◽  
Microvascular Dysfunction ◽  
Persistent Atrial Fibrillation ◽  
Coronary Microvascular Dysfunction ◽  
Normal Sinus

Abstract Background Artificial Intelligence (AI) algorithms enabled the detection of patients with paroxysmal atrial fibrillation (PAF) from a single normal sinus rhythm (NSR) ECG. Coronary microvascular dysfunction (CMD) is a precursor for coronary artery disease, which is a known risk factor for AF. Purpose The aim of this study is to examine the probability of PAF, according to AI-enabled algorithm estimation, in patients with CMD. Methods 1858 patients without persistent atrial fibrillation with signs and/or symptoms of ischemia and with non-obstructive CAD (<40% stenosis) who underwent invasive coronary microvascular functional assessment and the ECG closest to the functional assessment were included in this analysis. Patients with coronary flow velocity reserve (CFR) <2 in response to adenosine were labelled as endothelial-independent CMD; % increase in coronary blood flow (%ΔCBF) <50% in response to acetylcholine were labelled as endothelial-dependent CMD. Patients were categorized into 4 groups. G1: Normal (NL) CFR/NL %ΔCBF; G2: Abnormal (ABN) %ΔCBF only; G3: ABN CFR only; G4: ABL CFR & %ΔCBF. The probability of having PAF (%probAF) was calculated by a previously-trained and validated AI algorithm. AF Flag = %probAF >9%; which is a pre-set cut-off found to have the highest accuracy of identifying patients with PAF (Area Under the Curve = 0.87). Results Mean age for patients was 51.2±12.4 and 66.3% were females. 835 (45%) were in G1, 39 (2%) in G2, 911 (49%) in G3, and 73 (4%) in G4. Compared to G1 and G2, G3 and G4 were older, had more diabetes and higher smoking rates (p<0.05 for all). Furthermore, G4 had a significantly higher %probAF compared to other groups (Fig. 1). G4 were also more likely to be flagged by the algorithm as having PAF, even after adjusting for cardiovascular risk factors, with an odds ratio of 1.9 [CI 95% 1.1–3.3; p=0.03]) (Fig. 2). Conclusion Patients with combined CMD have a significantly higher probability of having PAF based on an AI-enabled algorithm. Further research is warranted to know if patients with CMD would benefit from formal AF screening at the time of diagnosis. Funding Acknowledgement Type of funding source: None

scholarly journals The complexity of clinically-normal sinus-rhythm ECGs is decreased in equine athletes with a diagnosis of paroxysmal atrial fibrillation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vadim Alexeenko ◽  
James A. Fraser ◽  
Mark Bowen ◽  
Christopher L.-H. Huang ◽  
Celia M. Marr ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Paroxysmal Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Clinically Normal ◽  
Normal Sinus

scholarly journals Electrophysiologic Changes in Arrhythmogenic Substrate Following the Maze Procedure in Patients With Lone and Paroxysmal Atrial Fibrillation

1997 ◽  
Vol 61 (12) ◽  
pp. 988-996 ◽  
Author(s):  
Wataru Shimizu ◽  
Yoshio Kosakai ◽  
Masashi Inagaki ◽  
Takashi Kurita ◽  
Kazuhiro Suyama ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
Maze Procedure ◽  
Arrhythmogenic Substrate

scholarly journals Can Common-Type Atrial Flutter Be a Sign of an Arrhythmogenic Substrate in Paroxysmal Atrial Fibrillation?

Circulation ◽  
2007 ◽  
Vol 116 (24) ◽  
pp. 2786-2792 ◽  
Author(s):  
Wendel Moreira ◽  
Carl Timmermans ◽  
Hein J.J. Wellens ◽  
Yuka Mizusawa ◽  
Suzanne Philippens ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Atrial Flutter ◽  
Paroxysmal Atrial Fibrillation ◽  
Common Type ◽  
Arrhythmogenic Substrate

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.

scholarly journals Paroxysmal Atrial Fibrillation Induced by Ciprofloxacin: A Rare Adverse Effect

2020 ◽  
Author(s):  
Fatma Hammami ◽  
Makram Koubaa ◽  
Sahar Ben Kahla ◽  
Amal Chakroun ◽  
Khaoula Rekik ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Adverse Effects ◽  
Liver Abscess ◽  
Paroxysmal Atrial Fibrillation ◽  
Epigastric Pain ◽  
Normal Sinus Rhythm ◽  
Klebsiella Pneumonia ◽  
Interval Prolongation ◽  
Normal Sinus ◽  
Qt Interval Prolongation

Despite their adverse effects, fluoroquinolones continue to be commonly prescribed antibiotics. Ciprofloxacin remains the safest with remarkably few adverse effects of all fluoroquinolones. Here, we present a rare case of paroxysmal atrial fibrillation induced by ciprofloxacin intake in a 72-year-old woman. She was treated with ciprofloxacin and ceftriaxone for urinary tract infection caused by Klebsiella pneumonia and complicated with liver abscess. On the fifth day of ciprofloxacin intake, she suddenly complained of heart palpitations and epigastric pain. An electrocardiogram revealed supraventricular tachycardia type atrial fibrillation at 130 beats per minute. No QT interval prolongation was noted. Ciprofloxacin was stopped as it was the most incriminated to induce arrhythmia. A control electrocardiogram showed normal sinus rhythm. We continued ceftriaxone use solely for 3 weeks until the resolution of the liver abscess. Although rare, early detection of atrial fibrillation induced by ciprofloxacin may decrease the severity of complications and prevent death.

scholarly journals Abnormal ECG Classification using Empirical Mode Decomposition and Entropy

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Suci Aulia ◽  
Sugondo Hadiyoso
Keyword(s):  
Atrial Fibrillation ◽  
Feature Extraction ◽  
Left Bundle Branch Block ◽  
Normal Sinus Rhythm ◽  
Heart Defects ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Specificity And Sensitivity

Heart disease is one of the leading causes of death in the world. Early detection followed by therapy is one of the efforts to reduce the mortality rate of this disease. One of the leading medical instruments for diagnosing heart disorders is the electrocardiogram (ECG). The shape of the ECG signal represents normal or abnormal heart conditions. Some of the most common heart defects are atrial fibrillation and left bundle branch block. Detection or classification can be difficult if performed visually. Therefore in this study, we propose a method for the automatic classification of ECG signals. This method generally consists of feature extraction and classification. The feature extraction used is based on information theory, namely Fuzzy entropy and Shannon entropy, which is calculated on the decomposed signal. The simulated ECG signals are of three types: normal sinus rhythm, atrial fibrillation, and left bundle branch block. Support vector machine and k-Nearest Neighbor algorithms were employed for the validation performance of the proposed method. From the test results obtained, the highest accuracy is 81.1%. With specificity and sensitivity of 79.4% and 89.8%, respectively. It is hoped that this proposed method can be further developed to assist clinical diagnosis.

scholarly journals Holter monitoring for assessment of possible paroxysmal atrial fibrillation as a cause in otherwise normal sinus rhythm patients with acute ischemic stroke: a hospital based study

2021 ◽  
Vol 10 (2) ◽  
pp. 63-66
Author(s):  
Navaraj Paudel ◽  
Namrata Thapa ◽  
Ramchandra Kafle ◽  
Subash Sapkota ◽  
Abhishek Maskey
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Paroxysmal Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Heart Rhythm ◽  
Female Ratio ◽  
Mortality And Morbidity ◽  
Cerebrovascular Accidents ◽  
Normal Sinus

Background: Stroke/ cerebrovascular accidents are common and among the major causes of mortality and morbidity. Thromboembolism are also among the causes of ischemic strokes. Diagnosis of atrial fibrillation makes the difference in the management of ischemic strokes for long term as anticoagulation are given in these cases for prevention of further embolic events. Methods: A prospective observational study was done from july 2019 to june 2021 for patients admitted for ischemic strokes who were otherwise found to have normal sinus rhythm. A 24 hour holter monitor was connected and analyzed for possible paroxysmal atrial fibrillation. Baseline investigations including trans-thoracic echocardiography was done. Data were analyzed and results were sought. Results: Out of 212 patients admitted for stroke, only 116 were eligible for the study. Male female ratio was 2:1. Ninety-four percent of patients had at least one or more risk factors: Smokers (74%) followed by Hypertensives (70%), Dyslipidemics (54%) and Diabetics (20%). Twenty-two percent of patients were found to have paroxysmal atrial fibrillation. There was no gender difference between the occurrences of paroxysmal atrial fibrillation. Among the risk factors, smoking and hypertension were significantly associated with the occurrence of paroxysmal atrial fibrillation (P: 0.001 and 0.002 respectively) while other risk factors like diabetes and dyslipidemia had no significant association. There was significant association of paroxysmal atrial fibrillation with mortality (P: 0.0013). Conclusion: Patients who are in otherwise normal sinus rhythm in electrocardiography with ischemic cerebrovascular accidents may have paroxysmal atrial fibrillation as cause of event. Smoking and hypertensive patients are significantly associated with occurrence of paroxysmal atrial fibrillation and stroke and these patients are more likely to die than the patients having normal heart rhythm. Management of these patients definitely defer in terms of possible use of anticoagulants. 

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