Premature atrial contraction during a wide complex tachycardia: What is the mechanism?

2020 ◽  
Vol 43 (11) ◽  
pp. 1390-1392
Author(s):  
Amin Zagzoog ◽  
Martin S. Green ◽  
Fahad Almehmadi ◽  
Girish M. Nair ◽  
Mehrdad Golian
Keyword(s):  
Wide Complex Tachycardia ◽  
Atrial Contraction ◽  
Premature Atrial Contraction

scholarly journals Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3235
Author(s):  
Koichi Fujiwara ◽  
Shota Miyatani ◽  
Asuka Goda ◽  
Miho Miyajima ◽  
Tetsuo Sasano ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mean Squared Error ◽  
False Positive Rate ◽  
Premature Ventricular Contraction ◽  
Heart Rate Variability Analysis ◽  
Unified Framework ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Positive Rate

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

scholarly journals Burden and Long Firing of Premature Atrial Contraction Early After Catheter Ablation Predict Late Recurrence of Atrial Fibrillation

2020 ◽  
Vol 84 (6) ◽  
pp. 894-901 ◽  
Author(s):  
Hiroyuki Inoue ◽  
Nobuaki Tanaka ◽  
Koji Tanaka ◽  
Yuichi Ninomiya ◽  
Yuko Hirao ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Late Recurrence ◽  
Atrial Contraction ◽  
Premature Atrial Contraction

scholarly journals Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

2021 ◽  
Author(s):  
Daria Aleksandrovna Ponomartseva ◽  
Ilia Vladislavovich Derevitskii ◽  
Sergey Valerevich Kovalchuk ◽  
Alina Yurevna Babenko
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Retrospective Study ◽  
Prediction Model ◽  
Performance Metrics ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Atrial Contraction ◽  
Premature Atrial Contraction

Abstract Background: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients’ management. However, to our knowledge, an instrument that establishes an individual risk of the condition is unavailable. Therefore, the aim of this study is to build a TAF prediction model and rank TAF predictors in order of importance. Methods: In this retrospective study, we have investigated 36 demographic and clinical features for 420 patients with overt hyperthyroidism, 30% of which had TAF. At first, the association of these features with TAF was evaluated by classical statistical methods. Then, we developed several TAF prediction models with eight different machine learning classifiers and compared them by performance metrics. The models included ten features that were selected based on their clinical effectuality and importance for model output. Finally, we ranked TAF predictors, elicited from the optimal final model, by the machine learning tehniques. Results: The best performance metrics prediction model was built with the extreme gradient boosting classifier. It had the reasonable accuracy of 84% and AUROC of 0.89 on the test set. The model confirmed such well-known TAF risk factors as age, sex, hyperthyroidism duration, heart rate and some concomitant cardiovascular diseases (arterial hypertension and conjestive heart rate). We also identified premature atrial contraction and premature ventricular contraction as new TAF predictors. The top five TAF predictors, elicited from the model, included (in order of importance) PAC, PVC, hyperthyroidism duration, heart rate during hyperthyroidism and age. Conclusions: We developed a machine learning model for TAF prediction. It seems to be the first available analytical tool for TAF risk assessment. In addition, we defined five most important TAF predictors, including premature atrial contraction and premature ventricular contraction as the new ones. These results have contributed to TAF prediction investigation and may serve as a basis for further research focused on TAF prediction improvement and facilitation of thyrotoxic patients’ management.

scholarly journals Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Syed Khairul Bashar ◽  
Dong Han ◽  
Shirin Hajeb-Mohammadalipour ◽  
Eric Ding ◽  
Cody Whitcomb ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
False Alarms ◽  
Time Frequency ◽  
Non Invasive ◽  
Accelerometer Signal ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Sensitivity Specificity

Abstract Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets.

scholarly journals e0560 Role of severity of OSAS on CRP and left atrial size in patients with premature atrial contraction

Heart ◽  
2010 ◽  
Vol 96 (Suppl 3) ◽  
pp. A173-A174
Author(s):  
K. Bundhoo ◽  
D. Xu
Keyword(s):  
Left Atrial ◽  
Left Atrial Size ◽  
Atrial Contraction ◽  
Atrial Size ◽  
Premature Atrial Contraction

Premature atrial contraction as a predictor of postoperative atrial fibrillation

2014 ◽  
Vol 23 (2) ◽  
pp. 153-156 ◽  
Author(s):  
Makoto Hashimoto ◽  
Akihiko Yamauchi ◽  
Satomi Inoue
Keyword(s):  
Atrial Fibrillation ◽  
Postoperative Atrial Fibrillation ◽  
Atrial Contraction ◽  
Premature Atrial Contraction

Premature Atrial Contraction

1971 ◽  
Vol 128 (3) ◽  
pp. 453 ◽  
Author(s):  
Douglas P. Zipes
Keyword(s):  
Atrial Contraction ◽  
Premature Atrial Contraction

Mo-P4:240 The loads of premature atrial contraction is associated with increased aortic stiffness

2006 ◽  
Vol 7 (3) ◽  
pp. 99
Author(s):  
J.Y. Chen ◽  
W.C. Tsai ◽  
C.H. Lee ◽  
T.H. Chao ◽  
C.H. Hsu ◽  
...  
Keyword(s):  
Aortic Stiffness ◽  
Atrial Contraction ◽  
Premature Atrial Contraction
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