Predicting termination of paroxysmal atrial fibrillation using higher order statistics in EMD domain

机译：使用高阶统计在EMD结构域中预测阵发性心房颤动的终止

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This paper presents an algorithm for predicting termination of paroxysmal atrial fibrillation (PAF) attacks by using higher order statistical moments of RR-intervals signal calculated in the empirical mode decomposition (EMD) domain. In the proposed method, RR-intervals signal is decomposed into a set of intrinsic mode functions (IMF) and higher order moments including variance, skewness, and kurtosis, calculated from the first four IMFs. The appropriateness of these features in predicting the termination of PAF is studied using atrial fibrillation termination database (AFTDB) which consists of 3 types of AF episodes: N-type (non-terminated AF episode), S-type (terminated 1 min after the end of the record), and T-type (terminated immediately after the end of the record). By using a Support vector machine (SVM) classifier for classification of PAF episodes, we obtained specificity, sensitivity, and positive predictivity 96.73%, 93.45%, and 94.84%, respectively. The significant advantage of the proposed method comparing to the other existing approaches is that our algorithm can simultaneously discriminate 3 types of AF episodes with high accuracy. The results demonstrate that the extracted features in EMD domain can be used as a suitable tool for predicting termination of PAF.

机译：本文介绍了一种用于预测阵发性心房颤动（PAF）攻击的终止通过使用在经验模式分解（EMD）域中计算的RR间隔信号的高阶统计矩来预测阵发性颤动的攻击。在所提出的方法中，RR-间隔信号被分解成一组内在模式功能（IMF）和高阶矩，包括从前四个IMF计算的方差，偏振和峰度。研究了这些特征在预测PAF终止时的适当性是使用心房颤动终端数据库（AFTDB），该数据库（AFTDB）由3种类型的AF发作组成：n型（非终止AF集），S型（后1分钟记录的结束）和T型（在记录结束后立即终止）。通过使用支持向量机（SVM）分类器进行PAF发作的分类，我们分别获得特异性，敏感性和阳性预测性96.73％，93.45％和94.84％。与其他现有方法相比的所提出的方法的显着优点是，我们的算法可以同时以高精度辨别3种类型的AF剧集。结果表明，EMD结构域中的提取特征可用作预测PAF终止的合适工具。

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《Iranian Conference on Biomedical Engineering》|2013年||共6页
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Mohebbi Maryam;
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中图分类生物医学工程;
关键词
Higher order statistics; RR-intervals signal; empirical mode decomposition; paroxysmal atrial fibrillation;

机译：高阶统计;rr-intervals信号;经验模式分解;阵发性心房颤动;

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专利

1. A NOVEL APPLICATION OF HIGHER ORDER STATISTICS OF R-R INTERVAL SIGNAL IN EMD DOMAIN FOR PREDICTING TERMINATION OF ATRIAL FIBRILLATION [J] . Mohebbi Maryam Journal of biological systems . 2015,第1期

机译：R-R间隔信号在EMD域中的高阶统计量在心房纤颤终止中的新应用
2. Predicting termination of paroxysmal atrial fibrillation using empirical mode decomposition of the atrial activity and statistical features of the heart rate variability [J] . MohebbiM., GhassemianH. Medical and Biological Engineering and Computing: Journal of the International Federation for Medical and Biological Engineering . 2014,第5期

机译：使用心房活动的经验模式分解和心率变异性的统计特征来预测阵发性心房颤动的终止
3. Echocardiographic predictors of frequency of paroxysmal atrial fibrillation (AF) and its progression to persistent AF in hypertensive patients with paroxysmal AF: Results from the Japanese Rhythm Management Trial II for Atrial Fibrillation (J-RHYTHM II Study) [J] . SuzukiT., YamazakiT., OgawaS., Heart rhythm: the official journal of the Heart Rhythm Society . 2011,第12期

机译：超声心动图预测阵发性房颤的高血压患者阵发性心房颤动（AF）的频率及其向持续性房颤的进展：日本房颤的心律管理试验II（J-RHYTHM II研究）
4. Predicting termination of paroxysmal atrial fibrillation using higher order statistics in EMD domain [C] . Mohebbi Maryam 2013 20th Iranian Conference on Biomedical Engineering . 2013

机译：使用EMD域中的高阶统计量来预测阵发性心房颤动的终止
5. Prefiltering for improved unknown and known source correlation detection of broadband oscillatory transients and predicting the onset of paroxysmal atrial fibrillation using feature extraction and a hamming neural network. [D] . Dean, Marcella Elsener. 2003

机译：进行预过滤，以改进宽带振荡瞬态的未知和已知源相关性检测，并使用特征提取和汉明神经网络预测阵发性房颤的发作。
6. Left atrial effective conducting size predicts atrial fibrillation vulnerability in persistent but not paroxysmal atrial fibrillation [O] . Steven E. Williams, Louisa O’Neill, Caroline H. Roney, -1

机译：左心房有效传导尺寸可预测持续性而非阵发性心房颤动中的心房颤动脆弱性
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Predicting termination of paroxysmal atrial fibrillation using higher order statistics in EMD domain

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