Suppr超能文献

持续性心房颤动的长期特征:波形形态、频率及不规则性分析

Long-term characterization of persistent atrial fibrillation: wave morphology, frequency, and irregularity analysis.

作者信息

Goya-Esteban Rebeca, Sandberg Frida, Barquero-Pérez Óscar, García-Alberola Arcadio, Sörnmo Leif, Rojo-Álvarez José Luis

机构信息

Department of Signal Theory and Communications, Rey Juan Carlos University, Camino del Molino s/n, 28943, Fuenlabrada, Spain,

出版信息

Med Biol Eng Comput. 2014 Dec;52(12):1053-60. doi: 10.1007/s11517-014-1199-x. Epub 2014 Oct 5.

Abstract

Short-term properties of atrial fibrillation (AF) frequency, f-wave morphology, and irregularity parameters have been thoroughly studied, but not long-term properties. In the present work, f-wave morphology is characterized by principal component analysis, introducing a novel temporal parameter defined by the cumulative normalized variance of the three largest principal components (r3). Based on 7-day recordings from nine patients with stable chronic heart failure and persistent AF, long-term properties were studied in terms of r3 AF frequency, and sample entropy (SampEn). The main result of the present study is that detection of circadian rhythms depends on the parameter considered: rhythms were found in six (r3, SampEn) and five (AF frequency) patients, but not always in the same patient. Another important result is that circadian rhythms detected in 7-day recordings could not always be detected in 24-h periods, thus shedding new light on the results in previous studies which all were based on 24-h recordings. Infradian rhythms were found in four (r3, SampEn) and one (AF frequency) patients.

摘要

心房颤动(AF)频率、f波形态和不规则参数的短期特性已得到充分研究,但长期特性尚未得到研究。在本研究中,通过主成分分析对f波形态进行表征,引入了一个由三个最大主成分的累积归一化方差(r3)定义的新时间参数。基于9例稳定慢性心力衰竭和持续性AF患者的7天记录,从r3、AF频率和样本熵(SampEn)方面研究了长期特性。本研究的主要结果是,昼夜节律的检测取决于所考虑的参数:在6例患者(r3、SampEn)和5例患者(AF频率)中发现了节律,但并非在同一患者中总是能发现。另一个重要结果是,在7天记录中检测到的昼夜节律在24小时期间并不总是能检测到,从而为以前所有基于24小时记录的研究结果提供了新的线索。在4例患者(r3、SampEn)和1例患者(AF频率)中发现了次昼夜节律。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验