Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation
Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation
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DOI：
10.1016/j.cmpb.2016.04.009
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发表时间：
2016-07-01
影响因子：
6.1
通讯作者：
Rieta, Jose J.
中科院分区：
文献类型：
--
作者：
Garcia, Manuel;Rodenas, Juan;Rieta, Jose J.
背景和目的：心房颤动（AF）是最常见的持续性心律失常，也是全球日益增长的医疗负担。它通常是无症状的，可能会出现非常短的持续时间的事件，因此，其自动检测方法的发展是一个具有挑战性的要求，以实现早期诊断和治疗策略。本工作介绍了一种新的方法，利用相对小波能量（RWE）自动检测AF发作的各种各样的长度。方法：所提出的方法分析心房活动的表面心电图（ECG），即，TQ间期，因此与心室活动无关。为了提高其性能在嘈杂的录音，信号平均技术。该方法的性能已经过测试，在不同的AF变量条件下，如心率，其变异性，心房活动幅度或噪声的存在下的合成记录。接下来，该方法进行了测试与真实的ECG recording.Results：结果证明，RWE提供了一个强大的自动检测AF下的心率，心房活动幅度以及噪声记录的宽范围。此外，该方法的检测延迟被证明是短于大多数以前的作品。通过对15个TQ间期取平均值，实现了检测延迟和噪声鲁棒性之间的权衡。在这些条件下，AF被检测到在不到7拍，准确率高于90%，这是以前的works.Conclusions：不同于大多数以前的作品，这主要是基于量化的不规则心室反应在AF，建议的度量提出了两个主要的优点。首先，即使在心率没有变化的情况下，它也可以成功地执行。其次，它由一个单一的指标，从而使其临床解释和实时实现比以前的方法更容易，需要在复杂的分类器下的组合指数。(C)2016爱思唯尔爱尔兰有限公司版权所有。
Background and Objectives: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a growing healthcare burden worldwide. It is often asymptomatic and may appear as episodes of very short duration; hence, the development of methods for its automatic detection is a challenging requirement to achieve early diagnosis and treatment strategies. The present work introduces a novel method exploiting the relative wavelet energy (RWE) to automatically detect AF episodes of a wide variety in length.Methods: The proposed method analyzes the atrial activity of the surface electrocardiogram (ECG), i.e., the TQ interval, thus being independent on the ventricular activity. To improve its performance under noisy recordings, signal averaging techniques were applied. The method's performance has been tested with synthesized recordings under different AF variable conditions, such as the heart rate, its variability, the atrial activity amplitude or the presence of noise. Next, the method was tested with real ECG recordings.Results: Results proved that the RWE provided a robust automatic detection of AF under wide ranges of heart rates, atrial activity amplitudes as well as noisy recordings. Moreover, the method's detection delay proved to be shorter than most of previous works. A trade-off between detection delay and noise robustness was reached by averaging 15 TQ intervals. Under these conditions, AF was detected in less than 7 beats, with an accuracy higher than 90%, which is comparable to previous works.Conclusions: Unlike most of previous works, which were mainly based on quantifying the irregular ventricular response during AF, the proposed metric presents two major advantages. First, it can perform successfully even under heart rates with no variability. Second, it consists of a single metric, thus turning its clinical interpretation and real-time implementation easier than previous methods requiring combined indices under complex classifiers. (C) 2016 Elsevier Ireland Ltd. All rights reserved.