Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions.
Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions.
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DOI：
10.1186/1475-925x-9-43
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发表时间：
2010-09-04
影响因子：
3.9
通讯作者：
Hasan MK
中科院分区：
文献类型：
--
作者：
Anas EM;Lee SY;Hasan MK
室性心动过速（VT）和室颤（VF）是最严重的心律失常，需要快速准确的检测来挽救生命。自动体外除颤器（AED）已经开发出来，可以使用其内部的复杂算法识别这些严重的心律失常，并确定是否应该进行电击以重置心律并恢复自主循环。通过设计能够更准确地区分可电击节律与不可电击节律的新算法来提高AED的安全性和功效是当今的要求，因为它们在公共场所中使用。在本文中，我们提出了一种顺序检测算法，以分离这些严重的心脏病理从其他心律失常的基础上的平均绝对值的信号，某些低阶固有模式函数（IMF）的经验模式分解（EMD）分析的信号和心率确定技术。首先，我们提出了一个直接的波形量化为基础的方法来分离VT加VF从其他心律失常。通过计算信号的平均绝对值（称为平均信号强度）来量化心电图波形。然后，我们使用的IMF，其中有较高的相似度与VF相比，VT，从VTVF信号分离VF。在最后阶段，使用简单的速率确定技术来计算VT信号的心率，并且测量VF信号的幅度以从VF中分离粗VF。经过这三个阶段的顺序检测程序，我们分别识别两个组成部分的可电击节律。该算法的有效性已被验证，并与其他现有的算法，例如，HILB、PSR、SPEC、TCI、计数，使用MIT-BIH心律失常数据库、Creighton大学室性快速性心律失常数据库和MIT-BIH恶性室性心律失常数据库。四个质量参数（例如，灵敏度、特异性、阳性预测性和准确性）以确定所提出的算法和其它比较算法的质量。比较结果已被提交的识别VTVF，VF和可电击的节奏（VF + VT高于180 bpm）。结果表明，显着提高性能的建议EMD为基础的新方法相比，其他报道的技术在检测威胁生命的心律失常从一组大型数据库。
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the most serious cardiac arrhythmias that require quick and accurate detection to save lives. Automated external defibrillators (AEDs) have been developed to recognize these severe cardiac arrhythmias using complex algorithms inside it and determine if an electric shock should in fact be delivered to reset the cardiac rhythm and restore spontaneous circulation. Improving AED safety and efficacy by devising new algorithms which can more accurately distinguish shockable from non-shockable rhythms is a requirement of the present-day because of their uses in public places. In this paper, we propose a sequential detection algorithm to separate these severe cardiac pathologies from other arrhythmias based on the mean absolute value of the signal, certain low-order intrinsic mode functions (IMFs) of the Empirical Mode Decomposition (EMD) analysis of the signal and a heart rate determination technique. First, we propose a direct waveform quantification based approach to separate VT plus VF from other arrhythmias. The quantification of the electrocardiographic waveforms is made by calculating the mean absolute value of the signal, called the mean signal strength. Then we use the IMFs, which have higher degree of similarity with the VF in comparison to VT, to separate VF from VTVF signals. At the last stage, a simple rate determination technique is used to calculate the heart rate of VT signals and the amplitude of the VF signals is measured to separate the coarse VF from VF. After these three stages of sequential detection procedure, we recognize the two components of shockable rhythms separately. The efficacy of the proposed algorithm has been verified and compared with other existing algorithms, e.g., HILB, PSR, SPEC, TCI, Count, using the MIT-BIH Arrhythmia Database, Creighton University Ventricular Tachyarrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database. Four quality parameters (e.g., sensitivity, specificity, positive predictivity, and accuracy) were calculated to ascertain the quality of the proposed and other comparing algorithms. Comparative results have been presented on the identification of VTVF, VF and shockable rhythms (VF + VT above 180 bpm). The results show significantly improved performance of the proposed EMD-based novel method as compared to other reported techniques in detecting the life threatening cardiac arrhythmias from a set of large databases.