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基于离散余弦变换-蒂杰能量算子模型的心电图室性早搏识别

Identification of Premature Ventricular Cycles of Electrocardiogram Using Discrete Cosine Transform-Teager Energy Operator Model.

作者信息

Sharmila Vallem, Reddy K Ashoka

机构信息

Department of ECE, Kamala Institute of Technology & Science, Huzurabad, Telangana 505468, India.

Department of ECE, Kakatiya Institute of Technology & Science, Warangal, Telangana 506015, India.

出版信息

J Med Eng. 2015;2015:438569. doi: 10.1155/2015/438569. Epub 2015 Mar 2.

DOI:10.1155/2015/438569
PMID:27019846
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4782747/
Abstract

An algorithm based on the ability of TEO to track the changes in the envelope of ECG signal is proposed for identifying PVCs in ECG. Teager energy is calculated from DCT coefficients of ECG signal. This method can be considered as computationally efficient algorithm when compared with the well-known DCT cepstrum technique. EPE is derived from the teager energy of DCT coefficients in DCT-TEO method and from the cepstrum of DCT coefficients in the existing method. EPE determines the decay rate of the action potential of ECG beat and provides sufficient information to identify the PVC beats in ECG data. EPEs obtained by DCT-TEO and existing DCT cepstrum models are compared. The proposed algorithm has resulted in performance measures like sensitivity of 98-100%, positive predictivity of 100%, and detection error rate of 0.03%, when tested on MIT-BIH database signals consisting of PVC and normal beats. Result analysis reveals that the DCT-TEO algorithm worked well in clear identification of PVCs from normal beats compared to the existing algorithm, even in the presence of artifacts like baseline wander, PLI, and noise with SNR of up to -5 dB.

摘要

提出了一种基于TEO跟踪心电信号包络变化能力的算法，用于识别心电图中的室性早搏。从心电信号的离散余弦变换（DCT）系数计算Teager能量。与著名的DCT倒谱技术相比，该方法可被视为一种计算效率高的算法。在DCT-TEO方法中，能量百分比熵（EPE）由DCT系数的Teager能量导出，而在现有方法中，EPE由DCT系数的倒谱导出。EPE决定了心电搏动动作电位的衰减率，并为识别心电图数据中的室性早搏提供了充分的信息。比较了DCT-TEO和现有DCT倒谱模型获得的EPE。在由室性早搏和正常搏动组成的MIT-BIH数据库信号上进行测试时，所提出的算法产生了诸如灵敏度为98 - 100%、阳性预测值为100%以及检测错误率为0.03%等性能指标。结果分析表明，与现有算法相比，DCT-TEO算法在从正常搏动中清晰识别室性早搏方面表现良好，即使存在诸如基线漂移、肌电干扰（PLI）以及信噪比高达 - 5 dB的噪声等伪迹。

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引用本文的文献

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