Khadra Labib, Al-Fahoum Amjed S, Binajjaj Saed
Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan.
IEEE Trans Biomed Eng. 2005 Nov;52(11):1840-5. doi: 10.1109/TBME.2005.856281.
Ventricular tachyarrhythmias, in particular ventricular fibrillation (VF), are the primary arrhythmic events in the majority of patients suffering from sudden cardiac death. Attention has focused upon these articular rhythms as it is recognized that prompt therapy can lead to a successful outcome. There has been considerable interest in analysis of the surface electrocardiogram (ECG) in VF centred on attempts to understand the pathophysiological processes occurring in sudden cardiac death, predicting the efficacy of therapy, and guiding the use of alternative or adjunct therapies to improve resuscitation success rates. Atrial fibrillation (AF) and ventricular tachycardia (VT) are other types of tachyarrhythmias that constitute a medical challenge. In this paper, a high order spectral analysis technique is suggested for quantitative analysis and classification of cardiac arrhythmias. The algorithm is based upon bispectral analysis techniques. The bispectrum is estimated using an autoregressive model, and the frequency support of the bispectrum is extracted as a quantitative measure to classify atrial and ventricular tachyarrhythmias. Results show a significant difference in the parameter values for different arrhythmias. Moreover, the bicoherency spectrum shows different bicoherency values for normal and tachycardia patients. In particular, the bicoherency indicates that phase coupling decreases as arrhythmia kicks in. The simplicity of the classification parameter and the obtained specificity and sensitivity of the classification scheme reveal the importance of higher order spectral analysis in the classification of life threatening arrhythmias. Further investigations and modification of the classification scheme could inherently improve the results of this technique and predict the instant of arrhythmia change.
室性快速心律失常，尤其是心室颤动（VF），是大多数心源性猝死患者的主要心律失常事件。由于人们认识到及时治疗可带来成功的结果，因此注意力集中在了这些特定的节律上。对心室颤动时体表心电图（ECG）的分析一直备受关注，其重点在于试图了解心源性猝死中发生的病理生理过程、预测治疗效果以及指导使用替代或辅助疗法以提高复苏成功率。心房颤动（AF）和室性心动过速（VT）是构成医学挑战的其他类型的快速心律失常。本文提出了一种高阶谱分析技术，用于心律失常的定量分析和分类。该算法基于双谱分析技术。使用自回归模型估计双谱，并提取双谱的频率支持作为定量指标，以对房性和室性快速心律失常进行分类。结果表明，不同心律失常的参数值存在显著差异。此外，双相干谱显示正常患者和心动过速患者的双相干值不同。特别是，双相干表明随着心律失常的发生，相位耦合会降低。分类参数的简单性以及所获得的分类方案的特异性和敏感性揭示了高阶谱分析在危及生命的心律失常分类中的重要性。对分类方案的进一步研究和改进可能会从本质上改善该技术的结果，并预测心律失常变化的时刻。
