Department of Biomedical Systems & Medical Physics, Tehran University of Medical Sciences, TUMS, Tehran, Iran.
Biomed Eng Online. 2011 Mar 28;10:22. doi: 10.1186/1475-925X-10-22.
Electrocardiography (ECG) signal is a primary criterion for medical practitioners to diagnose heart diseases. The development of a reliable, accurate, non-invasive and robust method for arrhythmia detection could assists cardiologists in the study of patients with heart diseases. This paper provides a method for morphological heart arrhythmia detection which might have different shapes in one category and also different morphologies in relation to the patients. The distinctive property of this method in addition to accuracy is the robustness of that, in presence of Gaussian noise, time and amplitude shift.
In this work 2nd, 3rd and 4th order cumulants of the ECG beat are calculated and modeled by linear combinations of Hermitian basis functions. Then, the parameters of each cumulant model are used as feature vectors to classify five different ECG beats namely as Normal, PVC, APC, RBBB and LBBB using 1-Nearest Neighborhood (1-NN) classifier. Finally, after classifying each model, a final decision making rule apply to these specified classes and the type of ECG beat is defined.
The experiment was applied for a set of ECG beats consist of 9367 samples in 5 different categories from MIT/BIH heart arrhythmia database. The specificity of 99.67% and the sensitivity of 98.66% in arrhythmia detection are achieved which indicates the power of the algorithm. Also, the accuracy of the system remained almost intact in the presence of Gaussian noise, time shift and amplitude shift of ECG signals.
This paper presents a novel and robust methodology in morphological heart arrhythmia detection. The methodology based on the Hermite model of the Higher-Order Statistics (HOS). The ability of HOS in suppressing morphological variations of different class-specific arrhythmias and also reducing the effects of Gaussian noise, made HOS, suitable for detection morphological heart arrhythmias. The proposed method exploits these properties in conjunction with Hermitian model to perform an efficient and reliable classification approach to detect five morphological heart arrhythmias. And the time consumption of this method for each beat is less than the period of a normal beat.
心电图（ECG）信号是医学从业者诊断心脏病的主要标准。开发一种可靠、准确、非侵入性和稳健的心律失常检测方法，可以帮助心脏病专家研究心脏病患者。本文提出了一种形态学心律失常检测方法，该方法在同一类别中可能具有不同的形状，并且与患者相关的形态也可能不同。除了准确性之外，该方法的独特之处在于其稳健性，即使存在高斯噪声、时间和幅度偏移，它也能保持稳健。
在这项工作中，计算了 ECG 心拍的二阶、三阶和四阶累积量，并通过厄米特基函数的线性组合对其进行建模。然后，将每个累积量模型的参数用作特征向量，使用 1-近邻（1-NN）分类器对五种不同的 ECG 心拍进行分类，即正常、PVC、APC、RBBB 和 LBBB。最后，对每个模型进行分类后，应用一个最终决策规则来对这些指定的类别进行定义，并确定 ECG 心拍的类型。
该实验应用于一组由 9367 个样本组成的 ECG 心拍，这些心拍来自 MIT/BIH 心律失常数据库中的 5 个不同类别。该算法在心律失常检测中实现了 99.67%的特异性和 98.66%的灵敏度，这表明了该算法的强大功能。此外，该系统的准确性在存在高斯噪声、ECG 信号的时间偏移和幅度偏移时几乎保持不变。
本文提出了一种新颖而稳健的形态学心律失常检测方法。该方法基于高阶统计量（HOS）的厄米特模型。HOS 抑制不同类别特定心律失常形态变化的能力，以及降低高斯噪声的影响，使得 HOS 适合检测形态学心律失常。所提出的方法利用这些特性与厄米特模型相结合，以执行一种有效的、可靠的分类方法来检测五种形态学心律失常。并且，该方法对每个心拍的时间消耗小于正常心拍的周期。
