Rabbani Hossein, Mahjoob Mohammad Parsa, Farahabadi Eiman, Farahabadi Amin, Dehnavi Alireza Mehri
Assistant Professor, Biomedical Engineering Department, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
J Res Med Sci. 2011 Nov;16(11):1473-82.
Ischemic heart disease is one of the common fatal diseases in advanced countries. Because signal perturbation in healthy people is less than signal perturbation in patients, entropy measure can be used as an appropriate feature for ischemia detection.
Four entropy-based methods comprising of using electrocardiogram (ECG) signal directly, wavelet sub-bands of ECG signals, extracted ST segments and reconstructed signal from time-frequency feature of ST segments in wavelet domain were investigated to distinguish between ECG signal of healthy individuals and patients. We used exercise treadmill test as a gold standard, with a sample of 40 patients who had ischemic signs based on initial diagnosis of medical practitioner.
The suggested technique in wavelet domain resulted in the highest discrepancy between healthy individuals and patients in comparison to other methods. Specificity and sensitivity of this method were 95% and 94% respectively.
The method based on wavelet sub-bands outperformed the others.
缺血性心脏病是发达国家常见的致命疾病之一。由于健康人的信号扰动小于患者的信号扰动，熵测量可作为检测缺血的合适特征。
研究了四种基于熵的方法，包括直接使用心电图（ECG）信号、ECG信号的小波子带、提取的ST段以及从小波域中ST段的时频特征重建的信号，以区分健康个体和患者的ECG信号。我们将运动平板试验作为金标准，样本为40名根据医生初步诊断有缺血迹象的患者。
与其他方法相比，小波域中提出的技术在健康个体和患者之间产生了最大差异。该方法的特异性和敏感性分别为95%和94%。
基于小波子带的方法优于其他方法。
