Ischemia detection by electrocardiogram in wavelet domain using entropy measure

Background: ischemic heart disease is one of the common fatal diseases in advanced countries. because signal perturbationin healthy people is less than signal perturbation in patients, entropy measure can be used as an appropriatefeature for ischemia detection.methods: four entropy-based methods comprising of using electrocardiogram (ecg) signal directly, wavelet subbandsof ecg signals, extracted st segments and reconstructed signal from time-frequency feature of st segments inwavelet domain were investigated to distinguish between ecg signal of healthy individuals and patients. we used exercisetreadmill test as a gold standard, with a sample of 40 patients who had ischemic signs based on initial diagnosis of medical practitioner.results: the suggested technique in wavelet domain resulted in the highest discrepancy between healthy individualsand patients in comparison to other methods. specificity and sensitivity of this method were 95% and 94% respectively.conclusions: the method based on wavelet subbands outperformed the others.