Wavelet time entropy as a T-wave alternans detector in presence of noise

Ventricular Fibrillation (VF) is one of the most common causes of sudden cardiac death. Clinical studies were conducted to link some ECG features to the occurrence of VF. It was found that T-wave Alternans (TWA) is one of the most promising risk prediction indicators for VF. The detection of TWA will assist physicians in taking preventive measures to avoid VF.;ECG signals are not stationary signals, and are mixed with noise. Moreover, TWA magnitude is in the millivolt level. Research is ongoing to increase the sensitivity and specificity of the TWA detection algorithms. Wavelet transform based algorithms were previously developed to detect TWAs.;In this thesis, the wavelet time entropy (WTE) is proposed as a new biomedical indicator to detect alternations in T-wave shape and amplitude. Any change in the T-wave morphology is expected to be reflected in its WTE value. The outcome of this research is used to complement existing work for the purpose of sensitivity and specificity improvement VF prediction.;A total of 300 simulated and recorded life electrocardiogram (ECG) signals each were used to test the sensitivity and specificity of the algorithm before and after using WTE. The tests results showed that the increase in the algorithm sensitivity due to using WTE exceeds the decrease in specificity, which makes WTE a beneficial predictor compared to other research methods. The maximum increase in sensitivity was 21% while the corresponding decrease in specificity was only 3%, with a 4% average increase in sensitivity. The increase in sensitivity was noticed mainly for problematic cases when the TWA amplitude and the SNR are low. In the final part of the thesis we used the MRA with the sliding window technique. We found that the specificity for the MRA is 100%, while the increase in the sensitivity compared with RA is up to 20%.