Denoising and Features Extraction of ECG Signals using Unbiased FIR Estimation Techniques

The electrocardiogram (ECG) signals bear fundamental information for medical experts to make decisions about heart diseases. Therefore, in the past decades the scientific community has made great efforts to develop methods for the heartbeat features extraction via ECG records with the highest accuracy and efficiency using different strategies. It should be noted that noise and artifacts induced by external factors make it difficult to learn specific patterns of ECG signals, which play an important role to find abnormalities. Using filtering techniques such as the unbiased finite impulse response FIR (UFIR) filtering approach promises better results. Aimed at extracting the features with the highest accuracy, in this dissertation, we have designed and applied to ECG signals the adaptive UFIR filter and smoother. We also compared the proposed technique with the traditional method such as UFIR predictors, standard filters (e.g. low-pass filter wavelet filters, and Savitsky-Golay filter. Base on extensive experimental studies, we show that the UFIR method outperforms the above mentioned techniques in applications to ECG signals in terms of accuracy and efficiency.