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Biomedical signal processing is the process of extracting clinically useful information from biosignals for the aspect of medical procedures. Biomedical signals like electrocardiogram wave commonly change their statistical properties over time tending to be nonstationary. For analyzing this kind of signal wavelet transforms are a powerful tool. The design of good wavelet for cardiac signal is discussed from the perspective of orthogonal filter banks. In this paper two wavelets are designed and evaluated based on perfect reconstruction conditions and the filters are perfectly matched. ECG records from the MIT-BIH Arrhythmia database are chosen for processing. In the first step, the filters are designed by reparametrization of filter coefficients by thetas for the proposed wavelets W1 and W2. ECG signal is decomposed to three levels and then reconstructed. From the reconstructed signal the error signal is found and it is compared with other wavelets available in the literature such as db4, bior4.4 and bior6.8. The reconstructed results show the potential of the method. The wavelet W2 gives the maximum error of 1.8*10-11 to 2.73*10-11 that is better than all other wavelets already exists in the literature. Baseline wandering is one of the noise artifacts that affect ECG signals. Automatic detection of the QRS complex can be affected unless the baseline wander is removed Thus baseline wander removal and QRS detection algorithm are effectively done using proposed wavelets and db4, bior4.4 and bior6.8 for ECG records from the MIT-BIH Arrhythmia database and their performance are compared.