Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database
Computers and Biomedical Research
Wavelet domain Wiener filtering for ECG denoising using improved signal estimate
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Design of Optimal Discrete Wavelet for ECG Signal Using Orthogonal Filter Bank
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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Electrocardiogram (ECG) classification systems have the potential to benefit from the inclusion of the automated measurement capabilities. The first stage in computerized processing of the ECG is beat detection. The accuracy of the beat detector is very important for the overall system performance hence there is a benefit in improving the accuracy. In present study we introduce the concept of Discrete Wavelet Transform which is suitable for the non stationary ECG signals as it has adequate scale values and shifting in time. As baseline Wander and different types of noise elimination are considered as classical problem in ECG analysis we present a wavelet based search algorithm in different scales for Denoising and subtractive procedure to isolate baseline wander from noisy ECG signal(to remove the noise). This algorithm is tested using the data record from MIT-BIH database and excellent results are obtained.