ECG Signal De-noising Study Based On Wavelet Transform

The electrocardiogram (ECG) signal is a kind of biomedical signal which has been investigated and applied in clinical medicine earlier by people. Through research on ECG signals, we can know the physiological property of heart and can supply important foundation for cardiovascular disease diagnosis. Because of the faintness and randomness of the ECG signals, the acquired ECG signals usually corrupted with noises, so we meet some difficulties in ECG signals' acquisition. In order to eliminate the interference in ECG signals, domestic and international researchers have proposed many methods, among them the signal de-noise methods based on wavelet transform are researched actively in recent years. The really point in ECG signals de-noise with wavelet transform is how to separate the signals and the noises after wavelet transform, that is, how to select a optimal threshold, so the focus in wavelet de-noise is threshold de-noising algorithm.After analyzing the advantages and shortages of existing wavelet de-noising methods of ECG signals and many better threshold de-noising algorithms, aiming at the shortage of existing threshold de-noising methods, this paper proposes a new adaptive threshold algorithm use ECG signals based on wavelet transform. Through estimation of the SNR of the noisy signals, this algorithm can suppress the noises in the ECG signals with adjustable threshold. Experimental results show that this algorithm can effectively eliminate the noises in the ECG signals and the SNR are greatly improved. With the same conditions, this paper compared the new results with traditional threshold algorithm results; experiment results show that the effect of adaptive threshold algorithm is better than traditional threshold algorithm. Meanwhile, this algorithm has a peculiar advantage for non-steady ECG signals' de-noising and protect ECG signals' characteristic perfectly.