Biomedical signals produced by mobile sensors usually carry various noises. This poses great challenges for the subsequent signal processing and disease analysis. Thus, noise removal becomes an important step of signal processing. This research proposes a noise reduction algorithm which can be applied to noisy ECG (electrocardiogram) signals to obtain a higher signal-to-noise ratio (SNR) for further processing. The proposed algorithm utilises wavelet transform and dynamic thresholding to reduce specific types of noise embedded in raw ECG signals. To prove the efficiency of the proposed algorithm, we employ a half-hour-long real ECG signal and add different types of noise for the evaluation of the proposed algorithm. We also compare the results obtained using different families of wavelets and different decomposition levels. The experimental results show that the proposed algorithm is able to produce a higher SNR in the output signal than that in the raw test signals.
|Title of host publication||Emerging Trends in Neuro Engineering and Neural Computation|
|Editors||Asim Bhatti, Kendall H. Lee, Hamid Garmestani, Chee Peng Lim|
|Place of Publication||Singapore|
|Number of pages||14|
|Publication status||Published - 2017|
Pandit, D., Zhang, L., Liu, C., Aslam, N., Chattopadhyay, S., & Lim, C. P. (2017). Noise Reduction in ECG Signals Using Wavelet Transform and Dynamic Thresholding. In A. Bhatti, K. H. Lee, H. Garmestani, & C. P. Lim (Eds.), Emerging Trends in Neuro Engineering and Neural Computation (pp. 193-206). Springer Singapore. https://doi.org/10.1007/978-981-10-3957-7_10