Kaimin Yu, Lei Feng, Yunfei Chen, Minfeng Wu, Yuanfang Zhang, Peibin Zhu, Wen Chen, Qihui Wu, Jianzhong Hao
Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise...
December 8, 2023: Computers in Biology and Medicine