Electrocardiography (ECG) is the most popular non-invasive method for generating an Electrocardiogram which contains some very interesting information about the electrical and myographic activities of the heart. It is a graph of voltage vs. time of the electrical activity of the heart using electrodes connected on the skin in various configurations. Due to the noninvasive nature of ECG and also due to capacitive or inductive coupling in this electrical circuit for ECG acquisition or electromyographic noises due to muscles adjacent to heart there is usually significant noises present in a typical ECG which makes it harder to analyze. There are many methods for denoising ECGs. In this paper an adaptive unscented Kalman filter, where the measurement noise covariance matrix is varied adaptively, is used for denoising acquired discrete ECG signals. The filtered output as well as the improvement of SNR is compared with other existing denoising frameworks like discrete wavelet transform and digital filters and extended Kalman filter, and unscented Kalman filter. The Adaptive Unscented Kalman Filter performed better than the aforementioned existing filtering algorithms in terms of maximum output SNR and MSE computed using Monte Carlo simulation.

dec

Gwalior, India

https://ieeexplore.ieee.org/document/10119404/