Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Javad Delavar Matanaq; Hamed Danandeh Hesar; Mohammad Hadi Ahmadi fam
Volume 17, Issue 1 , May 2023, , Pages 11-20
Abstract
In recent years, model-based ECG processing algorithms have been successfully developed in various fileds of ECG processing. The calculation of ECG dynamic model (EDM) is a crucial step for these methods. The EDM parameters can be calculated using optimization algorithms. One of the popular optimization ...
Read More
In recent years, model-based ECG processing algorithms have been successfully developed in various fileds of ECG processing. The calculation of ECG dynamic model (EDM) is a crucial step for these methods. The EDM parameters can be calculated using optimization algorithms. One of the popular optimization methods in this field is an offline nonlinear method in which users have to manually select points on ECG signal in order to calculate EDM parameters. The objective function used in this algorithm is a complex function which is hard to optimize. In this paper an automatic optimization algorithm is proposed which uses meta-heuristic optimization algorithms to calculate EDM parameters. In this algorithm, we don’t need to select points manually. In addition, the objective function in this algorithm is broken in to several simple objective functions which makes the optimization more accurate. Meta-heuristic optimization algorithms may perform successfully on some optimization problems while failing on others. As a result, a specific algorithm cannot be considered the best optimizer for all optimization problems. For this reason, in this paper, the performances of nine popular meta-heuristic algorithms such as particle swarm optimization, artificial bee colony, cucko search, etc are investigated. In this paper, 200 ECG segments from different records of the MIT-BIH Normal Sinus Rhythm Database (NSRDB) have been selected for evaluation. The duration of each segment was 30 seconds. The EDM parameters for each segment were calculated using the aforemetinoned optimization algorithms. For evaluation, the similarities between the original signals and the synthetic ECG signals were inspected for each optimization algorithm. These synthetic signals were created using the calculated EDM parameters. The similarity results showed that the water evaporation optimization (WEO), teaching learning-based optimization (TLBO), and cucko’s search (CS) algorithms achived better results compared with other methods.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hamed Danandeh Hesar; Maryam Mohebbi
Volume 11, Issue 4 , February 2018, , Pages 275-289
Abstract
Marginalized particle extended Kalman filter (MP-EKF) takes advantage of both extended Kalman filter and particle filter frameworks to estimate nonlinear ECG dynamic models (EDMs) with reduced number of calculations in comparison to typical particle filters. However, due to existence of Kalman filter ...
Read More
Marginalized particle extended Kalman filter (MP-EKF) takes advantage of both extended Kalman filter and particle filter frameworks to estimate nonlinear ECG dynamic models (EDMs) with reduced number of calculations in comparison to typical particle filters. However, due to existence of Kalman filter framework inside MP-EKF, some limitations are introduced in implementation of MP-EKF especially in embedded systems with finite numerical accuracies. In this paper, for the first time, we propose a square root filtering strategy for MP-EKF which alleviates these restrictions using factorization. Typical or other square-root Kalman filters cannot be employed inside MP-EKF due to presence of minus operations in some equations of MP-EKF. However, our method can be implemented in MP-EKF structure. The proposed method can be used in any EDM previously used by EKF based frameworks in the field of ECG processing.
