Design and Analysis of a Signal Denoising and Estimation System

• Published in: IOP conference series. Materials Science and Engineering 2021-02, Vol.1067 (1), p.12145
• Main Authors: Naji, Hassan Saadallah, Ahmed, Hanan Badeea
• Format: Article
• Language: English
• Subjects: Biomedical materials; Cardiovascular disease; Circulatory system; Electrocardiography; Heart diseases; Kalman filters; Noise; Noise reduction; Signal quality; White noise
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/1067/1/012145

Heart disease is one of the most common causes of death worldwide and electrocardiogram (ECG) signals are among the best tools currently available for diagonising heart disease by offering accurate data about heart performance and the circulatory system. Unfortunately, the linear-quadratic problem often arises, which is a serious problem affecting the estimation of any linear dynamic system’s immediate state due to disruption with white noise due to the linear employment of information about state and white noise corruption. This can theoritecally be addressed by using a Kalman filter as a recursive estimator. However, all biomedical applications that generate electrical signals are disposed to creating different types of noise with fixed coefficients filters, due to the random nature of these signals, and these can cause inaccuracies over time. In this paper, a double Kalman filter is suggested for ECG signal denoising and estimation. Additive White Noise added to the original ECG signal to form a noisy signal, and Kalman filter was used initially to improve the quality of the ECG signal and to offer high performance convergence while increasing the SNR. The second step involved employing Kalman filter to estimate the new denoised ECG signal during a given time period, as accuracy in time is very important in medical applications. The results were satisfactory, offering highly recognisable estimations.