ECG denoising using extended Kalman filter

Ouali, Mohammed Assam; Chafaa, Kheireddine; Ghanai, Mouna; Lorente, Luis Moreno; Rojas, Dolores Blanco

doi:10.1109/iccat.2013.6521994

Cited by 3 publications

(2 citation statements)

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“…The performance of the hybrid linearization (HL) algorithm is evaluated by two parameters, SNR (in dB) and MSE, as described in Eqs. (6) and (7), respectively [28]:…”

Section: Hybrid Linearization Methodsmentioning

confidence: 99%

“…For extending the functionality of the Kalman filter to nonlinear dynamic structures, a modified variant of it, the EKF, has been developed [26,27]. For a discrete, nonlinear system x k+1 = f (x k , w k ) and its observation y k = g(x k , v k ) , linear approximation close to a reference point (x k ,ŵ k ,v k ) can be formulated [28] as in Eq. (1):…”

Section: Extended Kalman Filtermentioning

confidence: 99%

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Electrocardiogram signal analysis for R-peak detection and denoising with hybrid linearization and principal component analysis

Kaur¹,

Rajni²

2017

Turk J Elec Eng & Comp Sci

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Abstract:In the areas of biomedical and healthcare, electrocardiogram (ECG) signal analysis is one of the major aspects of research. The accuracy in the detection of subtle characteristic features in ECG is of great significance. This paper deals with an algorithm based on hybrid linearization and principal component analysis for ECG signal denoising and R-peak detection. The ECG data have been taken from the MIT-BIH Arrhythmia Database for performance evaluation.The signal is denoised by applying the hybrid linearization method, which is an arrangement of the extended Kalman filter along with discrete wavelet transform, and then principal component analysis is employed to detect R waves and the QRS complex. The reported work has been implemented in the MATLAB environment for 25 different ECG records yielding 99.90% sensitivity, 99.97% positive predictivity, and a detection error rate of 0.120%. The achieved performance outperforms the recent research done in the area of interest.

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“…The performance of the hybrid linearization (HL) algorithm is evaluated by two parameters, SNR (in dB) and MSE, as described in Eqs. (6) and (7), respectively [28]:…”

Section: Hybrid Linearization Methodsmentioning

confidence: 99%

Section: Extended Kalman Filtermentioning

confidence: 99%