ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

Abstract

Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p<0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

“…7. Two levels DWT decomposition [1] For the reconstruction process, the reversed methodology can simply be applied to the signal, so the detail and approximation components at the larger scale are fed back through the low and high pass filters respectively. Before that, the coefficients have to be upsampled by 2, see Figure 8 [25,27].…”

Denoising and Features Extraction of ECG Signals in State Space Using Unbiased FIR Smoothing

“…To provide efficient denoising and features extractions, in this subsection we model an ECG signal in discrete-time statespace. We represent an ECG signal on a horizon [m, n] of N points, from m = n − N + 1 to n, where n is the discrete time index, with a degree polynomial as shown in [50]. The inherent ECG noise is still not well understood and its incorrect description may cause estimation errors.…”

“…A solution to the optimization problem (12) has been provided in our early paper together with an algorithm [50], which we will further use. It has been found out in [50] that an optimal horizon N opt = 21 serves for the 2-degree polynomial corresponding to three states, K = 3, and database [51] exploited in this paper.…”

“…Specified N opt and N apt for the database used as shown in [50], the horizon N opt = 21 is applied beyond the QRS complex.…”

“…Referring to the first results obtained in [49], [50], where the batch UFIR smoothing filter has demonstrated a better performance than several other well-recognized estimators, in this paper we employ and develop an iterative UFIR smoother in state space. An objective is to increase accuracy of the features extraction and fiducial points detection.…”