Electrocardiogram (ECG) recordings are very often contaminated by residual power-line (PL) interference [1, 2, 3, 4], base-line drift [5, 6, 7], artefacts and EMG disturbances due to involuntary muscle contractions (tremor) of the patient [8, 9, 10, 11, 12]. The base-line drift resulting from electrochemical processes at the electrode-to-skin barrier [7] is a typical low-frequency noise that distorts the susceptible ST segment [6, 13]. Interference and tremor have overlapping frequency bands. Therefore, many algorithms are aimed at their common suppression [14, 15, 16, 17] in order to provide an accurate automatic delineation of the ECG wave boundaries [18].
Specific digital filter for PL interference cancellation, called subtraction procedure, has been developed some two decades ago and permanently improved later on [19]. It does not affect the signal frequency components around the rated PL frequency. Moving averaging is applied on linear segments of the signal (usually found in the PQ and TP intervals, but also in sufficiently long straight parts of the R and T waves) to remove the interference components. They are stored as phase locked corrections and further subtracted from the signal wherever non-linear segments are encountered, e.g. QRS complexes or other high and steep waves. Several criteria for linearity have been tested and implemented depending on the purpose. In general, they are based on the second difference of the signal (mathematical evaluation of the curvature).
Filtering out the tremor is
a priori partially successful since it has a relatively wide spectrum, which covers the useful ECG frequency band. One of the first recommendations for ECG instruments [20] suggests a low-pass filter with minimum 35 Hz cut-off. However, in this way the amplitudes of sharp QRS waves are reduced. The moving averaging (comb filter with linear phase characteristic) gives similar results [21].The time averaging is one of the classic methods for ECG noise suppression. It is based on the assumption that the ECG signal is repeatable [22]. As the variability of the ECG morphology is also suppressed, some authors [23, 24] proposed adaptive triggered filtering. Another way to preserve the ECG individuality is to reduce the number of the averaged beats but thus the effect of noise suppression is decreased. The variable ECG morphology, which is related to the respiration, may be compensated in multilead recordings by spatial transformations [24]. However, they can not be applied in the case of single channel time alignment.
Kotas [25] published projective filtering of time-aligned ECG beats. This is an extension of time averaging, which preserves the variability of the beat morphology. The method employs the rules of principal component analysis for the desired ECG reconstruction and aims to retain to some extent the deviations from the averaged component changes, in the same time, rejecting deviations caused by noise. However, the nonlinear projective filtering is computationally intensive and is known to be sensitive to noise changes.
Adaptive filtration has been also attempted but with limited success because the QRS complexes disturb the adaptation process up to the end of the T-waves [14]. Luo and Tompkins [8] obtained faster convergence using additional EMG channel as reference input. Bensadoun
et al [9] proposed a multidimensional method but the reduction of sharp Q-waves amplitudes is too high.Clifford
et al [26] reported a model-based filtering method. P-, Q-, R-, S- and T-waves are defined by a Gaussian with three parameters: amplitude, width and relative position with respect to the R-peak. T-wave is described by T+ and T- because of its asymmetric turning point. Non-linear least-squares optimization is applied to fit this ECG model to the observed signal. The authors present one cleanly recorded P-QRS-T interval superimposed by electrode motion noise. The result shows almost total noise suppression but also significant waveform distortions. However, the locations of the wave peaks match the uncorrupted signal; the errors around the isoelectric line and the S-T segment are negligible. Thus, much of the clinical information of the beats is captured after the noise removal. Nevertheless, the error tolerance has to be tested over a set of databases, since non-parameterized beat will be considered to be an artefact, while some artefacts may closely resemble a known beat. An important advantage of the method is the almost total elimination of series of pulses (artefacts).Sameni
et al [27] proposed a nonlinear Bayesian filtering framework consisting of Extended Kalman Filter (EKF), Extended Kalman Smoother (EKS) and Unscented Kalman Filter (UKF) as suboptimal filtering schemes. They are based on modified dynamic ECG model thus utilizing a priori information about the underlying dynamics of ECG signals. Recordings taken from the MIT-BIH Normal Sinus Rhythm Database are …
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