- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
In this paper, several simple and efficient sign based normalized adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancelation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (LMS) based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio and computational complexity.
The electrocardiogram (ECG) is a graphical representation of hearts functionality and is an important tool used for diagnosis of cardiac abnormalities. In clinical environment during acquisition, the ECG signal encounters with various types of artifacts. The predominant artifacts present in the ECG includes: baseline wander (BW), power-line interference (PLI), muscle artifacts (MA) and motion artifacts (EM). These artifacts strongly affects the ST segment, degrades the signal quality, frequency resolution, produces large amplitude signals in ECG that can resemble PQRST waveforms and masks tiny features that are important for clinical monitoring and diagnosis. Cancelation of these artifacts in ECG signals is an important task for better diagnosis. The extraction of high-resolution ECG signals from recordings which are contaminated with background noise is an important issue to investigate. The goal of ECG signal enhancement is to separate the valid signal components from the undesired artifacts, so as to present an ECG that facilitates easy and accurate interpretation. Many approaches have been reported in the literature to address ECG enhancement using both adaptive and non-adaptive techniques [1–13], adaptive filtering techniques permit to the detect time varying potentials and to track the dynamic variations of the signals. In , Thakor et al. proposed an LMS based adaptive recurrent filter to acquire the impulse response of normal QRS complexes and then applied it for arrhythmia detection in ambulatory ECG recordings. The reference inputs to the LMS algorithm are deterministic functions and are defined by a periodically extended, truncated set of orthonormal basis functions. In such a case, the LMS algorithm operates on an instantaneous basis such that the weight vector is updated for every new sample within the occurrence based on an instantaneous gradient estimate. In a study, however, a steady state convergence analysis for the LMS algorithm with deterministic reference inputs showed that the steadystate weight vector is biased and thus the adaptive estimate does not approach the Wiener solution .
In this paper the problem of noise cancelation from ECG signal using sign based normalized adaptive filters, their block based versions are proposed and tested on real signals with different artifacts obtained from the MIT-BIH database. For this, the input and the desired response signals are properly chosen in such a way that the filter output is the best least squared estimate of the original ECG signal. Among the six algorithms, the NSRLMS performs better than the other. From the simulated results it is clear that these algorithms removes non-stationary noise efficiently. The proposed treatment provides high signal to noise ratio with less computational complexity. The computational complexity in terms of MACs and SNR contrast are presented in Tables 1–5. Hence the proposed NSRLMS, NSLMS, NSSLMS based adaptive filters and their block based versions are more suitable for wireless biotelemetry ECG systems.