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The electrocardiogram (ECG) signals bear fundamental information for making decisions about different kinds of heart diseases. Therefore, many efforts were made during decades to extract features of heartbeats via ECG records with high accuracy and efficiency using different strategies and methods. In this paper, we solve the problem in discrete-time state-space using a novel q-lag unbiased finite impulse response (UFIR) smoother, which we adapt to the ECG signal shape via the time-varying optimal averaging horizon. It is shown that the adaptive UFIR smoother performs better in applications to ECG signals than the standard techniques such as the Savitsky-Golay, wavelet-based, low-pass, band-pass, notch, and median filters. Applications are given for the PhysioBank data benchmark, which contains several records taken from different databases such as the MIT-BIH Arrhythmia (MITDB). A complete statistical analysis is provided via normalized histograms and statistical classifiers. It is shown in a comparison with other methods that the adaptive UFIR smoother has a higher accuracy in denoising, features extraction, and features classification for ECG records with normal rhythm and atrial fibrillation (AF).INDEX TERMS Biomedical signal processing, electrocardiogram (ECG) signal denoising, ECG features extraction, unbiased finite impulse response (UFIR) filtering.

Wireless sensor network (WSN) technologies are used to provide mobile object tracking due to advantages such as mobility, scalability, and flexibility. However, wireless interaction between the network nodes is often accompanied by missing data, which requires robustness from the estimator. This paper develops an iterative distributed unbiased finite impulse response (dUFIR) filtering algorithm for object tracking via WSNs with consensus on estimates and shows that it has higher robustness than the distributed Kalman filter (dKF). The tracking problem is viewed as a real-time position estimation of an unmanned ground vehicle (UGV). The extensive simulations are provided using real sensor parameters and measurements of the UGV position with missing data. Two different scenarios are considered when: 1) each sensor is capable of measuring the UGV position and 2) sensors have different time-varying noise variances, as in practical WSNs. The higher robustness of the dUFIR against the dKF is demonstrated under diverse operation conditions. INDEX TERMS Distributed wireless sensor network, object tracking, unbiased FIR filter, Kalman filter, robustness, consensus on estimates.

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