https://users.wpi.edu/~ted/full_text/2002clancy_morin_jek.pdf

Sampling, noise-reduction and amplitude estimation issues in surface electromyography

ECG baseline wander and powerline interference reduction using nonlinear filter bank

Critical government and industry sectors (such as law enforcement, transportation, communication, and finance) are growing increasingly dependent on Global Navigation Satellite Systems (GNSS) for positioning, navigation, and timing. At the same time, the availability of low-cost GNSS jamming devices are presenting a serious threat to GNSS and increasing the likelihood of outages to infrastructures relying on GNSS. The attacks range from malicious parties intentionally jamming GNSS signals within a targeted geographical region to uninformed users causing accidental interference. This paper is an overview of different approaches adopted to date to mitigate GNSS disruption caused by intentional and unintentional jamming. The first approach outlined in this paper is the use of inertial systems to aid GNSS. The second and third approaches are the filtering of jamming/interference in the spatial and time-frequency domains, respectively. The fourth approach is vector tracking of GNSS signals in the receiver.

Protecting GNSS Receivers From Jamming and Interference

The proper analysis and characterization of atrial fibrillation (AF) from surface electrocardiographic (ECG) recordings requires to cancel out the ventricular activity (VA), which is composed of the QRS complex and the T wave. Historically, for single-lead ECGs, the averaged beat subtraction (ABS) has been the most widely used technique. However, this method is very sensitive to QRST wave variations and, moreover, high-quality cancelation templates may be difficult to obtain when only short length and single-lead recordings are available. In order to overcome these limitations, a new QRST cancelation method based on adaptive singular value cancelation (ASVC) applied to each single beat is proposed. In addition, an exhaustive study about the optimal set of complexes for better cancelation of every beat is also presented for the first time. The whole study has been carried out with both simulated and real AF signals. For simulated AF, the cancelation performance was evaluated making use of a cross-correlation index and the normalized mean square error (nmse) between the estimated and the original atrial activity (AA). For real AF signals, two additional new parameters were proposed. First, the ventricular residue (VR) index estimated the presence of ventricular activity in the extracted AA. Second, the similarity (S) evaluated how the algorithm preserved the AA segments out of the QRST interval. Results indicated that for simulated AF signals, mean correlation, nmse, VR and S values were 0.945 ± 0.024, 0.332 ± 0.073, 1.552 ± 0.386 and 0.986 ± 0.012, respectively, for the ASVC method and 0.866 ± 0.042, 0.424 ± 0.120, 2.161 ± 0.564 and 0.922 ± 0.051 for ABS. In the case of real signals, the mean VR and S values were 1.725 ± 0.826 and 0.983 ± 0.038, respectively, for ASVC and 3.159 ± 1.097 and 0.951 ± 0.049 for ABS. Thus, ASVC provides a more accurate beat-to-beat ventricular QRST representation than traditional techniques. As a consequence, VA cancelation is optimized and the AA can be extracted more precisely. Finally, the study has proven that optimal VA cancelation is achieved when a number between 20 and 30 complexes is selected following a correlation-based strategy.

Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms

In a Global Navigation Satellite System (GNSS) receiver the presence of detection and mitigation units, capable of reducing the impact of disturbing signals, can extremely enhance the position accuracy. However the presence of such units is usually limited to professional receivers that dispose of additional computational power that can be used for interference detection and mitigation. In this paper the two-pole notch filter, the natural extension of the one-pole notch filter analyzed in [1], is proposed as computationally effective solution for interference detection and mitigation. The notch filter structure and the adaptive algorithm employed for tracking the disturbing signal are analyzed, and an interference detection unit, based on the adaptive algorithm convergence, is proposed. The two-pole notch filter coupled with the detection unit is used as elementary block for the design of a multi-pole notch filter that can efficiently mitigate more than one CW interference. Theoretical and simulative analyses show the feasibility and the good performance of the proposed method.

Two-Pole and Multi-Pole Notch Filters: A Computationally Effective Solution for GNSS Interference Detection and Mitigation

Investigates adaptive digital notch filters for the elimination of powerline noise from biomedical signals. Since the distribution of the frequency variation of the powerline noise may or may not be centered at 60 Hz. Three different adaptive digital notch filters are considered. For the first case, an adaptive FIR second-order digital notch filter is designed to track the center frequency variation. For the second case, the zeroes of an adaptive IIR second-order digital notch filter are fixed on the unit circle and the poles are adapted to find an optimum bandwidth to eliminate the noise to a pre-defined attenuation level. In the third case, both the poles and zeroes of the adaptive IIR second-order filter are adapted to track the center frequency variation within an optimum bandwidth. The adaptive process is considerably simplified by designing the notch filters by pole-zero placement on the unit circle using some suggested rules. A constrained least mean-squared algorithm is used for the adaptive process. To evaluate their performance, the three adaptive notch filters are applied to a powerline noise sample and to a noisy EEG as an illustration of a biomedical signal. >

R.E. Barr

Papers

Adaptive digital notch filter design on the unit circle for the removal of powerline noise from biomedical signals