A study of recursive least squares (RLS) adaptive filter algorithm in noise removal from ECG signals

Mugdha, Arya Chowdhury; Rawnaque, Ferdousi Sabera; Ahmed, Mosabber Uddin

doi:10.1109/iciev.2015.7333998

Cited by 31 publications

(12 citation statements)

References 12 publications

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“…Different variants of the LMS algorithm are investigated and compared. [19][20][21][22] Further, Mugdha et al 23 successfully suppressed PLI by testing and comparing the RLS algorithm with LMS. Biswas and Maniruzzaman 24 compared the NLMS adaptive filter performance and RLS adaptive filter with notch filter in both the time and frequency domain for PLI removal from ECG.…”

Section: Related Workmentioning

confidence: 99%

Extended Kalman Filter-Based Power Line Interference Canceller for Electrocardiogram Signal

et al. 2022

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Cardiac diseases constitute a major root of global mortality and they are likely to persist. Electrocardiogram (ECG) is widely opted in clinics to detect countless heart illnesses. Numerous artifacts interfere with the ECG signal, and their elimination is vital to allow medical specialists to acquire valuable statistics from the ECG. The utmost artifact that is added to the ECG signal is power line interference (PLI). Numerous filtering methods have been employed in the literature to eliminate PLI from noisy ECG. This article proposes an extended Kalman filter (EKF)-based adaptive noise canceller (ANC) that comprises PLI frequency as a distinct model parameter. Thus, it is capable of tracking PLI with drifting frequency. The proposed canceller's performance is compared with state-space recursive least squares (SSRLSs) filter-based PLI canceling. The evaluation is carried out for four cases of PLI, that is, PLI with known amplitude and frequency, PLI with unknown amplitude and frequency, PLI with drifting amplitude and frequency, and PLI removal from a real-time ECG recording. The samples of the Massachusetts Institude of Technology (MIT)-Boston's Beth Israel Hospital (BIH) arrhythmia database are considered for the first three cases, whereas, for the fourth case, real ECG signal is taken from armed forces institude of cardiology, the national institude of heart diseases (AFIC/NIHD), Pakistan. Mean square error, frequency spectrum, and noise reduction are selected as performance metrics for comparison. Simulation results depict that the presented EKF-based ANC system outperforms the SSRLS-based ANC system and effectively eliminates PLI from ECG under all four investigated scenarios.

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Section: Related Workmentioning

confidence: 99%

Extended Kalman Filter-Based Power Line Interference Canceller for Electrocardiogram Signal

et al. 2022

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“…[56]- [58], adaptive filters with recursive optimal adaptation (RLS, FTF, etc.) [59]- [61], Kalman filtering [62], adaptive linear neuron ADALINE [63], etc., soft-computing methods (e.g. artificial neural networks, fuzzy logic, fuzzy-neural networks (e.g.…”

Section: Description Of Adaptive Cancelling Systemmentioning

confidence: 99%

A Comparison Between Novel FPGA-Based Pad Monitoring System Using Ballistocardiography and the Conventional Systems for Synchronization and Gating of CMRI at 3 Tesla: A Pilot Study

et al. 2020

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This pilot pre-clinical study demonstrates the applicability of a new type of pneumatic cardiac triggering (PCT) for cardiac imaging. The pilot research compares the novel FPGA-based pad monitoring system for cardiac triggering using ballistocardiography (BCG) with conventional systems based on electrocardiography (ECG) and photoplethysmography (PPG). The implemented system enables cardiac triggering without the need to fix the sensors to the patient's body. This unique approach has the potential to reduce the preparation time for examination and the examination itself and to increase patient's comfort. The pilot pre-clinical study was conducted on 10 subjects at the Siemens Prisma 3T MRI Scanner within the

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“…To suppress DPI and MPI, an adaptive filtering algorithm was adopted [22][23][24]. The direct wave signal received on the reference channels is used as reference signals to cancel the DPI and MPI on the monitoring channels.…”

Section: Influence Of Reference Channel Interference On Dpi and Mpi Smentioning

confidence: 99%

Passive Detection of Moving Aerial Target Based on Multiple Collaborative GPS Satellites

et al. 2020

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Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance–velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite.

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A study of recursive least squares (RLS) adaptive filter algorithm in noise removal from ECG signals

Cited by 31 publications

References 12 publications

Extended Kalman Filter-Based Power Line Interference Canceller for Electrocardiogram Signal

Extended Kalman Filter-Based Power Line Interference Canceller for Electrocardiogram Signal

A Comparison Between Novel FPGA-Based Pad Monitoring System Using Ballistocardiography and the Conventional Systems for Synchronization and Gating of CMRI at 3 Tesla: A Pilot Study

Passive Detection of Moving Aerial Target Based on Multiple Collaborative GPS Satellites

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