De-noising of Electrocardiogram (ECG) with Adaptive Filter Using MATLAB

Makwana, Gaurav; Gupta, Lalita

doi:10.1109/csnt.2015.126

Cited by 11 publications

(8 citation statements)

References 5 publications

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“…12 The least mean square (LMS) algorithm fits to this group. 13,14 Another ANF of sharp resolution is reported for mitigating PLI from ECG signal. 15 The fast Fourier transform is done on the input signal in the proposed method.…”

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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“…The T wave is ventricular repolarization and it is the small wave after QRS complex. Though in Figure 1 an ECG signal is shown the initial spike is the P wave followed by a small spike and longer spike representing QRS complex and afterwards a T wave forming the ST signal [4][5][6][7][8]. If such an ECG signal is corrupted by 50 Hz noise, it can be represented by Figure 2.…”

Section: Figure 1 Electrocardiogram Signalmentioning

confidence: 99%

“…By suitable design of digital filter for power line noise such as 50 Hz or 60 Hz depending on the frequency, artifacts can be removed. A suitable digital filter can be designed and the corrupted ECG signal can be passed through the digital filter [6][7][8][9] to recover the desired ECG signal. Several research papers have been presented and surveyed.…”

Section: Introductionmentioning

confidence: 99%

Medical Signal Processing via Digital Filter and Transmission Reception Using Cognitive Radio Technology

Premkumar¹,

Sathiyapriya²,

Arun³

et al. 2022

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This research paper provides a viable solution for processing noise affected Electrocardiogram (ECG) signal via digital filter and transmission of ECG signal and reception via cognitive radio (CR) technology. Health assessment signals such as ECG signal, Electroencephalogram (EEG) signal, Electromyogram (EMG) signal are vital for diagnosis and rehabilitation of human welfare among which ECG attains prime importance due to its information on heart functioning. However, electrocardiogram signals are prone to addition of noise such as power line noise 50 Hz mainly due to improper shielding which can lead to wrong interpretation, incorrect diagnosis and at times will eventually lead to loss of human life. On combining signal processing into medical applications misconceptions can be eliminated and diagnosis can be done effectively through a designed digital filter. Effect of noise can be cancelled in an ECG signal and by using cognitive radio technology ECG information can be transmitted to a medical physician mobile terminal for remedial measures relating to medical treatment. Simulation results are shown in matrix laboratory (MATLAB) for cancelling noise in an ECG signal having noise using a digital filter which is designed represented by its transfer function. Also, ECG signal is transmitted and received in a CR system where the metric of probability of error is obtained which can be useful for signal processing fraternity.

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“…Such filters operate like a servo system with negative feedback, which minimizes a given error function while optimizing the filter coefficients and extracting the noise. Usually, these filters minimize the output signal power by minimizing the mean squared error (MSE), and such an approach of iteratively modifying the filter coefficients using the MSE is referred to as a least-mean-square (LMS) algorithm [24][25][26]. Various modifications of a recursive least-squares algorithm [27], block-based time-frequency domain adaptive filters [28], or cascaded multistage adaptive structures [29] present improved efficiency.…”

Section: Introductionmentioning

confidence: 99%

Common-Mode Driven Synchronous Filtering of the Powerline Interference in ECG

2022

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Powerline interference (PLI) is a major disturbing factor in ground-free biopotential acquisition systems. PLI produces both common-mode and differential input voltages. The first is suppressed by a high common-mode rejection ratio of bioamplifiers. However, the differential PLI component evoked by the imbalance of electrode impedances is amplified together with the diagnostic differential biosignal. Therefore, PLI filtering is always demanded and commonly managed by analog or digital band-rejection filters. In electrocardiography (ECG), PLI filters are not ideal, inducing QRS and ST distortions as a transient reaction to steep slopes, or PLI remains when its amplitude varies and PLI frequency deviates from the notch. This study aims to minimize the filter errors in wide deviation ranges of PLI amplitudes and frequencies, introducing a novel biopotential readout circuit with a software PLI demodulator–remodulator concept for synchronous processing of both differential-mode and common-mode signals. A closed-loop digital synchronous filtering (SF) algorithm is designed to subtract a PLI estimation from the differential-mode input in real time. The PLI estimation branch connected to the SF output includes four stages: (i) prefilter and QRS limiter; (ii) quadrature demodulator of the output PLI using a common-mode driven reference; (iii) two servo loops for low-pass filtering and the integration of in-phase and quadrature errors; (iv) quadrature remodulator for synthesis of the estimated PLI using the common-mode signal as a carrier frequency. A simulation study of artificially generated PLI sinusoids with frequency deviations (48–52 Hz, slew rate 0.01–0.1 Hz/s) and amplitude deviations (root mean square (r.m.s.) 50–1000 μV, slew rate 10–200 μV/s) is conducted for the optimization of SF servo loop settings with artificial signals from the CTS-ECG calibration database (10 s, 1 lead) as well as for the SF algorithm test with 40 low-noise recordings from the Physionet PTB Diagnostic ECG database (10 s, 12 leads) and CTS-ECG analytical database (10 s, 8 leads). The statistical study for the PLI frequencies (48–52 Hz, slew rate ≤ 0.1 Hz/s) and amplitudes (≤1000 μV r.m.s., slew rate ≤ 40 μV/s) show that maximal SF errors do not exceed 15 μV for any record and any lead, which satisfies the standard requirements for a peak ringing noise of < 25 μV. The signal-to-noise ratio improvement reaches 57–60 dB. SF is shown to be robust against phase shifts between differential- and common-mode PLI. Although validated for ECG signals, the presented SF algorithm is generalizable to different biopotential acquisition settings via surface electrodes (electroencephalogram, electromyogram, electrooculogram, etc.) and can benefit many diagnostic and therapeutic medical devices.

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De-noising of Electrocardiogram (ECG) with Adaptive Filter Using MATLAB

Cited by 11 publications

References 5 publications

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

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

Medical Signal Processing via Digital Filter and Transmission Reception Using Cognitive Radio Technology

Common-Mode Driven Synchronous Filtering of the Powerline Interference in ECG

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