An Intelligent Adaptive Filter for Elimination of Power Line Interference From High Resolution Electrocardiogram

Razzaq, Nauman; Sheikh, Shafa-at Ali; Zaidi, Tahir

doi:10.1109/access.2016.2548362

Cited by 39 publications

(20 citation statements)

References 16 publications

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“…The authors have retracted this article [1] because Figures 1, 2, 3, 4, 7B, 17 and Tables 1 and 2 were duplicated and additional content was adapted from a previously published article by Razzaq et al [2], without the permission of the authors of [2] . All authors of [1] agree to this retraction.…”

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confidence: 99%

RETRACTED ARTICLE: Removing Artifacts from Raw Electrocardiogram Signals Using Adaptive Filter in State Space

Khan

Shafi

2019

Circuits Syst Signal Process

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confidence: 99%

RETRACTED ARTICLE: Removing Artifacts from Raw Electrocardiogram Signals Using Adaptive Filter in State Space

Khan

Shafi

2019

Circuits Syst Signal Process

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“…36,37 Researchers mitigated impulsive, BW, and PLI from degraded ECG by opting SSRLS. [38][39][40][41][42][43][44] It is very effective in mitigating 50 Hz sinusoidal noise irrespective of information about adaptive filter parameters but at a cost of high complexity.…”

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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“…PLI is a significant source of noise in the frequency range of 50-60 Hz. State Space Recursive Least Square Adaptive Filter (SSRLS), ANF, and Fast Fourier Transform (FFT) Filter-based denoising of power-line interference is performed by [26][27][28], [31] and unknown external disturbances are removed by adaptive control schemes in [32]. Other external white noise can be removed by Hardware Descriptive Language (HDL)-based Finite Impulse Response (FIR) filters and NN-based Denoising Autoencoders (DAE) [22][23][24], [29].…”

Section: B Stage2: Denoisingmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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An Intelligent Adaptive Filter for Elimination of Power Line Interference From High Resolution Electrocardiogram

Cited by 39 publications

References 16 publications

RETRACTED ARTICLE: Removing Artifacts from Raw Electrocardiogram Signals Using Adaptive Filter in State Space

RETRACTED ARTICLE: Removing Artifacts from Raw Electrocardiogram Signals Using Adaptive Filter in State Space

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

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

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