Review paper on denoising of ECG signal

Keshavamurthy, T G; Eshwarappa, M. N.

doi:10.1109/icecct.2017.8117941

Cited by 14 publications

(4 citation statements)

References 12 publications

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“…A conventional filter stage is implemented to cancel interference from the power-line; in this case, it is possible to use anything from a notch filter with a cut-off frequency of 50 or 60 Hz to an adaptive filter ( 108 ). Concerning ECG signals, since ECG is affected by diverse noise sources ( 109 , 110 ), the filtering process is a crucial stage that will influence systems analysis stages such as feature extraction and classification. Some techniques used are FIR and IIR filters ( 111 ), least mean square filters ( 112 ), wavelet transform ( 113 ), and Kalman filtering ( 114 ).…”

Section: Discussionmentioning

confidence: 99%

“…Some important information in ECG is represented by characteristics which are named features, and they are used for several purposes such as ECG filtering ( 123 ), ECG quality assessment ( 124 ), and disease classification ( 110 ). Many tools have been used to extract features from ECG signals, such as wavelet transform ( 113 ), PCA ( 125 ), statistics ( 126 ), analysis-based autocorrelation ( 127 ), Fourier transform ( 128 ), singular value decomposition SVD, variational mode decomposition VMD ( 129 ), Hilbert transform ( 130 ), and morphological methods ( 131 ).…”

Section: Discussionmentioning

confidence: 99%

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A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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“…Therefore, results achieved by KF for varying PLI frequency cases were not comparable with the existing adaptive filters. Keshavamurthy and Eshwarappa 35 removed various artifacts from ECG by using an EKF and singular value decomposition. The reference noise affects the filtering performance of LMS and RLS algorithms.…”

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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“…As ECG is primary source of any disorder that initiates from heart and a lot of work is already done on it but still it is famous issue for various researcher. By in large the ECG signal with its heart rate depicts the healthiness situation inside the heart [1].…”

Section: Introductionmentioning

confidence: 99%

Denoisation of ECG Signal using JADE and FAST ICA Comparison

Shah¹,

Shah²

2019

IJEW

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Electrocardiograph signal is effective tool in diagnosis of cardiac related diseases and plays an important role in biomedical research. To diagnose the cardiac disease, the signal must be recorded properly. The addition of artifacts like Persistent noises, Burst noises and their types play an important in making it difficult to interpret and analyses the electrocardiograph signal. A Blind source separation (BSS) related technique named Independent Component Analysis (ICA) is the right solution for it. In this paper, different ICA Algorithms like JADE, FAST are used to de-noise the ECG signal from the artifacts and a comparison between both is shown which is done on the basis Performance Index (PI) using a dsp ICALAB toolbox in MATLAB.

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Review paper on denoising of ECG signal

Cited by 14 publications

References 12 publications

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

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

Denoisation of ECG Signal using JADE and FAST ICA Comparison

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