Hybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation

Rakshit, Manas; Das, Susmita

doi:10.1049/iet-smt.2018.5060

Cited by 11 publications

(7 citation statements)

References 32 publications

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“…In [119 ], another version of the SSA procedure (GSLPF) is also considered for denoising. In [122 ], a dictionary learning based sparse representation (DLSR) framework is presented, where a frequency‐localised sparse representation is used to remove BW and PLI. For AWGN and MA, a time‐localised sparse representation is used for denoising ECG signals.…”

Section: Techniques For Ecg Noise Removalmentioning

confidence: 99%

Review of noise removal techniques in ECG signals

Chatterjee

Thakur

Yadav

et al. 2020

IET signal process.

194

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Section: Techniques For Ecg Noise Removalmentioning

confidence: 99%

Review of noise removal techniques in ECG signals

Chatterjee

Thakur

Yadav

et al. 2020

IET signal process.

194

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“…The fourth group utilises the sparsity property of ECG for sparse optimisation to denoise ECG signals. Here, the signal is split into segments, and every segment is broken into sparse parts and residues to denoise ECG signals [7].…”

Section: Noise In Ecg Signalsmentioning

confidence: 99%

“…The fifth group uses Bayesian filters to introduce changes in the conventional dynamic ECG model of Kalman filter to denoise ECG signals. The last group is defined as hybrid systems combining various denoising methods previously reported in literature [7]. It has been shown that combined approaches including filtering techniques like conventional filtering [8] and adaptive filtering [9] can offer an improved reduction of noise in ECG signals.…”

Section: Noise In Ecg Signalsmentioning

confidence: 99%

“…It has been shown that combined approaches including filtering techniques like conventional filtering [8] and adaptive filtering [9] can offer an improved reduction of noise in ECG signals. However, the results obtained using hybrid models, showed that these filtering techniques introduce different kinds of waveform distortion [7]. Therefore, a robust and reliable method to extract the noises present in ECG recordings becomes important [2].…”

Section: Noise In Ecg Signalsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of a combined approach for denoising ECG measurements using unconventional sensing technology.

Dore¹,

Aviles-Espinosa²,

Rendon-Morales³

2022

Computing in Cardiology Conference (CinC)

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The human electrocardiogram is a key indicator to examine the electrical heart condition and function. A common problem is that its recording can be impacted by external sources of noise. In this paper, we focus on the reduction of powerline interference in ECG measurements using a combined approach. We discuss the typical noise types affecting ECG recordings and describe several filtering techniques currently used to denoise ECG measurements. We propose an approach that combines unconventional ECG electric field sensors with Wiener filtering to improve the ECG denoising performance. Validation tests are performed though an experimental system using both pre-recorded ECG and ambient noise signals. We show that our approach can lead to an overall improved noise cancelling and the corresponding retrieval of the desired ECG signal reducing the noise levels from -25dB down to -52dB.1.

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“…Here piston driven device was used for monitoring exactly the rhythm when compressions occurred in the chest with enhanced results. A dictionary learning (DL)-based sparse illustration method is used for ECG signal improvement in [24]- [25] with computational efficiency. In [26] they found a technique on wavelet entropy measurements of heart rate variability (HRV) wave which gave better performance.…”

Section: Introductionmentioning

confidence: 99%

An Artifact Elimination in Cardiac Signal using Circular Leaky Normalized LMS Adaptive Algorithm through Iot

Soniya*,

Nutalapati,

Krishna

et al. 2020

IJITEE

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In distant cardiac care observing purposes, Electrocardiogram (ECG) waves are infected by artifacts while gaining data and broadcast of signals. The elimination of the noise is a predominant mission for accurate analysis. In this paper, an effort has been made to eliminate the artifacts, mainly baseline wander (BW), muscle artifacts (MA), power line interface (PLI), and Electrode motion (EM) using a Normalized Least mean square error (NLMS) algorithm. Later on to recover the filtering capacity and speed up the convergence process, data normalization is used. The above algorithm is normalized with reference to maximum data normalization which results in lessened computational difficulty in the denominator. Based on the above algorithms, a variety of adaptive signal enhancers (ASE’s) are improved. To decrease the computational difficulty of the signal enhancer, the designed ASE’s are united with sign-based algorithms. The designed ASE’s are analyzed on original ECG signals gained from the MIT-BIH database to evaluate the performance. The reproduction outcomes gained demonstrates that the block based algorithms are finer than NLMS in terms of the signal to noise ratio (SNR), excess mean square error and computational difficulty. Among the NLMS alternatives, the VSS-CLNLMS (Variable Stepsize Circular Leaky Normalized Least Mean Square error) based ASE’s have fine filtering capacity with a lessened number of computations. The development of the SNR obtained in the progression with the use of VSS-CLNLMS-based ASE’s are calculated as 13.2945 dBs, 12.4589 dBs, 15.6179 dBs and 14.0881 dBs respectively for BW, MA, PLI and EM artifacts.

show abstract

Hybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation

Cited by 11 publications

References 32 publications

Review of noise removal techniques in ECG signals

Review of noise removal techniques in ECG signals

Evaluation of a combined approach for denoising ECG measurements using unconventional sensing technology.

An Artifact Elimination in Cardiac Signal using Circular Leaky Normalized LMS Adaptive Algorithm through Iot

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