Efficient and Simplified Adaptive Noise Cancelers for ECG Sensor Based Remote Health Monitoring

Rahman, Md. Zıa Ur; Ahamed, Shaik Rafi; Reddy, D. V. Rama Koti

doi:10.1109/jsen.2011.2111453

Cited by 79 publications

(29 citation statements)

References 37 publications

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“…But it is especially reasonable for applications requiring substantial signal to noise proportions with less computational complexity [14]. Similarly, many trials have been made to cancel the noise from ECG signal with modification of existing algorithms [14][15][16][17][18][19][20][21]. But the literature cannot provide sufficient information regarding impulsive noise cancellation from cardiac signal.…”

Section: Related Literaturementioning

confidence: 99%

Impulsive Noise Cancellation from Cardiac Signal using Modified WLMS Algorithm

Mohanty¹,

Panda²

2017

Annals of Computer Science and Information Systems

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Abstract-For clean signal, noise cancellation techniques are explored day-by-day. At the user end the clean signal is highly essential for different purposes. In this authors have considered the bio-medical signal that is corrupted with impulsive noise. It is very important to separate from the signal, as its occurrence is sudden and often similar to the signal. The popular adaptive algorithms have been tested for cancellation of impulsive noise. Further most used Wilcoxon LMS is also verified for impulsive noise case. Finally it has been modified for the same purpose. The result found excellent in terms of less MSE, SNR improvement and faster convergence.

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Section: Related Literaturementioning

confidence: 99%

Impulsive Noise Cancellation from Cardiac Signal using Modified WLMS Algorithm

Mohanty¹,

Panda²

2017

Annals of Computer Science and Information Systems

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“…There are different techniques available for use in denoising ECG signals, such as wavelet, [4][5][6][7][8][9] adaptive filtering, [11][12][13][14] empirical mode decomposition (EMD) 15 16 and more.…”

Section: Introductionmentioning

confidence: 99%

“…[12][13][14]. Rahman, et al 15 proposed several adaptive recurrent filters, such assigned regressor algorithm (SRA), normalized least mean square (NLMS), and error nonlinear confirmed that the performance of sign-based algorithms is better than the LMS counterpart. Adaptive filters often require noise reference signals, which are difficult to obtain with the ECG signal acquisition system as an input.…”

Section: Introductionmentioning

confidence: 99%

Denoising Autoencoder for Eletrocardiogram Signal Enhancement

Xiong¹,

Wang²,

Liu³

et al. 2015

j med imaging hlth inform

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Eletrocardiogram (ECG) is a useful diagnostic method to detect electrical signals of both healthy or diseased hearts. However, ECG signals are often contaminated by various types of noise, such as baseline wander, electrode contact noise, and motion artifacts. A Denoising Autoencoder (DAE) is selected to enhance ECG signal in this paper. The ECG signals used for DAE training is initially corrupted by means of a stochastic mapping and then reconstructed to the initial uncorrupted signals. DAEs are stacked to build deep architecture, which will improve the expression with multi-level feature extraction. The method is evaluated on ECG signals from the MIT-BIH Arrthythmia Database. MIT-BIH Noise Stress Test Database was used for generating noise. Computer simulation results show that the proposed system performed better than typically used ECG denoising methods with significant improvement on signal to noise ratio (SNR) and root mean square error (RMSE).

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“…This means that lesser time will be available to carry out the computations of filtering operation. 23,24 Thus far, to the best of the authors knowledge, no effort has been made to reduce the computational complexity of the ANC in the context of brain signal enhancement. A typical Least Mean Square (LMS) based ANC has a drawback of estimating the coefficients of linear expansion.…”

Section: Introductionmentioning

confidence: 99%

Efficient block processing of long duration biotelemetric brain data for health care monitoring

Soumya

Rahman

Reddy

et al. 2015

Review of Scientific Instruments

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In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS, which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence.

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Efficient and Simplified Adaptive Noise Cancelers for ECG Sensor Based Remote Health Monitoring

Cited by 79 publications

References 37 publications

Impulsive Noise Cancellation from Cardiac Signal using Modified WLMS Algorithm

Impulsive Noise Cancellation from Cardiac Signal using Modified WLMS Algorithm

Denoising Autoencoder for Eletrocardiogram Signal Enhancement

Efficient block processing of long duration biotelemetric brain data for health care monitoring

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