Noise Cancellation on ECG and Heart Rate Signals Using the Undecimated Wavelet Transform

Hernandez, O.; Olvera, Edgar

doi:10.1109/etelemed.2009.49

Cited by 18 publications

(15 citation statements)

References 3 publications

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“…DWT (Discrete Wavelet Transform), SWT (Stationary Wavelet Transform) [8], UWT (Undecimated wavelet Transform) [9] and TIWT (Translation Invariant Wavelet transform) are main four approaches for baseline wandering removal and denoising [10][11] the ECG signal, all these approaches are based on wavelet transform and use different wavelet filters. All these techniques were employed and from the observation of all these approaches on 40 subjects it was found that approach 3 and 4 gives better performance in retracing the original signal by removing the artifacts.…”

Section: Baseline Wandering Removal and Denoisingmentioning

confidence: 99%

A Low Complexity Architecture for Online On-chip Detection and Identification of f-QRS Feature for Remote Personalized Health Care Applications

Vemishetty

Jain

Amber

et al. 2014

2014 Fifth International Symposium on Electronic System Design

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This paper introduces a novel low complexity highly accurate on-chip architecture for the detection of fragmented QRS (f-QRS) feature including notches and local extrema in the QRS complexes and subsequently identifies its various morphologies (Notched S, rsR', RsR' without elevation etc.) under the real-time environment targeting remote personalized health care. The proposed architecture uses the outcome of recently proposed Hybrid feature extraction algorithm (HFEA) [1] Level 3 detailed coefficients and detects and identifies the fragmentation feature from the QRS complex based on the criteria of the positions, and the magnitudes of the extrema (maxima and minima) and notches from the wavelet coefficients with no extra cost in terms of arithmetic complexity. To verify the proposed architecture 100 patients were randomly selected from the MIT-BIH PhysioNet PTB database and their ECG was examined by two experienced cardiologists individually and the results were compared with those obtained from the architecture output wherein we have achieved 95 % diagnostic matching.

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Section: Baseline Wandering Removal and Denoisingmentioning

confidence: 99%

A Low Complexity Architecture for Online On-chip Detection and Identification of f-QRS Feature for Remote Personalized Health Care Applications

Vemishetty

Jain

Amber

et al. 2014

2014 Fifth International Symposium on Electronic System Design

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show abstract

“…Previous researchers have used: "db4", "coif5" and "sym7" wavelet function for genetic algorithm based denoising in ECG signal [23]. We extended the work by including more thresholding rules, decomposition levels for latest wavelet functions.…”

Section: System Implementationmentioning

confidence: 99%

“…Al. (2012) [20] considered the DWT for denoising using three wavelet functions ("db4", "coif5" and "sym7") and four different Thresholding rule were used to denoise the ECG signals. The experimental result shows the significant reduction of above considered noises and it retains the ECG signal morphology effectively.…”

Section: Continuous Wavelet Transform Of Signal F(t) Is Calculated Asmentioning

confidence: 99%

“…In a clinical setting where the amount of noise is unknown, IIR filters appears to be preferred for consistent performance. Oscar Hernández (2009) [22] uses Undecimated Wavelet Transform (UWT) with db06 wavelet for ECG noise removal. This research paper aims to select the optimal wavelet function and related features to attain the best noise free signal.…”

Section: Continuous Wavelet Transform Of Signal F(t) Is Calculated Asmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Wavelet Approach for ECG Noise Cancellation

Goel

Tomar

Kaur

2016

IJBSBT

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“…Previous researchers have used: "db4", "coif5" and "sym7" wavelet function for genetic algorithm based denoising ECG signal [23]. We extended the work by including more thresholding rules, decomposition levels for latest wavelet functions and using Fuzzy Inference System(FIS) for system analysis.…”

Section: System Implementationmentioning

confidence: 99%