Projection operator based removal of baseline wander noise from ECG signals

Agrawal, Sakshi; Gupta, Anubha

doi:10.1109/acssc.2013.6810431

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…Hence, the contraction of left ventricle is later than that of right ventricle [38]. ECG recordings of patients suffering with LBBB have the following characteristics: (1) QRS duration is greater than 120 ms; (2) Lead V1 signal shows a slurring of QRS with an initial R wave; (3) ST segment is seen to have displacement; and (4) the direction of T wave is opposite to R wave [40].…”

Section: Left Bundle Branch Block (Lbbb)mentioning

confidence: 98%

“…Using the method proposed above, we remove baseline wander noise from the noisy ECG signal and obtain the denoised signal [38]. We further note that the proposed approach of baseline wander removal is independent of the number of sample functions of the first order fBm processes taken to generate the X matrix of size N Â M where N is the length of the input signal block and M is the number of sample functions of fBm considered.…”

Section: S ¼ Yàŷ ð13þmentioning

confidence: 99%

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Fractal and EMD based removal of baseline wander and powerline interference from ECG signals

Agrawal¹,

Gupta²

2013

Computers in Biology and Medicine

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Section: Left Bundle Branch Block (Lbbb)mentioning

confidence: 98%

Section: S ¼ Yàŷ ð13þmentioning

confidence: 99%

Fractal and EMD based removal of baseline wander and powerline interference from ECG signals

Agrawal¹,

Gupta²

2013

Computers in Biology and Medicine

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“…Nevertheless, the contribution of these distortions for the purpose of visualizing HBS using contours was insignificant. The conventional use of filters for HBS like that employed in EPS might require extensive evaluation due to the possible imposition of filtering distortions (Agarwal & Gupta, 2013 ; An & Stylios, 2020 ) and might mislead the noninvasive identification of HBS. The present work managed the filtering problem by defining the region of interest and appropriately using wavelet thresholding and SSP as demonstrated.…”

Section: Discussionmentioning

confidence: 99%

A method for noninvasive beat‐by‐beat visualization of His bundle signals

Senthil‐Nathan

Devi

Sasikala

et al. 2023

Noninvasive Electrocardiol

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BackgroundInvasive recording of His bundle signals (HBS) in electrophysiological study (EPS) is important in determining HV interval, the time taken to activate the ventricles from the His bundle. Noninvasive surface measurements of HBS are attempted by averaging typically 100–200 cardiac cycles of ECG time series in body surface potential mapping (BSPM) and in magnetocardiography (MCG) which records weak cardiac magnetic fields by highly sensitive detectors. However, noninvasive beat‐by‐beat extraction of HBS is challenged by ramp‐like atrial signals and noise in PR segment of the cardiac cycle.MethodsBy making use of a signal‐averaged trace showing prominent HBS as a guide trace, we developed a method combining interval‐dependent wavelet thresholding (IDWT) and signal space projection (SSP) technique to eliminate artifacts from single beats. The method was applied on MCG recorded on 21 subjects with known HV intervals based on EPS and noninvasive signal‐averaging, including five subjects with BSPM recorded subsequently. The method was also applied on stress‐MCG of a subject featuring autonomic dynamics.ResultsHBS could be extracted from 19 out of 21 subjects by signal‐averaging whose timing differed from EPS between −8 and 11 ms as tested by 2 observers. HBS in single beats were seen as aligned patterns in inter‐beat contours and were appreciable in stress‐MCG and conspicuous than BSPM. The performance of the method was evaluated on simulated and measured MCG to be adequate if the signal‐to‐noise ratio was at least 20 dB.ConclusionsThese results suggest the use of this method for noninvasive assessments on HBS.

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“…However, prior investigations have recommended a number of methods for SNR calculation. One is described in [9]:…”

Section: A Baseline Wander (Blw)mentioning

confidence: 99%

An ECG dataset representing real-world signal characteristics for wearable computers

Zhang¹,

Zahed²,

Nathan

et al. 2015

2015 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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We present an ECG dataset collected in realworld scenarios for wearable devices that includes over 260 recordings of 90-210 seconds that provide guidance for designers to evaluate signal acquisition circuit and system solutions. Several variations on the signal acquisition path are demonstrated, including various sources of interference (baseline wander, motion artifacts, and power line interference), signal path variations (electrode type, coupling method, and common-mode rejection method), and electrode placements (wrist and chest). Based on detailed analysis of signal characteristics under different scenarios, analog front-end (AFE) design recommendations are proposed.

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Projection operator based removal of baseline wander noise from ECG signals

Cited by 12 publications

References 13 publications

Fractal and EMD based removal of baseline wander and powerline interference from ECG signals

Fractal and EMD based removal of baseline wander and powerline interference from ECG signals

A method for noninvasive beat‐by‐beat visualization of His bundle signals

An ECG dataset representing real-world signal characteristics for wearable computers

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