Total Removal of Baseline Drift from ECG Signal

Chouhan, V. S.; Mehta, S. S.

doi:10.1109/iccta.2007.126

Cited by 79 publications

(31 citation statements)

References 14 publications

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“…Baseline drift causes inaccurate waveform estimation results. For this reason, we employ the method proposed in [30] to remove baseline drift in each RR interval.…”

Section: ) Removal Of Baseline Driftmentioning

confidence: 99%

P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler

Lin

Mailhes

Tourneret

2010

IEEE Trans. Biomed. Eng.

106

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Detection and delineation of P and T-waves are important parts in the analysis and interpretation of ECG signals. This report approaches this problem using Bayesian inference to represent a priori relationships among ECG wave components. Based on the recently introduced partially collapsed Gibbs sampler principle, the wave delineation and estimation are conducted simultaneously by using a Bayesian algorithm combined with a Markov chain Monte Carlo method. This method exploits the strong local dependency of ECG signals. The proposed strategy is evaluated on the annotated QT database and compared to other classical algorithms. An important property of this work is that it allows not only the detection of wave peaks and boundaries, but also the estimation of P and T waveforms for each analysis window. This can be useful for some ECG analysis that require wave morphology information. Index TermsECG, P and T-wave delineation, hierarchical Bayesian analysis, partially collapsed Gibbs sampling, Markov chain Monte Carlo method.

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“…Baseline drift causes inaccurate waveform estimation results. For this reason, we employ the method proposed in [30] to remove baseline drift in each RR interval.…”

Section: ) Removal Of Baseline Driftmentioning

confidence: 99%

P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler

Lin

Mailhes

Tourneret

2010

IEEE Trans. Biomed. Eng.

106

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“…Several approaches to remove baseline drift from electrocardiography (ECG) signals have been proposed (for example, see [31], [32], [33]). As ECG shows repetitive signal characteristics, these algorithms perform sufficiently well at removing baseline drift.…”

Section: Baseline Drift Removalmentioning

confidence: 99%

Eye movement analysis for activity recognition

Bulling

Ward

Gellersen

et al. 2009

Proceedings of the 11th International Conference on Ubiquitous Computing

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Abstract-In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

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“…, D}. This local baseline model extends that of [11], which assumes that the local baseline is constant in Jn (i.e., γn,i = 0 for i ≥ 2). In vector-matrix form, (3) reads as cn = Mnγ n , with the known Nn × 5 Vandermonde matrix Mn and the unknown coefficient vector γ n = (γn,1 · · · γn,5) T .…”

Section: Signal Model For the Non-qrs Intervalsmentioning

confidence: 95%

“…. , D do Sample the block bJ T,n ∼ p(bJ T,n |θ∼J T,n , θP, θcw, x) (see (9)) for k ∈ JT,n do if b T,k = 1 then Sample a T,k ∼ p(a T,k |b T,k = 1, θ∼J T,n , θP, θcw, x) (see (10)) end if end for Sample the block bJ P,n from p(bJ P,n |θ∼J P,n , θT, θcw, x) for k ∈ JP,n do if b P,k = 1 then Sample a P,k ∼ p(a P,k |b P,k = 1, θ∼J P,n , θT, θcw, x) end if end for end for Sample αT from p(αT|bT, aT, θP, θcw, x) (see (11) (13)) Here, UT is the Toeplitz matrix of size K × (L + 1) with first row…”

Section: Algorithm 1 Block Gibbs Samplermentioning

confidence: 99%

P and twave delineation andwaveform estimation in ECG signals using a block gibbs sampler

Lin

Kail²,

Tourneret

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The delineation of P and T waves is important for the interpretation of ECG signals. We propose a Bayesian detection-estimation algorithm for simultaneous detection, delineation, and estimation of P and T waves. A block Gibbs sampler exploits the strong local dependencies in ECG signals by imposing block constraints on the P and T wave locations. The proposed algorithm is evaluated on the annotated QT database and compared with two classical algorithms.

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Total Removal of Baseline Drift from ECG Signal

Cited by 79 publications

References 14 publications

P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler

P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler

Eye movement analysis for activity recognition

P and twave delineation andwaveform estimation in ECG signals using a block gibbs sampler

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