ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing.
Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG sig...
| Publicado en: | BioMed Research International pp. 1 - 17 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/20/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134818488&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134818488 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/20/2019 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 134818488 134818488 134818488 10.1155/2019/2608547 134818488 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing. aug: au: Lastre-Domínguez, Carlos Shmaliy, Yuriy S. Ibarra-Manzano, Oscar Munoz-Minjares, Jorge Morales-Mendoza, Luis J. affil: Universidad de Guanajuato, Department of Electronics Engineering, Salamanca 36885, Gto., Mexico sug: subj: Electrocardiography Equipment and Supplies Signal Processing, Computer Assisted QRS Complex Neural Networks (Computer) Algorithms Information Retrieval ab: Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p<0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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