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...

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Bibliographic Details
Published in:BioMed research international 2019-01, Vol.2019 (2019), p.1-16
Main Authors: Munoz-Minjares, Jorge, Ibarra-Manzano, O. G., Shmaliy, Yuriy S., Lastre-Domínguez, Carlos, Morales-Mendoza, Luis J.
Format: Article
Language:English
Subjects:
Abnormalities
Accuracy
Adaptive algorithms
Adaptive filters
Algorithms
Analysis
Arrhythmias, Cardiac - physiopathology
Artefacts
Biomedical engineering
Biometrics
Cardiac arrhythmia
Data processing
Discriminant analysis
Echocardiography
EKG
Electrocardiogram
Electrocardiography
Electrocardiography - methods
Feature extraction
Female
Fourier transforms
Heart
Humans
Impulse response
Linear prediction
Male
Noise
Noise measurement
Noise reduction
Principal components analysis
Signal processing
Signal Processing, Computer-Assisted
Signal to noise ratio
Smoothing
Software
Wavelet transforms
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Summary: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
ISSN:2314-6133
2314-6141
DOI:10.1155/2019/2608547