An MLP neural network for ecg noise removal based on kalman filter

Sara Moein

doi:10.1007/978-1-4419-5913-3_13

An MLP neural network for ecg noise removal based on kalman filter

Sara Moein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown.

Original language	English
Title of host publication	Advances in Computational Biology
Editors	Hamid Arabnia
Pages	109-116
Number of pages	8
DOIs	https://doi.org/10.1007/978-1-4419-5913-3_13
State	Published - 2010
Externally published	Yes

Publication series

Name	Advances in Experimental Medicine and Biology
Volume	680
ISSN (Print)	0065-2598

Keywords

Dataset
Kalman filter
MLP training
Noise removal
Performance

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10.1007/978-1-4419-5913-3_13

Cite this

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title = "An MLP neural network for ecg noise removal based on kalman filter",

abstract = "In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown.",

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AB - In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown.

KW - Dataset

KW - Kalman filter

KW - MLP training

KW - Noise removal

KW - Performance

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DO - 10.1007/978-1-4419-5913-3_13

M3 - Conference contribution

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SN - 9781441959126

T3 - Advances in Experimental Medicine and Biology

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EP - 116

BT - Advances in Computational Biology

A2 - Arabnia, Hamid

ER -

An MLP neural network for ecg noise removal based on kalman filter

Abstract

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Keywords

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