Separation of fetal electrocardiography (ECG) from composite ECG using adaptive linear neural network for fetal monitoring

M. S. Amin, Md. Mamun Ibne Reaz, Fazida Hanim Hashim, Hafizah Husain

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The signal strength of the maternal ECG (MECG) is usually many times that of the fetal ECG (FECG). Separating FECG from abdominal ECG (AECG) is therefore always a challenge. Some multiple-lead algorithms use the thoracic MECG to cancel the MECG in the AECG to get FECG, though this is inconvenient for the patient during long-term monitoring. Hence, this paper describes an adaptive method to separate fetal ECG from composite electrocardiography (ECG) that consists of both maternal and fetal ECGs by using adaptive linear neural network (ADALINE) for easy fetal monitoring. The input signal is the maternal signal and the target signal is the composite signal. The network emulate maternal signal as closely as possible to abdominal signal, thus only predict the maternal ECG in the abdominal ECG. The network error equals abdominal ECG minus maternal ECG, which is the fetal ECG. The characteristic that enables fetal extraction is due to the correlation between maternal ECG signals and the abdominal ECG signal of pregnant woman. A graphic user interface (GUI) program is written in Matlab to detect the changes in extracted fetal ECG by different values of momentum, learning rate and initial weights used in the network. However, the learning rate, momentum and initial weights are adjusted until the results are reasonably well. It is found that filtering performs best by high learning rate, low momentum and small initial weights.

Original languageEnglish
Pages (from-to)5871-5876
Number of pages6
JournalInternational Journal of Physical Sciences
Volume6
Issue number24
Publication statusPublished - 16 Oct 2011

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Fetal monitoring
electrocardiography
Electrocardiography
Neural networks
composite materials
Composite materials
learning
momentum
Momentum

Keywords

  • Fetal electrocardiography (ECG)
  • Fetal monitoring
  • Maternal ECG
  • Neural network
  • Pregnancy
  • QRS

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Electronic, Optical and Magnetic Materials

Cite this

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title = "Separation of fetal electrocardiography (ECG) from composite ECG using adaptive linear neural network for fetal monitoring",
abstract = "The signal strength of the maternal ECG (MECG) is usually many times that of the fetal ECG (FECG). Separating FECG from abdominal ECG (AECG) is therefore always a challenge. Some multiple-lead algorithms use the thoracic MECG to cancel the MECG in the AECG to get FECG, though this is inconvenient for the patient during long-term monitoring. Hence, this paper describes an adaptive method to separate fetal ECG from composite electrocardiography (ECG) that consists of both maternal and fetal ECGs by using adaptive linear neural network (ADALINE) for easy fetal monitoring. The input signal is the maternal signal and the target signal is the composite signal. The network emulate maternal signal as closely as possible to abdominal signal, thus only predict the maternal ECG in the abdominal ECG. The network error equals abdominal ECG minus maternal ECG, which is the fetal ECG. The characteristic that enables fetal extraction is due to the correlation between maternal ECG signals and the abdominal ECG signal of pregnant woman. A graphic user interface (GUI) program is written in Matlab to detect the changes in extracted fetal ECG by different values of momentum, learning rate and initial weights used in the network. However, the learning rate, momentum and initial weights are adjusted until the results are reasonably well. It is found that filtering performs best by high learning rate, low momentum and small initial weights.",
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AU - Husain, Hafizah

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