An efficient method for fetal electrocardiogram extraction from the abdominal electrocardiogram signal

Muhammad Asraful Hassan, Muhammad Ibn Ibrahimy, Md. Mamun Ibne Reaz

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Problem statement: FECG (Fetal Electrocardiogram) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. Approach: Conventional techniques were often unable to achieve the extraction of FECG from the Abdominal ECG (AECG) in satisfactorily level. A new methodology by combining the Artificial Neural Network (ANN) and Correlation (ANNC) approach had been proposed in this study. Results: The accuracy of the proposed method for FECG extraction from the AECG signal was about 100% and the performance of the method for FHR extraction is 93.75%. Conclusions/Recommendations: The proposed approach involved the FECG extraction even though the MECG and FECG are overlapped in the AECG signal so that the physician and clinician can make the correct decision for the well-being of the fetus and mother during the pregnancy period.

Original languageEnglish
Pages (from-to)619-623
Number of pages5
JournalJournal of Computer Science
Volume5
Issue number9
DOIs
Publication statusPublished - 2009

Fingerprint

Electrocardiography
Personnel
Neural networks

Keywords

  • AECG
  • Artificial neural network
  • FECG
  • QRS complex

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

An efficient method for fetal electrocardiogram extraction from the abdominal electrocardiogram signal. / Hassan, Muhammad Asraful; Ibrahimy, Muhammad Ibn; Ibne Reaz, Md. Mamun.

In: Journal of Computer Science, Vol. 5, No. 9, 2009, p. 619-623.

Research output: Contribution to journalArticle

@article{3ede16c2ba91465cbbf2fd8a4d90df43,
title = "An efficient method for fetal electrocardiogram extraction from the abdominal electrocardiogram signal",
abstract = "Problem statement: FECG (Fetal Electrocardiogram) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. Approach: Conventional techniques were often unable to achieve the extraction of FECG from the Abdominal ECG (AECG) in satisfactorily level. A new methodology by combining the Artificial Neural Network (ANN) and Correlation (ANNC) approach had been proposed in this study. Results: The accuracy of the proposed method for FECG extraction from the AECG signal was about 100{\%} and the performance of the method for FHR extraction is 93.75{\%}. Conclusions/Recommendations: The proposed approach involved the FECG extraction even though the MECG and FECG are overlapped in the AECG signal so that the physician and clinician can make the correct decision for the well-being of the fetus and mother during the pregnancy period.",
keywords = "AECG, Artificial neural network, FECG, QRS complex",
author = "Hassan, {Muhammad Asraful} and Ibrahimy, {Muhammad Ibn} and {Ibne Reaz}, {Md. Mamun}",
year = "2009",
doi = "10.3844/jcssp.2009.619.623",
language = "English",
volume = "5",
pages = "619--623",
journal = "Journal of Computer Science",
issn = "1549-3636",
publisher = "Science Publications",
number = "9",

}

TY - JOUR

T1 - An efficient method for fetal electrocardiogram extraction from the abdominal electrocardiogram signal

AU - Hassan, Muhammad Asraful

AU - Ibrahimy, Muhammad Ibn

AU - Ibne Reaz, Md. Mamun

PY - 2009

Y1 - 2009

N2 - Problem statement: FECG (Fetal Electrocardiogram) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. Approach: Conventional techniques were often unable to achieve the extraction of FECG from the Abdominal ECG (AECG) in satisfactorily level. A new methodology by combining the Artificial Neural Network (ANN) and Correlation (ANNC) approach had been proposed in this study. Results: The accuracy of the proposed method for FECG extraction from the AECG signal was about 100% and the performance of the method for FHR extraction is 93.75%. Conclusions/Recommendations: The proposed approach involved the FECG extraction even though the MECG and FECG are overlapped in the AECG signal so that the physician and clinician can make the correct decision for the well-being of the fetus and mother during the pregnancy period.

AB - Problem statement: FECG (Fetal Electrocardiogram) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. Approach: Conventional techniques were often unable to achieve the extraction of FECG from the Abdominal ECG (AECG) in satisfactorily level. A new methodology by combining the Artificial Neural Network (ANN) and Correlation (ANNC) approach had been proposed in this study. Results: The accuracy of the proposed method for FECG extraction from the AECG signal was about 100% and the performance of the method for FHR extraction is 93.75%. Conclusions/Recommendations: The proposed approach involved the FECG extraction even though the MECG and FECG are overlapped in the AECG signal so that the physician and clinician can make the correct decision for the well-being of the fetus and mother during the pregnancy period.

KW - AECG

KW - Artificial neural network

KW - FECG

KW - QRS complex

UR - http://www.scopus.com/inward/record.url?scp=70349164135&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349164135&partnerID=8YFLogxK

U2 - 10.3844/jcssp.2009.619.623

DO - 10.3844/jcssp.2009.619.623

M3 - Article

AN - SCOPUS:70349164135

VL - 5

SP - 619

EP - 623

JO - Journal of Computer Science

JF - Journal of Computer Science

SN - 1549-3636

IS - 9

ER -