Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and Correlation

M. A. Hasan, Md. Mamun Ibne Reaz, M. I. Ibrahimy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Conventional techniques are often unable to achieve the Fetal Electrocardiogram FECG extraction and R-peak detection in FECG from the abdominal ECG (AECG) in satisfactorily level for Fetal Heart Rate (FHR) monitoring. A new methodology by combining the Artificial Neural Network (ANN) and Correlation approach has been proposed in this paper. Artificial Neural Network is chosen primarily since it is adaptive to the nonlinear and time-varying features of the ECG signal. The supervised multilayer perception (MLP) network has been used because it requires a desired output in order to learn. Similarly, the Correlation method has been chosen as the correlation factor can be used to scale the MECG when subtracting it from the AECG, in order to get the FECG. By combining these two approaches the proposed methodology gives better and efficient result in terms of accuracy for FECG extraction and R-peak detection in the AECG signal due to its above characteristics. The proposed approach involves the FECG extraction from the AECG signal with the accuracy of 100% and R-peak detection performance is 93.75%, even though the overlapping situation of MECG and FECG signal in the AECG signal. Therefore the physician and clinician can make the correct decision for the well-being status of the fetus and mother during the pregnancy period.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages15-20
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA
Duration: 31 Jul 20115 Aug 2011

Other

Other2011 International Joint Conference on Neural Network, IJCNN 2011
CitySan Jose, CA
Period31/7/115/8/11

Fingerprint

Electrocardiography
Neural networks
Monitoring
Correlation methods
Multilayers

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Hasan, M. A., Ibne Reaz, M. M., & Ibrahimy, M. I. (2011). Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and Correlation. In Proceedings of the International Joint Conference on Neural Networks (pp. 15-20). [6033193] https://doi.org/10.1109/IJCNN.2011.6033193

Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and Correlation. / Hasan, M. A.; Ibne Reaz, Md. Mamun; Ibrahimy, M. I.

Proceedings of the International Joint Conference on Neural Networks. 2011. p. 15-20 6033193.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hasan, MA, Ibne Reaz, MM & Ibrahimy, MI 2011, Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and Correlation. in Proceedings of the International Joint Conference on Neural Networks., 6033193, pp. 15-20, 2011 International Joint Conference on Neural Network, IJCNN 2011, San Jose, CA, 31/7/11. https://doi.org/10.1109/IJCNN.2011.6033193
Hasan, M. A. ; Ibne Reaz, Md. Mamun ; Ibrahimy, M. I. / Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and Correlation. Proceedings of the International Joint Conference on Neural Networks. 2011. pp. 15-20
@inproceedings{d180417f254d494b85f65f5ca9b16ebe,
title = "Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and Correlation",
abstract = "Conventional techniques are often unable to achieve the Fetal Electrocardiogram FECG extraction and R-peak detection in FECG from the abdominal ECG (AECG) in satisfactorily level for Fetal Heart Rate (FHR) monitoring. A new methodology by combining the Artificial Neural Network (ANN) and Correlation approach has been proposed in this paper. Artificial Neural Network is chosen primarily since it is adaptive to the nonlinear and time-varying features of the ECG signal. The supervised multilayer perception (MLP) network has been used because it requires a desired output in order to learn. Similarly, the Correlation method has been chosen as the correlation factor can be used to scale the MECG when subtracting it from the AECG, in order to get the FECG. By combining these two approaches the proposed methodology gives better and efficient result in terms of accuracy for FECG extraction and R-peak detection in the AECG signal due to its above characteristics. The proposed approach involves the FECG extraction from the AECG signal with the accuracy of 100{\%} and R-peak detection performance is 93.75{\%}, even though the overlapping situation of MECG and FECG signal in the AECG signal. Therefore the physician and clinician can make the correct decision for the well-being status of the fetus and mother during the pregnancy period.",
author = "Hasan, {M. A.} and {Ibne Reaz}, {Md. Mamun} and Ibrahimy, {M. I.}",
year = "2011",
doi = "10.1109/IJCNN.2011.6033193",
language = "English",
isbn = "9781457710865",
pages = "15--20",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",

}

TY - GEN

T1 - Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and Correlation

AU - Hasan, M. A.

AU - Ibne Reaz, Md. Mamun

AU - Ibrahimy, M. I.

PY - 2011

Y1 - 2011

N2 - Conventional techniques are often unable to achieve the Fetal Electrocardiogram FECG extraction and R-peak detection in FECG from the abdominal ECG (AECG) in satisfactorily level for Fetal Heart Rate (FHR) monitoring. A new methodology by combining the Artificial Neural Network (ANN) and Correlation approach has been proposed in this paper. Artificial Neural Network is chosen primarily since it is adaptive to the nonlinear and time-varying features of the ECG signal. The supervised multilayer perception (MLP) network has been used because it requires a desired output in order to learn. Similarly, the Correlation method has been chosen as the correlation factor can be used to scale the MECG when subtracting it from the AECG, in order to get the FECG. By combining these two approaches the proposed methodology gives better and efficient result in terms of accuracy for FECG extraction and R-peak detection in the AECG signal due to its above characteristics. The proposed approach involves the FECG extraction from the AECG signal with the accuracy of 100% and R-peak detection performance is 93.75%, even though the overlapping situation of MECG and FECG signal in the AECG signal. Therefore the physician and clinician can make the correct decision for the well-being status of the fetus and mother during the pregnancy period.

AB - Conventional techniques are often unable to achieve the Fetal Electrocardiogram FECG extraction and R-peak detection in FECG from the abdominal ECG (AECG) in satisfactorily level for Fetal Heart Rate (FHR) monitoring. A new methodology by combining the Artificial Neural Network (ANN) and Correlation approach has been proposed in this paper. Artificial Neural Network is chosen primarily since it is adaptive to the nonlinear and time-varying features of the ECG signal. The supervised multilayer perception (MLP) network has been used because it requires a desired output in order to learn. Similarly, the Correlation method has been chosen as the correlation factor can be used to scale the MECG when subtracting it from the AECG, in order to get the FECG. By combining these two approaches the proposed methodology gives better and efficient result in terms of accuracy for FECG extraction and R-peak detection in the AECG signal due to its above characteristics. The proposed approach involves the FECG extraction from the AECG signal with the accuracy of 100% and R-peak detection performance is 93.75%, even though the overlapping situation of MECG and FECG signal in the AECG signal. Therefore the physician and clinician can make the correct decision for the well-being status of the fetus and mother during the pregnancy period.

UR - http://www.scopus.com/inward/record.url?scp=80054752033&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80054752033&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2011.6033193

DO - 10.1109/IJCNN.2011.6033193

M3 - Conference contribution

AN - SCOPUS:80054752033

SN - 9781457710865

SP - 15

EP - 20

BT - Proceedings of the International Joint Conference on Neural Networks

ER -