ECG materno baseado em rede neural backpropagation a partir de sinal abdominal para monitoramento fetal contínuo

Translated title of the contribution: BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring

Muhammad Asraful Hasan, Md. Mamun Ibne Reaz

Research output: Contribution to journalArticle

Abstract

Fetal monitoring may help with possible recognition of problems in the fetus. This research work focuses on the design of the Back-propagation Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal Electrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-being during the pregnancy period of a mother to overcome some existing difficulties regarding the fetal heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algorithm performance. The accuracy of the QRS detection using the designed algorithm is 99%. This research work further made a comparison study between various methods' performance and accuracy and found that the developed algorithm gives the highest accuracy. This paper opens up a passage to biomedical scientists, researchers, and end users to advocate to extract the FECG signal from the AECG signal for FHR monitoring system by providing valuable information to help them for developing more dominant, flexible and resourceful applications.

Original languageUndefined/Unknown
Pages (from-to)195-203
Number of pages9
JournalActa Scientiarum - Technology
Volume35
Issue number2
DOIs
Publication statusPublished - 2013

Fingerprint

Fetal monitoring
electrocardiography
back propagation
Back-propagation Neural Network
Electrocardiography
Backpropagation
Elimination
elimination
heart rate
fetuses
research work
Monitoring
Neural networks
monitoring system
monitoring
pregnancy
Heart Rate
Monitoring System
Pregnancy
Electrocardiogram

Keywords

  • Artificial intelligence
  • Fetal electrocardiogram
  • Fetal heart rate
  • Neural network
  • QRS complex

ASJC Scopus subject areas

  • Engineering(all)
  • Mathematics(all)
  • Physics and Astronomy(all)
  • Chemistry(all)
  • Computer Science(all)
  • Earth and Planetary Sciences(all)

Cite this

ECG materno baseado em rede neural backpropagation a partir de sinal abdominal para monitoramento fetal contínuo. / Hasan, Muhammad Asraful; Ibne Reaz, Md. Mamun.

In: Acta Scientiarum - Technology, Vol. 35, No. 2, 2013, p. 195-203.

Research output: Contribution to journalArticle

@article{339224a7721846eeba5f62d4e6b4f0ef,
title = "ECG materno baseado em rede neural backpropagation a partir de sinal abdominal para monitoramento fetal cont{\'i}nuo",
abstract = "Fetal monitoring may help with possible recognition of problems in the fetus. This research work focuses on the design of the Back-propagation Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal Electrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-being during the pregnancy period of a mother to overcome some existing difficulties regarding the fetal heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algorithm performance. The accuracy of the QRS detection using the designed algorithm is 99{\%}. This research work further made a comparison study between various methods' performance and accuracy and found that the developed algorithm gives the highest accuracy. This paper opens up a passage to biomedical scientists, researchers, and end users to advocate to extract the FECG signal from the AECG signal for FHR monitoring system by providing valuable information to help them for developing more dominant, flexible and resourceful applications.",
keywords = "Artificial intelligence, Fetal electrocardiogram, Fetal heart rate, Neural network, QRS complex",
author = "Hasan, {Muhammad Asraful} and {Ibne Reaz}, {Md. Mamun}",
year = "2013",
doi = "10.4025/actascitechnol.v35i2.15361",
language = "Undefined/Unknown",
volume = "35",
pages = "195--203",
journal = "Acta Scientiarum - Technology",
issn = "1806-2563",
publisher = "Universidade Estadual de Maringa",
number = "2",

}

TY - JOUR

T1 - ECG materno baseado em rede neural backpropagation a partir de sinal abdominal para monitoramento fetal contínuo

AU - Hasan, Muhammad Asraful

AU - Ibne Reaz, Md. Mamun

PY - 2013

Y1 - 2013

N2 - Fetal monitoring may help with possible recognition of problems in the fetus. This research work focuses on the design of the Back-propagation Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal Electrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-being during the pregnancy period of a mother to overcome some existing difficulties regarding the fetal heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algorithm performance. The accuracy of the QRS detection using the designed algorithm is 99%. This research work further made a comparison study between various methods' performance and accuracy and found that the developed algorithm gives the highest accuracy. This paper opens up a passage to biomedical scientists, researchers, and end users to advocate to extract the FECG signal from the AECG signal for FHR monitoring system by providing valuable information to help them for developing more dominant, flexible and resourceful applications.

AB - Fetal monitoring may help with possible recognition of problems in the fetus. This research work focuses on the design of the Back-propagation Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal Electrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-being during the pregnancy period of a mother to overcome some existing difficulties regarding the fetal heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algorithm performance. The accuracy of the QRS detection using the designed algorithm is 99%. This research work further made a comparison study between various methods' performance and accuracy and found that the developed algorithm gives the highest accuracy. This paper opens up a passage to biomedical scientists, researchers, and end users to advocate to extract the FECG signal from the AECG signal for FHR monitoring system by providing valuable information to help them for developing more dominant, flexible and resourceful applications.

KW - Artificial intelligence

KW - Fetal electrocardiogram

KW - Fetal heart rate

KW - Neural network

KW - QRS complex

UR - http://www.scopus.com/inward/record.url?scp=84876429841&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876429841&partnerID=8YFLogxK

U2 - 10.4025/actascitechnol.v35i2.15361

DO - 10.4025/actascitechnol.v35i2.15361

M3 - Article

VL - 35

SP - 195

EP - 203

JO - Acta Scientiarum - Technology

JF - Acta Scientiarum - Technology

SN - 1806-2563

IS - 2

ER -