BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring
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| Format: | Artículo científico |
| Language: | en |
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Universidade Estadual de Maringá
2013
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| _version_ | 1876465085847699456 |
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| author | Muhammad Asraful Hasan |
| author_facet | Muhammad Asraful Hasan |
| contents | BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring Muhammad Asraful Hasan Md Mamun Ingeniería QRS complex neural ne twork fetal heart rate Fetal electrocardiogram artificial intelligence Fetal monitoring may help with possible recognit ion of problems in the fetus. This research work focuses on the design of the Back-propagati on Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal El ectrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-be ing during the pregnancy period of a mother to overcome some existing difficulties regarding the feta l heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algo rithm performance. The accu racy of the QRS detection using the designed algorithm is 99%. This research wo rk further made a comparis on study between various methods' performance and accuracy and found that the developed algorithm gi ves the highest accuracy. This paper opens up a passage to biom edical scientists, researchers, and en d 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. 2013 artículo científico 1806-2563 https://www.redalyc.org/articulo.oa?id=303229362002 en http://www.redalyc.org/revista.oa?id=3032 Acta Scientiarum. Technology application/pdf Universidade Estadual de Maringá Acta Scientiarum. Technology (Brasil) Num.2 Vol.35 |
| format | Artículo científico |
| id | redalyc_303229362002 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Universidade Estadual de Maringá |
| spellingShingle | BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring Muhammad Asraful Hasan Ingeniería QRS complex neural ne twork fetal heart rate Fetal electrocardiogram artificial intelligence BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring Muhammad Asraful Hasan Md Mamun Ingeniería QRS complex neural ne twork fetal heart rate Fetal electrocardiogram artificial intelligence Fetal monitoring may help with possible recognit ion of problems in the fetus. This research work focuses on the design of the Back-propagati on Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal El ectrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-be ing during the pregnancy period of a mother to overcome some existing difficulties regarding the feta l heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algo rithm performance. The accu racy of the QRS detection using the designed algorithm is 99%. This research wo rk further made a comparis on study between various methods' performance and accuracy and found that the developed algorithm gi ves the highest accuracy. This paper opens up a passage to biom edical scientists, researchers, and en d 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. 2013 artículo científico 1806-2563 https://www.redalyc.org/articulo.oa?id=303229362002 en http://www.redalyc.org/revista.oa?id=3032 Acta Scientiarum. Technology application/pdf Universidade Estadual de Maringá Acta Scientiarum. Technology (Brasil) Num.2 Vol.35 |
| title | BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring |
| topic | Ingeniería QRS complex neural ne twork fetal heart rate Fetal electrocardiogram artificial intelligence |
| url | https://www.redalyc.org/articulo.oa?id=303229362002 |