<b>BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring</b> - doi: 10.4025/actascitechnol.v35i2.15361
<p class="aresumo">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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universidade Estadual de Maringá
2013-04-01
|
| Series: | Acta Scientiarum: Technology |
| Subjects: | |
| Online Access: | http://periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/15361 |
| _version_ | 1819022333211312128 |
|---|---|
| author | Muhammad Asraful Hasan Md Mamun |
| author_facet | Muhammad Asraful Hasan Md Mamun |
| author_sort | Muhammad Asraful Hasan |
| collection | DOAJ |
| description | <p class="aresumo">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.</p> <p class="akeyword"> </p> |
| first_indexed | 2024-12-21T04:21:20Z |
| format | Article |
| id | doaj.art-ad65f85467194a038c8525e5f03b3862 |
| institution | Directory Open Access Journal |
| issn | 1806-2563 1807-8664 |
| language | English |
| last_indexed | 2024-12-21T04:21:20Z |
| publishDate | 2013-04-01 |
| publisher | Universidade Estadual de Maringá |
| record_format | Article |
| series | Acta Scientiarum: Technology |
| spelling | doaj.art-ad65f85467194a038c8525e5f03b38622022-12-21T19:16:10ZengUniversidade Estadual de MaringáActa Scientiarum: Technology1806-25631807-86642013-04-0135219520310.4025/actascitechnol.v35i2.153619184<b>BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring</b> - doi: 10.4025/actascitechnol.v35i2.15361Muhammad Asraful Hasan0Md Mamun1The University of AdelaideUniversiti Kebangsaan Malaysia<p class="aresumo">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.</p> <p class="akeyword"> </p>http://periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/15361fetal electrocardiogramQRS complexneural networkartificial intelligencefetal heart rate |
| spellingShingle | Muhammad Asraful Hasan Md Mamun <b>BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring</b> - doi: 10.4025/actascitechnol.v35i2.15361 Acta Scientiarum: Technology fetal electrocardiogram QRS complex neural network artificial intelligence fetal heart rate |
| title | <b>BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring</b> - doi: 10.4025/actascitechnol.v35i2.15361 |
| title_full | <b>BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring</b> - doi: 10.4025/actascitechnol.v35i2.15361 |
| title_fullStr | <b>BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring</b> - doi: 10.4025/actascitechnol.v35i2.15361 |
| title_full_unstemmed | <b>BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring</b> - doi: 10.4025/actascitechnol.v35i2.15361 |
| title_short | <b>BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring</b> - doi: 10.4025/actascitechnol.v35i2.15361 |
| title_sort | b bpnn based mecg elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring b doi 10 4025 actascitechnol v35i2 15361 |
| topic | fetal electrocardiogram QRS complex neural network artificial intelligence fetal heart rate |
| url | http://periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/15361 |
| work_keys_str_mv | AT muhammadasrafulhasan bbpnnbasedmecgeliminationfromtheabdominalsignaltoextractfetalsignalforcontinuousfetalmonitoringbdoi104025actascitechnolv35i215361 AT mdmamun bbpnnbasedmecgeliminationfromtheabdominalsignaltoextractfetalsignalforcontinuousfetalmonitoringbdoi104025actascitechnolv35i215361 |