Cardiac abnormality prediction using HMLP network
Cardiac abnormality often occurs regardless of gender, age and races but depends on the lifestyle. This problem sometimes does not show any symptoms and usually detected once it already critical which lead to a sudden death to the patient. Basically, cardiac abnormality is the irregular electrical s...
| Published in: | AIP Conference Proceedings |
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| Format: | Conference paper |
| Language: | English |
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American Institute of Physics Inc.
2018
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041918103&doi=10.1063%2f1.5022899&partnerID=40&md5=788ad3b33ca3155551c7b0cdbb3a2964 |
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2-s2.0-85041918103 |
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2-s2.0-85041918103 Adnan J.; Ahmad K.A.; Mat M.H.; Rizman Z.I.; Ahmad S. Cardiac abnormality prediction using HMLP network 2018 AIP Conference Proceedings 1930 10.1063/1.5022899 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041918103&doi=10.1063%2f1.5022899&partnerID=40&md5=788ad3b33ca3155551c7b0cdbb3a2964 Cardiac abnormality often occurs regardless of gender, age and races but depends on the lifestyle. This problem sometimes does not show any symptoms and usually detected once it already critical which lead to a sudden death to the patient. Basically, cardiac abnormality is the irregular electrical signal that generate by the pacemaker of the heart. This paper attempts to develop a program that can detect cardiac abnormality activity through implementation of Hybrid Multilayer Perceptron (HMLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP and HMLP network by using Modified Recursive Prediction Error (MRPE) algorithm and to test the network performance. © 2018 Author(s). American Institute of Physics Inc. 0094243X English Conference paper All Open Access; Bronze Open Access |
| author |
Adnan J.; Ahmad K.A.; Mat M.H.; Rizman Z.I.; Ahmad S. |
| spellingShingle |
Adnan J.; Ahmad K.A.; Mat M.H.; Rizman Z.I.; Ahmad S. Cardiac abnormality prediction using HMLP network |
| author_facet |
Adnan J.; Ahmad K.A.; Mat M.H.; Rizman Z.I.; Ahmad S. |
| author_sort |
Adnan J.; Ahmad K.A.; Mat M.H.; Rizman Z.I.; Ahmad S. |
| title |
Cardiac abnormality prediction using HMLP network |
| title_short |
Cardiac abnormality prediction using HMLP network |
| title_full |
Cardiac abnormality prediction using HMLP network |
| title_fullStr |
Cardiac abnormality prediction using HMLP network |
| title_full_unstemmed |
Cardiac abnormality prediction using HMLP network |
| title_sort |
Cardiac abnormality prediction using HMLP network |
| publishDate |
2018 |
| container_title |
AIP Conference Proceedings |
| container_volume |
1930 |
| container_issue |
|
| doi_str_mv |
10.1063/1.5022899 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041918103&doi=10.1063%2f1.5022899&partnerID=40&md5=788ad3b33ca3155551c7b0cdbb3a2964 |
| description |
Cardiac abnormality often occurs regardless of gender, age and races but depends on the lifestyle. This problem sometimes does not show any symptoms and usually detected once it already critical which lead to a sudden death to the patient. Basically, cardiac abnormality is the irregular electrical signal that generate by the pacemaker of the heart. This paper attempts to develop a program that can detect cardiac abnormality activity through implementation of Hybrid Multilayer Perceptron (HMLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP and HMLP network by using Modified Recursive Prediction Error (MRPE) algorithm and to test the network performance. © 2018 Author(s). |
| publisher |
American Institute of Physics Inc. |
| issn |
0094243X |
| language |
English |
| format |
Conference paper |
| accesstype |
All Open Access; Bronze Open Access |
| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1809677907497320448 |