Cardiac abnormality prediction using HMLP network

• Published in: AIP Conference Proceedings
• Main Author: Adnan J.; Ahmad K.A.; Mat M.H.; Rizman Z.I.; Ahmad S.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics Inc. 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041918103&doi=10.1063%2f1.5022899&partnerID=40&md5=788ad3b33ca3155551c7b0cdbb3a2964
• ISSN: 0094243X
• DOI: 10.1063/1.5022899

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).