Open AccessConstruction of cardiac arrhythmia prediction model using deep learning and gradient boosting
Author(s) -
Dhanar Bintang Pratama,
Favian Dewanta,
Syamsul Rizal
Publication year - 2021
Publication title -
jurnal infotel/jurnal infotel
Language(s) - English
Resource type - Journals
eISSN - 2460-0997
pISSN - 2085-3688
DOI - 10.20895/infotel.v13i3.683
Subject(s) - cardiac arrhythmia , heartbeat , deep learning , artificial intelligence , convolutional neural network , computer science , boosting (machine learning) , gradient boosting , electrocardiography , pattern recognition (psychology) , artificial neural network , machine learning , cardiology , medicine , atrial fibrillation , computer security , random forest
Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.