Open AccessArrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate
Author(s) -
Hilmy Assodiky,
Iwan Syarif,
Tessy Badriyah
Publication year - 2018
Publication title -
emitter international journal of engineering technology
Language(s) - English
Resource type - Journals
eISSN - 2443-1168
pISSN - 2355-391X
DOI - 10.24003/emitter.v6i1.265
Subject(s) - heartbeat , cardiac arrhythmia , computer science , artificial intelligence , term (time) , abnormality , pattern recognition (psychology) , machine learning , cardiology , medicine , physics , computer security , quantum mechanics , psychiatry , atrial fibrillation
Arrhythmia is a heartbeat abnormality that can be harmless or harmful. It depends on what kind of arrhythmia that the patient suffers. People with arrhythmia usually feel the same physical symptoms but every arrhythmia requires different treatments. For arrhythmia detection, the cardiologist uses electrocardiogram that represents the cardiac electrical activity. And it is a kind of sequential data with high complexity. So the high performance classification method to help the arrhythmia detection is needed. In this paper, Long Short-Term Memory (LSTM) method was used to classify the arrhythmia. The performance was boosted by using AdaDelta as the adaptive learning rate method. As a comparison, it was compared to LSTM without adaptive learning rate. And the best result that showed high accuracy was obtained by using LSTM with AdaDelta. The correct classification rate was 98% for train data and 97% for test data.