Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate
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.
Copyright (c) 2018 EMITTER International Journal of Engineering Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright to this article is transferred to Politeknik Elektronika Negeri Surabaya(PENS) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to PENS. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here .
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
- Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
- Authors may reproduce or authorize others to reproduce the work or derivative works for the author’s personal use or company use, provided that the source and the copyright notice of Politeknik Elektronika Negeri Surabaya (PENS) publisher are indicated.
- Authors are allowed to use and reuse their articles under the same CC-BY-NC-SA license as third parties.
- Third-parties are allowed to share and adapt the publication work for all non-commercial purposes and if they remix, transform, or build upon the material, they must distribute under the same license as the original.
Plagiarism screening will be conducted by EMITTER Journal Editorial Board using iThenticate Plagiarism Checker and CrossCheck plagiarism screening service. The author should download and sign the declaration of plagiarism form here and resubmit it with the copyright transfer form via online submission.