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Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory

Title
Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory
Author
김영훈
Issue Date
2018-07
Publisher
IEEE
Citation
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Page. 2707-2710
Abstract
Heart disease classification based on electrocardiogram(ECG) signal has become a priority topic in the diagnosis of heart diseases because it can be obtained with a simple diagnostic tool of low cost. Since early detection of heart disease can enable us to ease the treatment as well as save people's lives, accurate detection of heart disease using ECG is very important. In this paper, we propose a classification method of heart diseases based on ECG by adopting a machine learning method, called Long Short-Term Memory (LSTM), which is a state-of-the-art technique analyzing time series sequences in deep learning. As suitable data preprocessing, we also utilize symbolic aggregate approximation (SAX) to improve the accuracy. Our experiment results show that our approach not only achieves significantly better accuracy but also classifies heart diseases correctly in smaller response time than baseline techniques.
URI
https://ieeexplore.ieee.org/abstract/document/8512761http://repository.hanyang.ac.kr/handle/20.500.11754/106178
ISBN
978-1-5386-3646-6
ISSN
1558-4615
DOI
10.1109/EMBC.2018.8512761
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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