Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory
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.
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There are fewer studies that use hybrid metaheuristic techniques for heart disease prediction.
“However, there are fewer studies [25], [29], [30] which use hybrid metaheuristic techniques.”
From A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS (2020) · cites this paper as [29]
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- Authors
- Liu, M., & Kim, Y.
- Affiliations
- Hanyang University (KR)
- Venue
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2018-07
- Volume
- 2018
- Pages
- 2707-2710
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2898682710
- Cited by
- 83
- References
- 17
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 18 · 2023: 15 · 2022: 13 · 2021: 6 · 2020: 10 · 2019: 2
- FWCI
- 11.03
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- The Royal Society of Chemistry’s Journals, Books and Databases (The Royal Society of Chemistry) (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- heart disease classification, electrocardiogram, long short-term memory, symbolic aggregate approximation, time series classification, early detection of heart disease, ECG signal preprocessing
- MeSH terms
- Deep Learning, Electrocardiography, Heart Diseases, Humans
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