Optimizing beat-wise input for arrhythmia detection using 1-D convolutional neural networks: A real-world ECG study
Utilizing four heartbeats as the input size for arrhythmia classification results in a trade-off between accuracy and computational efficiency. This discovery has significant implications for real-time wearable ECG devices, where both performance and resource constraints are crucial considerations. This insight is expected to serve as a valuable reference for enhancing the design and implementation of arrhythmia detection systems for scalable and efficient mHealth applications.
Authors
- In Cheol Jeong (ORCID: https://orcid.org/0000-0001-9314-5601)
- Jeonghwan Koh (ORCID: https://orcid.org/0009-0002-1883-8971)
- Zicheng Xu (ORCID: https://orcid.org/0000-0003-1221-3954)
- Sunghan Lee (ORCID: https://orcid.org/0000-0002-8694-7144)
- Vladimir Braverman (ORCID: https://orcid.org/0000-0001-7709-8753)
- Sung Pil Cho (ORCID: https://orcid.org/0009-0003-8914-7091)
- Sung Il Im (ORCID: https://orcid.org/0000-0003-2544-2422)
- Haoran Li (ORCID: https://orcid.org/0000-0002-2379-1541)
- Guangyao Zheng (ORCID: https://orcid.org/0000-0002-8864-8578)
Institutions
- Hallym University (KR)
- Daewoo Electronics (South Korea) (KR)
- Kosin University Gospel Hospital (KR)
- Rice University (US)
- Kosin University (KR)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108898
- Citations
- 5
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 6.94
Funders
- Ministry of Trade, Industry and Energy
- Korea Institute for Advancement of Technology
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea