SURF: Subject-Adaptive Unsupervised ECG Signal Compression for Wearable Fitness Monitors

Author:

Hooshmand Mohsen,Zordan DavideORCID,Melodia Tommaso,Rossi Michele

Funder

Samsung Advanced Institute of Technology, South Korea, as part of its Samsung Global Research Outreach program

University of Padova through the Project IoT-SURF

U.S. National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimizing Autoencoder Training for Efficient Data Compression;2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA);2023-09-12

2. Integration of Machine Learning with Wearable Technologies;Handbook of Human‐Machine Systems;2023-07-07

3. A Parametric Lossy Compression Techniques for Biosignals: A Review;Wireless Personal Communications;2022-10-07

4. A Hybrid Biosignal Compression Model for Healthcare Sensor Networks;2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET);2022-09-13

5. Joint ECG–EMG–EEG signal compression and reconstruction with incremental multimodal autoencoder approach;Circuits, Systems, and Signal Processing;2022-06-28

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