Building superior heartbeat classification datasets

Garrett I. Cayce; Arthur C. Depoian II; Hae Jin Kim; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2619145

31 May 2022 Building superior heartbeat classification datasets

Garrett I. Cayce, Arthur C. Depoian II, Hae Jin Kim, Colleen P. Bailey, Parthasarathy Guturu

Author Affiliations +

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 120970O (2022) https://doi.org/10.1117/12.2619145
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

For the past 4 decades the MIT-BIH dataset has become the industry standard for the analysis of a comparative metric of signal processing and machine learning techniques. This is because medical data is difficult to collect and use because it is not widely available and open-source. There exists a need to standardize the metric for comparative reasons. This paper proposes a set of datasets targeted at specific tasks currently under investigation in state-of-the-art works. The open sharing of these datasets in multiple formats will allow for the application of the benchmark data to multiple advanced classification algorithms. Published methods will be profiled using this new dataset building the foundation for its merit. A series of datasets are identified with applicable criteria as to their usage such as, TinyML for health monitoring and detection of heart disease.

Conference Presentation

Citation Download Citation

Garrett I. Cayce, Arthur C. Depoian II, Hae Jin Kim, Colleen P. Bailey, and Parthasarathy Guturu "Building superior heartbeat classification datasets", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970O (31 May 2022); https://doi.org/10.1117/12.2619145

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