@inproceedings{83e98d6c7677418e9dcce2ac4bcaee26,
title = "IoMT Synthetic Cardiac Arrest Dataset for eHealth with AI-based Validation",
abstract = "In the present era, data plays a crucial role across various disciplines, serving as the foundation for exploration and advancements. However, in the domain of eHealth, a readily available dataset for training AI models to predict cardiac arrest using the internet of medical things (IoMT) is lacking. To bridge this gap, this research article addresses the need for a synthesized dataset that can be utilized by researchers in the eHealth field to evaluate the effectiveness of their AI/ML models. The article presents a synthesized IoMT dataset specifically designed for cardiac arrest prediction, incorporating valid ranges of IoMT-based medical features sourced from peer-reviewed journals and articles. This study offers the capability to generate synthetic datasets of varying sizes, catering to the specific requirements of researchers focused on cardiac arrest prediction for individual subjects (patients). The availability of such a dataset will contribute to the advancement of AI-driven research in the eHealth domain.",
keywords = "AI, Cardiac Arrest, eHealth, IoMT, ML, Synthetic Dataset",
author = "Joy Dutta and Deepak Puthal",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 26th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 ; Conference date: 20-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.1109/ISVLSI59464.2023.10238552",
language = "British English",
series = "Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI",
publisher = "IEEE Computer Society",
editor = "Fernanda Kastensmidt and Ricardo Reis and Aida Todri-Sanial and Hai Li and Carolina Metzler",
booktitle = "2023 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 - Proceedings",
address = "United States",
}