@inproceedings{40cd65e5ad9a406e83dae985e4a87429,
title = "Artificial Intelligence of Things Wearable System for Cardiac Disease Detection",
abstract = "This study proposes an artificial intelligence of things (AIoT) system for electrocardiogram (ECG) analysis and cardiac disease detection. The system includes a front-end IoT-based hardware, a user interface on smart device's application (APP), a cloud database, and an AI platform for cardiac disease detection. The front-end IoT-based hardware, a wearable ECG patch that includes an analog front-end circuit and a Bluetooth module, can detect ECG signals. The APP on smart devices can not only display users' real-time ECG signals but also label unusual signals instantly and reach real-time disease detection. These ECG signals will be uploaded to the cloud database. The cloud database is used to store each user's ECG signals, which forms a big-data database for AI algorithm to detect cardiac disease. The algorithm proposed by this study is based on convolutional neural network and the average accuracy is 94.96{\%}. The ECG dataset applied in this study is collected from patients in Tainan Hospital, Ministry of Health and Welfare. Moreover, signal verification was also performed by a cardiologist.",
author = "Lin, {Yu Jin} and Chuang, {Chen Wei} and Yen, {Chun Yueh} and Huang, {Sheng Hsin} and Huang, {Peng Wei} and Chen, {Ju Yi} and Lee, {Shuenn Yuh}",
year = "2019",
month = "3",
doi = "10.1109/AICAS.2019.8771630",
language = "English",
series = "Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "67--70",
booktitle = "Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019",
address = "United States",
note = "1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 ; Conference date: 18-03-2019 Through 20-03-2019",
}