Artificial Intelligence of Things Wearable System for Cardiac Disease Detection

Yu Jin Lin, Chen Wei Chuang, Chun Yueh Yen, Sheng Hsin Huang, Peng Wei Huang, Ju Yi Chen, Shuenn-Yuh Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-70
Number of pages4
ISBN (Electronic)9781538678848
DOIs
Publication statusPublished - 2019 Mar 1
Event1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan
Duration: 2019 Mar 182019 Mar 20

Publication series

NameProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

Conference

Conference1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
CountryTaiwan
CityHsinchu
Period19-03-1819-03-20

Fingerprint

Electrocardiography
Artificial intelligence
Hardware
Bluetooth
User interfaces
Labels
Health
Neural networks
Networks (circuits)

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Lin, Y. J., Chuang, C. W., Yen, C. Y., Huang, S. H., Huang, P. W., Chen, J. Y., & Lee, S-Y. (2019). Artificial Intelligence of Things Wearable System for Cardiac Disease Detection. In Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 (pp. 67-70). [8771630] (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AICAS.2019.8771630
Lin, Yu Jin ; Chuang, Chen Wei ; Yen, Chun Yueh ; Huang, Sheng Hsin ; Huang, Peng Wei ; Chen, Ju Yi ; Lee, Shuenn-Yuh. / Artificial Intelligence of Things Wearable System for Cardiac Disease Detection. Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 67-70 (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019).
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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.",
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Lin, YJ, Chuang, CW, Yen, CY, Huang, SH, Huang, PW, Chen, JY & Lee, S-Y 2019, Artificial Intelligence of Things Wearable System for Cardiac Disease Detection. in Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019., 8771630, Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 67-70, 1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019, Hsinchu, Taiwan, 19-03-18. https://doi.org/10.1109/AICAS.2019.8771630

Artificial Intelligence of Things Wearable System for Cardiac Disease Detection. / Lin, Yu Jin; Chuang, Chen Wei; Yen, Chun Yueh; Huang, Sheng Hsin; Huang, Peng Wei; Chen, Ju Yi; Lee, Shuenn-Yuh.

Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 67-70 8771630 (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lin YJ, Chuang CW, Yen CY, Huang SH, Huang PW, Chen JY et al. Artificial Intelligence of Things Wearable System for Cardiac Disease Detection. In Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 67-70. 8771630. (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019). https://doi.org/10.1109/AICAS.2019.8771630