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 language | English |
|---|---|
| Title of host publication | Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 67-70 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538678848 |
| DOIs | |
| Publication status | Published - 2019 Mar 1 |
| Event | 1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan Duration: 2019 Mar 18 → 2019 Mar 20 |
Publication series
| Name | Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 |
|---|
Conference
| Conference | 1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 |
|---|---|
| Country | Taiwan |
| City | Hsinchu |
| Period | 19-03-18 → 19-03-20 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Hardware and Architecture
- Electrical and Electronic Engineering
Cite this
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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 proceeding › Conference contribution
TY - GEN
T1 - Artificial Intelligence of Things Wearable System for Cardiac Disease Detection
AU - Lin, Yu Jin
AU - Chuang, Chen Wei
AU - Yen, Chun Yueh
AU - Huang, Sheng Hsin
AU - Huang, Peng Wei
AU - Chen, Ju Yi
AU - Lee, Shuenn-Yuh
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85070457637&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070457637&partnerID=8YFLogxK
U2 - 10.1109/AICAS.2019.8771630
DO - 10.1109/AICAS.2019.8771630
M3 - Conference contribution
AN - SCOPUS:85070457637
T3 - Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
SP - 67
EP - 70
BT - Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -