Abstract:
This live demonstration presents a highly completable AIoT wearable ECG monitoring system for electrocardiogram (ECG) signal analysis and cardiac disease detection. The p...Show MoreMetadata
Abstract:
This live demonstration presents a highly completable AIoT wearable ECG monitoring system for electrocardiogram (ECG) signal analysis and cardiac disease detection. The proposed monitor system can be divided into 3 parts: 1) ECG Patch device. 2) iOS-based application (APP). 3) Web-based cloud platform. The wearable ECG patch, which was developed by our team, includes an analog front-end circuit module (AFE) to sense the ECG raw signal and a microcontroller unit accompanied by the Bluetooth module (MCU+BLE) to send the record data to the iOS smart device. Using the self-designed iOS-based APP, the real-time ECG signals can be displayed, the unusual signals can be labeled instantly to reach real-time cardiac disease detection. The Web-based Cloud Platform is composed of an Express server, MongoDB database, and Vue website. The server has its own API that can handle the business logic of the HTTP request from the iOS-based APP we developed. The ECG signals will be uploaded to the MongoDB database through the protection of the server. We also implement the user authenticate system by Google Firebase Service to ensure the data security of individual patients. The cloud database is used to store each user’s ECG signals, which forms a big-data database for AI algorithms to detect cardiac disease. The algorithm proposed by this study is based on a convolutional neural network and the average accuracy is more than 94.96%. The ECG dataset to train, validate, and test is collected from patients in Tainan Hospital, Ministry of Health and Welfare. The algorithm analysis results are double-confirmed by the cardiologist. The main advantages of this system are real-time monitoring, convenience for wear, and ease of use. The functionality of this ECG patch has been approved by TFDA (ID: 008050) (https:// lmspiq.fda.gov.tw/web/MDPIQ/MDPIQ1000Result?lic BaseId=1B7F2D5D-CDCC-4532-84BE-DCF74E65E687) with QMS2183. The iOS APP is also downloadable on the iOS App Store (https:// apps. appl...
Date of Conference: 07-09 November 2024
Date Added to IEEE Xplore: 27 December 2024
ISBN Information: