Live demonstration: An aiot wearable ecg patch with decision tree for arrhythmia analysis

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

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

2 Citations (Scopus)


This live demonstration presents a novel electrocardiogram (ECG) monitoring system with artificial intelligence of things (AIoT) design, which is based on decision tree (DT). The proposed system includes a front-end device and a software system. The front-end device includes a solar charging circuit, a wireless charging circuit and an analog front-end circuit. First, the solar charging block takes a dye-sensitized sorlar cell from National Chung Hsing University, which is responsible for energy harvesting under indoor illuminance. Second, the wireless charging block gives users an additional charging method to meet the demand of long-Term monitoring. Third, the analog front-end block is composed of the ECG-sensing circuit, the microcontroller unit (MCU) and the Bluetooth Low Energy (BLE) module. The ECG-sensing circuit is based on single lead measurement, and it includes level shifter units, differential amplifiers and filters. The circuits are implemented by the commercial components and realized by self-designed print circuit boards (PCB). On the other hand, this paper takes ARM Cortex M4 and BLE 5.0 as the solution for data transmitting and encoding. All the above circuits are integrated into one PCB, and the prototype is designed by 3D-printer. The whole ECG Patch's size is 86.6 mm∗ 50 mm∗ 20 mm. The software system includes an application (APP) with DT algorithms, a cloud server is available to execute DT training and to provide a user interface for supporting telemedicine. This paper proposes a simplified DT model, which can be realized in APP based on iOS system. The APP classifies real-Time ECG data into different arrhythmias, and the delay latency is 500 ms in average. Meanwhile, according to 4G or Wi-Fi, the collected ECG data are uploaded to the cloud server for training DT. Then, the coefficients of the pre-Trained DT will be sent back to the APP for updating. The accuracy is 98.7%. By the proposed AIoT system, doctors and users can realize the task of long-Term ECG monitoring, which is valuable for cardiovascular disease diagnosis. Also, doctors can assist users instantly by the web user interface, to meet the demands of telemedicine. The proposed AIoT system has been conducted human trials in National Cheng Kung University Hospital. The power consumption of the proposed front-end device is 8.25 mW, and it can be continuously used up to 32 hours with a 120 mAh lithium-ion battery. If it turns on solar charging, the device can continually operate, until the solar cell is dead.

Original languageEnglish
Title of host publicationBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006175
Publication statusPublished - 2019 Oct
Event2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 - Nara, Japan
Duration: 2019 Oct 172019 Oct 19

Publication series

NameBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings


Conference2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Instrumentation


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