TY - GEN
T1 - Lightweight polygonal approximation-based ECG signal processing platform
AU - Kwak, Junho
AU - Lee, Seungmin
AU - Cho, Jeonghun
AU - Park, Daejin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - An electrocardiogram (ECG) signal is one of the most important bio-signals because it is caused by the heart's electrical activity. Therefore, ECG-signal analysis provides information about the heart's condition, especially heart disease. For ECG-signal processing, the original method applied to Internet-of-Things edge devices (such as wearable devices) is server-centric digital signal processing. Edge devices have some restrictions, such as small memory size, limited performance, and poor power supply. Although these devices perform only simple processing, including data acquisition and transmission, the devices' power consumption is high because of a large amount of communication. To solve this problem, we propose a polygonal approximation-based ECG-signal processing platform that is lightweight enough to be implemented in edge devices. In this platform, the ECG data are compressed to a small number of vertices by polygonal approximation, and only vertices are transmitted to the server. Therefore, the amount of communication decreases, thereby reducing the edge device's power consumption. The proposed platform was validated on a virtual edge device, which consists of a RaspberryPi 3 model B microcontroller (MCU) and HealthyPi v3. Results showed a 98% reduction in power consumption compared to a server-centric digital signal processor.
AB - An electrocardiogram (ECG) signal is one of the most important bio-signals because it is caused by the heart's electrical activity. Therefore, ECG-signal analysis provides information about the heart's condition, especially heart disease. For ECG-signal processing, the original method applied to Internet-of-Things edge devices (such as wearable devices) is server-centric digital signal processing. Edge devices have some restrictions, such as small memory size, limited performance, and poor power supply. Although these devices perform only simple processing, including data acquisition and transmission, the devices' power consumption is high because of a large amount of communication. To solve this problem, we propose a polygonal approximation-based ECG-signal processing platform that is lightweight enough to be implemented in edge devices. In this platform, the ECG data are compressed to a small number of vertices by polygonal approximation, and only vertices are transmitted to the server. Therefore, the amount of communication decreases, thereby reducing the edge device's power consumption. The proposed platform was validated on a virtual edge device, which consists of a RaspberryPi 3 model B microcontroller (MCU) and HealthyPi v3. Results showed a 98% reduction in power consumption compared to a server-centric digital signal processor.
KW - Data compression
KW - Electrocardiogram(ECG)
KW - Lightweight
KW - Platform
KW - Polygonal approximation
UR - http://www.scopus.com/inward/record.url?scp=85075156893&partnerID=8YFLogxK
U2 - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00150
DO - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00150
M3 - Conference contribution
AN - SCOPUS:85075156893
T3 - Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
SP - 819
EP - 824
BT - Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
Y2 - 5 August 2019 through 8 August 2019
ER -