Lightweight polygonal approximation-based ECG signal processing platform

Junho Kwak, Seungmin Lee, Jeonghun Cho, Daejin Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 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
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages819-824
Number of pages6
ISBN (Electronic)9781728130248
DOIs
StatePublished - Aug 2019
Event17th 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 - Fukuoka, Japan
Duration: 5 Aug 20198 Aug 2019

Publication series

NameProceedings - 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

Conference

Conference17th 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
Country/TerritoryJapan
CityFukuoka
Period5/08/198/08/19

Keywords

  • Data compression
  • Electrocardiogram(ECG)
  • Lightweight
  • Platform
  • Polygonal approximation

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