Abstract
This paper presents an in-house design of System-onChip (SoC) based arrhythmia screener, so-called Throb, with selfarrhythmia classification using electrocardiograms (ECG) as the
input signal. It is a light-weight, cost effective and equips with
intuitive touch screen graphical user interfaces (GUI) design. It is
able to provide early screening of arrhythmias for the public,
especially for the small clinics and general hospital in rural area
where the specialists or cardiologists are not sufficient to the
population. Throb applies knowledge-based classification to
identify Premature Ventricular Contraction (PVC), Ventricular
Fibrillation (VF), Second Degree Heart Block, and Atrial
Fibrillation (AF). The verification input is based on offline ECG
dataset obtained from MIT BIH online arrhythmia database. The
complete system is implemented on Terasic Video Embedded
Evaluation Kit with Multitouch (VEEK-MT) which utilizes the
Altera Cyclone IV FPGA chip and capacitive touch screen. This
system is also equipped with the ECG acquisition unit to obtain
the ECG from the patient as input signal. Result shows that this
system is user friendly, and the arrhythmia classification accuracy
of PVC is 88.56%, VF is 96.30%, 2nd degree heart block is 85.71%
and AF is 86.17%, respectively.
Metadata
Item Type: | Article |
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Creators: | Creators Email / ID Num. Huey, Woan Lim hwlim4@live.utm.com Mohd Sani, Mohd Syafiq Affendi UNSPECIFIED Hashim, Amin UNSPECIFIED Yuan, Wen Hau UNSPECIFIED |
Subjects: | R Medicine > R Medicine (General) > Medical technology R Medicine > R Medicine (General) > Computer applications to medicine. Medical informatics R Medicine > R Medicine (General) > Neural Networks (Computer). Artificial intelligence |
Divisions: | Universiti Teknologi MARA, Shah Alam |
Journal or Publication Title: | Journal of Electrical and Electronic Systems Research (JEESR) |
UiTM Journal Collections: | UiTM Journal > Journal of Electrical and Electronic Systems Research (JEESR) |
ISSN: | 1985-5389 |
Volume: | 8 |
Page Range: | pp. 30-36 |
Keywords: | Arrhythmia, Electrocardiogram (ECG), Field Programmable Gate Array (FPGA), Knowledge-based Classification, System-on-Chip (SoC) |
Date: | December 2015 |
URI: | https://ir.uitm.edu.my/id/eprint/62982 |