eprintid: 11999
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/19/99
datestamp: 2023-11-10 03:26:32
lastmod: 2023-11-10 03:26:32
status_changed: 2023-11-10 01:16:38
type: article
metadata_visibility: show
creators_name: Foong, O.-M.
creators_name: Sulaiman, S.
creators_name: Khairuddin, A.A.
title: Atrial Fibrillation for Stroke Detection
ispublished: pub
keywords: Acceptance tests; Computational methods; Diseases; Intelligent systems; Monitoring; Patient monitoring, Atrial fibrillation; Heart beats; Heart rates; Monitoring device; Risk factors; Stroke; Stroke detection; User acceptance testing, Heart
note: cited By 0; Conference of 3rd Computational Methods in Systems and Software, CoMeSySo 2019 ; Conference Date: 10 September 2019 Through 12 September 2019; Conference Code:232339
abstract: This paper presents Atrial Fibrillation Detection for Stroke Prevention. Stroke is the third leading cause of death in Malaysia after cancer. Furthermore, one of the risk factors for stroke is Atrial Fibrillation. The objective of this paper is to develop an IoT device that helps to perform early detection of irregular heart beat at affordable cost. However, the devices or gadgets are expensive and a doctor will not be always available to monitor patient�s pulse regularly. Therefore, a real-time heart rate and rhythm monitoring device are presented in this paper. Experimental results show that the proposed device has achieved satisfactory performance in terms of user acceptance testing. © 2019, Springer Nature Switzerland AG.
date: 2019
publisher: Springer
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075614847&doi=10.1007%2f978-3-030-31362-3_14&partnerID=40&md5=f99a0854a7cb3a883504c36e7f47d688
id_number: 10.1007/978-3-030-31362-3₁₄
full_text_status: none
publication: Advances in Intelligent Systems and Computing
volume: 1047
pagerange: 136-143
refereed: TRUE
isbn: 9783030313616
issn: 21945357
citation:   Foong, O.-M. and Sulaiman, S. and Khairuddin, A.A.  (2019) Atrial Fibrillation for Stroke Detection.  Advances in Intelligent Systems and Computing, 1047.  pp. 136-143.  ISSN 21945357