Authors:
Hélio B. M. Lourenço
1
;
Víctor Sanfins
2
;
Sílvia Ala
3
;
Francisco Barros
4
;
Hugo P. Silva
5
and
Manuel J.C.S. Reis
6
Affiliations:
1
Ace Centre, Abingdon, OX14 1RG, U.K., University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real and Portugal
;
2
Hospital de Guimarães, Serviço de Cardiologia/Laboratório de Arritmologia, Pacing e Electrofisiologia and Portugal
;
3
Instituto Politécnico de Bragança, Departamento de Ciências Sociais e Gerontologia, Portugal, Inst. Inves. Sanitaria Galicia Sur—Grupo de Investigación en Neurociencia y Enfermedades Psiquiátricas, Spain, Neurosciences and Clinical Psychology, University of Vigo and Spain
;
4
University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal, Inst. Inves. Sanitaria Galicia Sur—Grupo de Investigación en Neurociencia y Enfermedades Psiquiátricas and Spain
;
5
IT—Instituto de Telecomunicações, EST/IPS—Escola Superior de Tecnologia do Instituto Politécnico de Setúbal and Portugal
;
6
University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal, IEETA/Department of Engineering and Portugal
Keyword(s):
On-the-Person ECG, Low-cost, Open Source, Dynamic Applications, QTc, Heart Rate Variability.
Related
Ontology
Subjects/Areas/Topics:
Biosignal Acquisition, Analysis and Processing
;
Human-Computer Interaction
;
Methodologies and Methods
;
Physiological Computing Systems
Abstract:
Electrocardiographic (ECG) data analysis can reveal crucial information about the cardiovascular physiological phenomenon, which is modulated by the Autonomic Nervous System. Hereupon, beyond cardiovascular diagnosis, ECG markers can also reflect workload levels, or even physical and mental performance, through Heart Rate Variability (HRV) analysis. Building upon previous work found within the state-of-the-art, this pilot research explores the potential of using a low-cost device for cardiopathy pre-screening, through ECG signal analysis. With the aim of performing the rhythmical analysis, we performed empirical tests from a population of 21 control subjects in a resting position, and an additional 2 subjects, one of them in dynamic condition, in the scope of an exploratory research, using ECG wave segments analysis and HRV features extraction for numerical analysis. Results have demonstrated that the signal quality allows reliable ECG acquisition for further rhythmical and HRV analy
sis, in stationary and dynamic monitoring, for the bipolar leads applied. There was also evidence to suggest a benefit from including ECG morphological analysis with this hardware and software setup for prevention and diagnosis of cardiovascular disorders, although requiring further investigation.
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