Authors:
Mohamed Elgendi
;
Mirjam Jonkman
and
Friso DeBoer
Affiliation:
Charles Darwin University, Australia
Keyword(s):
Heart Rate, HRV, Plethysmography, SDPTG.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Heart-rate monitoring is a basic measure for cardiovascular functionality assessment. The electrocardiogram (ECG) and Holter monitoring devices are accurate, but their use in the field is limited. Photoplethysmography is an optical technique that has been developed for experimental use in vascular disease. Because of its non-invasive, safe, and easy-to-use properties, it is considered a promising tool that may replace some of the current traditional cardiovascular diagnostic tools. A useful algorithm for a-wave detection in the second derivative plethysmogram (SDPTG) is introduced for heart–rate monitoring. The performance of the proposed method was tested on 27 records measured at rest and after exercise. Statistical HRV measures can be calculated using the a-a interval of the SDPTG.