Authors:
Abraham Otero
1
;
Xosé Vila
2
;
Francisco Palacios
3
and
Francisco J. Coves
3
Affiliations:
1
University San Pablo CEU, Spain
;
2
University of Vigo, Spain
;
3
Hospital Universitary Hospital of Elche, Spain
Keyword(s):
Obstructive sleep apnea (OSA), Biosignal processing, Heart rate variability.
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
;
Informatics in Control, Automation and Robotics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Time and Frequency Response
;
Time-Frequency Analysis
Abstract:
This paper presents a new algorithm for the detection of Obstructive Sleep Apnea (OSA) from a single electrocardiogram lead. It is based on the alterations that OSA patients present in the LF and HF bands of the heart rate variability power spectrum. The algorithm calculates the power of the spectrum in two bands that roughly corresponding with the LF and HF bands. Then the ratio between the power of the low band and the power of the high band is obtained. If this ratio is greater than a certain threshold the patient is classified as having OSA, otherwise he/she is classified as not having OSA. Then the algorithm was validated over the test data set of the Apnea-ECG Database, classifying correctly 29 of 30 recordings.