Sleep Apnea Detection using Heart Rate Variability and Classifiers
Journal Title: Elysium Journal of Engineering Research and Management - Year 2017, Vol 4, Issue 6
Abstract
The Obstructive Sleep Apnea (OSA) or Obstructive Sleep Apnea Syndrome (OSAS) is the sleeping disorder causing the pause in breathing process or a very low breathing rate while sleeping. The standard technique for analyzing Obstructive Sleep Apnea is called Polosmonography (PSG), which needs an overnight stay in sleep Labs, which is very costly and inconvenient. Alternatively Electrocardiogram (ECG) signal is very promising for OSA detection. An automated classification algorithm is presented in this paper which can deal with short time duration data of an ECG Signal Heart Rate Variability (HRV), from which temporal features are extracted and these extracted features are used in classification. The classification technique being used is based on Support Vector Machine (SVM), trained and tested for data taken from Physionet.org. Resulting algorithm has a high accuracy up to 86.025% and a less processing time.
Authors and Affiliations
Humaira Batool
Keywords
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