SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2016, Vol 4, Issue 1
Abstract
Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snoring, nocturnal arousals, sweating and restless sleep. Obstructive Sleep Apnea (OSA), the most common SA, is a result of a collapsed upper respiratory airway, which is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying sleep apnea. The idea is based on efficient feature extraction of the electrocardiogram (ECG) signal by employing a hybrid of signal processing techniques and classification using a linear-kernel Support Vector Machine (SVM). The optimum set of RR-interval features of the ECG signal yields a high classification accuracy of 97.1% when tested on the Physionet Apnea-ECG recordings. The results provide motivating insights towards future developments of convenient and effective OSA screening setups.
Authors and Affiliations
Laiali Almazaydeh| Department of Software Engineering, Al Hussein Bin Talal University, Jordan, Khaled Elleithy| Department of Computer Science and Engineering, University of Bridgeport, CT 06604, USA, Miad Faezipour| Department of Computer Science and Engineering, University of Bridgeport, CT 06604, USA, Helen Ocbagabir| Department of Computer Science and Engineering, University of Bridgeport, CT 06604, USA
Keywords
Related Articles
- EP ID EP789
- DOI 10.18201/ijisae.79075
- Views 448
- Downloads 21
Long Term and Remote Health Monitoring with Smartphone
The basic aim of our work is to provide solutions with monitoring the heart beat rates of disabled or old people. And also we expect to help the people who have specific heart diseases like potential cardiac arrests...
Diagnosis of Anemia in Children via Artificial Neural Network
In this paper, a neural network algorithm, which diagnosis of anemia for children under 18 years of age, is presented. The network is trained by using data from hemogram test results from 30 patients and an ex...
Classification of Different Wheat Varieties by Using Data Mining Algorithms
There are various applications using computer-aided quality controlling system. In this study, seed data set acquired from UCI machine learning database was used. The purpose of the study is to perform the operations for...
BAT algorithm for Cryptanalysis of Feistel cryptosystems
Recent cryptosystems constitute an effective task for cryptanalysis algorithms due to their internal structure based on nonlinearity. This problem can be formulated as NP-Hard. It has long been subject to various attacks...
Estimating of Compressive Strength of Concrete with Artificial Neural Network According to Concrete Mixture Ratio and Age
Compressive strength of concrete is one of the most important elements for an existing building and a new structure to be built. While obtaining the desired compressive strength of concrete with an appropriate mix and cu...