Sleep Apnea Identification using HRV Features of ECG Signals
TLDR
A classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals is built and time domain features shows the most dominant performance among the HRV features.Abstract:
Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subject-specific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.read more
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References
More filters
Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Journal ArticleDOI
Kubios HRV - Heart rate variability analysis software
Mika P. Tarvainen,Juha-Pekka Niskanen,Jukka A. Lipponen,Perttu O. Ranta-aho,Pasi A. Karjalainen +4 more
TL;DR: Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis that includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection.
Journal ArticleDOI
CPAP for Prevention of Cardiovascular Events in Obstructive Sleep Apnea
Ronald McEvoy,Nick A. Antic,Emma Heeley,Y. Luo,Qiong Ou,Xilong Zhang,Olga Mediano,Rui Chen,Luciano F. Drager,Zhihong Liu,Guofang Chen,Bin Du,Nigel McArdle,Sutapa Mukherjee,Manjari Tripathi,Laurent Billot,Qiang Li,Geraldo Lorenzi-Filho,Ferran Barbé,Susan Redline,Jiguang Wang,Hisatomi Arima,Bruce Neal,David P. White,Ronald R. Grunstein,Nanshan Zhong,Craig S. Anderson +26 more
TL;DR: Therapy with CPAP plus usual care, as compared with usual care alone, did not prevent cardiovascular events in patients with moderate-to-severe obstructive sleep apnea and established cardiovascular disease.
Journal ArticleDOI
Adaptive Servo-Ventilation for Central Sleep Apnea in Systolic Heart Failure
Martin R. Cowie,Holger Woehrle,Karl Wegscheider,Christiane E. Angermann,Marie Pia d'Ortho,Erland Erdmann,Patrick Levy,Anita K. Simonds,Virend K. Somers,Faiez Zannad,Helmut Teschler +10 more
TL;DR: Adaptive servo-ventilation had no significant effect on the primary end point in patients who had heart failure with reduced ejection fraction and predominantly central sleep apnea, but all-cause and cardiovascular mortality were both increased with this therapy.
Journal ArticleDOI
Document-level sentiment classification: An empirical comparison between SVM and ANN
TL;DR: An empirical comparison between SVM and ANN regarding document-level sentiment analysis is presented and it is indicated that ANN produce superior or at least comparable results to SVM's, even on the context of unbalanced data.