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Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal

Abstrakt

Obstructive sleep apnea (OSA) is a condition of cyclic, periodic ob-struction (stenosis) of the upper respiratory tract. OSA could be associated with serious cardiovascular problems, such as hypertension, arrhythmias, hearth failure or peripheral vascular disease. Understanding the way of connection between OSA and cardiovascular diseases is important to choose proper treatment strategy. In this paper, we present a method for integrated measurements of biosignals for automatic OSA detection. The proposed method was implemented using a porta-ble device with the application of the Support Vector Machine (SVM) classifier. The specific objective of this work is to analyze the minimum set of features for the ECG signal that could produce acceptable classification results. Those features can be further expanded using other biosignals, measured by the portable SleAp device. Additionally, the influence of the body movements and positions on meas-urement results with SleAp system are presented. The proposed system could help to determine the influence of OSA on the state of the cardiovascular system.

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Kategoria:
Publikacja monograficzna
Typ:
rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Opublikowano w:
Advances in Intelligent Systems and Computing nr 300, strony 179 - 192,
ISSN: 2194-5357
Tytuł wydania:
W : Human-Computer Systems Interaction: Backgrounds and Applications 3 strony 179 - 192
Język:
angielski
Rok wydania:
2014
Opis bibliograficzny:
Przystup P., Bujnowski A., Poliński A., Rumiński J., Wtorek J.: Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal// W : Human-Computer Systems Interaction: Backgrounds and Applications 3/ : Springer International Publishing, 2014, s.179-192
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/978-3-319-08491-6_15
Weryfikacja:
Politechnika Gdańska

wyświetlono 84 razy

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