NDLI: Adaptive classification system for real-time detection of apnea and hypopnea events

Content Provider	IEEE Xplore Digital Library
Author	Koley, B. Dey, D.
Copyright Year	2013
Description	Author affiliation: Electr. Eng. Dept., Jadavpur Univ., Kolkata, India (Dey, D.) \|\| Dept. of Instrum. Eng., B.C. Roy Eng. Coll., Durgapur, India (Koley, B.)
Abstract	This paper presents a real time portable apnea and hypopnea event detection system from the measurement of oronasal airflow signal only. The system uses a combined classification system for detection of events on the basis of personalized normal breathing pattern. Events are detected first, by identifying some abnormal breathing segments with the help of a binary classifier and then, the identified abnormal segments are further classified into any one of the two classes, i.e., apnea (A) and hypopnea (H). The second stage classification system is adaptive in nature, implemented to improve separation of apnea from hypopnea events. The proposed real time system was implemented in personal computer and was clinically validated by offline and online test, the event detection accuracy 93.4% and 91.8% was achieved on 8 different subjects in each case.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	42
Ending Page	45
File Size	1015344
Page Count	4
File Format	PDF
ISBN	9781467327657
e-ISBN	9781467327671
e-ISBN	9781467327664
DOI	10.1109/PHT.2013.6461280
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Event detection Respiration signal Support vector machine classification Feature extraction Sleep apnea Real-time systems Support vector machine Obstructive sleep apnea Real-time monitoring Testing
Content Type	Text
Resource Type	Article

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