Obstructive sleep apnea detection using SVM-based classification of ECG signal features
Sleep apnea is the instance when one either has pauses of breathing in their sleep, or has very low breath while asleep. This pause in breathing can range in frequency and duration. Obstructive sleep apnea (OSA) is the common form of sleep apnea, which is currently tested through polysomnography (PS...
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| container_volume | 2012 |
| creator | Almazaydeh, L. Elleithy, K. Faezipour, M. |
| description | Sleep apnea is the instance when one either has pauses of breathing in their sleep, or has very low breath while asleep. This pause in breathing can range in frequency and duration. Obstructive sleep apnea (OSA) is the common form of sleep apnea, which is currently tested through polysomnography (PSG) at sleep labs. PSG is both expensive and inconvenient as an expert human observer is required to work over night. New sleep apnea classification techniques are nowadays being developed by bioengineers for most comfortable and timely detection. This paper focuses on an automated classification algorithm which processes short duration epochs of the electrocardiogram (ECG) data. The presented classification technique is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high accuracy of 96.5% or higher. Furthermore, the proposed system can be used as a basis for future development of a tool for OSA screening. |
| doi_str_mv | 10.1109/EMBC.2012.6347100 |
| format | Conference Proceeding |
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This pause in breathing can range in frequency and duration. Obstructive sleep apnea (OSA) is the common form of sleep apnea, which is currently tested through polysomnography (PSG) at sleep labs. PSG is both expensive and inconvenient as an expert human observer is required to work over night. New sleep apnea classification techniques are nowadays being developed by bioengineers for most comfortable and timely detection. This paper focuses on an automated classification algorithm which processes short duration epochs of the electrocardiogram (ECG) data. The presented classification technique is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high accuracy of 96.5% or higher. 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Elleithy, K. ; Faezipour, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-34e4a936371eb4dc248afdf14c64fadbd322a8436a9276fa1922ce25f0aa54853</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>ECG</topic><topic>Electrocardiography</topic><topic>Electrocardiography - methods</topic><topic>Feature extraction</topic><topic>Heart Rate</topic><topic>Humans</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Polysomnography - methods</topic><topic>PSG</topic><topic>Reproducibility of Results</topic><topic>RR interval</topic><topic>Sensitivity and Specificity</topic><topic>Sleep apnea</topic><topic>Sleep Apnea, Obstructive - diagnosis</topic><topic>Sleep Apnea, Obstructive - physiopathology</topic><topic>Support Vector Machine</topic><topic>Support vector machines</topic><topic>SVM</topic><toplevel>online_resources</toplevel><creatorcontrib>Almazaydeh, L.</creatorcontrib><creatorcontrib>Elleithy, K.</creatorcontrib><creatorcontrib>Faezipour, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Almazaydeh, L.</au><au>Elleithy, K.</au><au>Faezipour, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Obstructive sleep apnea detection using SVM-based classification of ECG signal features</atitle><btitle>2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society</btitle><stitle>EMBC</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2012-01-01</date><risdate>2012</risdate><volume>2012</volume><spage>4938</spage><epage>4941</epage><pages>4938-4941</pages><issn>1094-687X</issn><issn>1557-170X</issn><eissn>1558-4615</eissn><isbn>1424441196</isbn><isbn>9781424441198</isbn><eisbn>9781457717871</eisbn><eisbn>1457717875</eisbn><abstract>Sleep apnea is the instance when one either has pauses of breathing in their sleep, or has very low breath while asleep. This pause in breathing can range in frequency and duration. Obstructive sleep apnea (OSA) is the common form of sleep apnea, which is currently tested through polysomnography (PSG) at sleep labs. PSG is both expensive and inconvenient as an expert human observer is required to work over night. New sleep apnea classification techniques are nowadays being developed by bioengineers for most comfortable and timely detection. This paper focuses on an automated classification algorithm which processes short duration epochs of the electrocardiogram (ECG) data. The presented classification technique is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high accuracy of 96.5% or higher. 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| issn | 1094-687X 1557-170X 1558-4615 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Algorithms Diagnosis, Computer-Assisted - methods ECG Electrocardiography Electrocardiography - methods Feature extraction Heart Rate Humans Pattern Recognition, Automated - methods Polysomnography - methods PSG Reproducibility of Results RR interval Sensitivity and Specificity Sleep apnea Sleep Apnea, Obstructive - diagnosis Sleep Apnea, Obstructive - physiopathology Support Vector Machine Support vector machines SVM |
| title | Obstructive sleep apnea detection using SVM-based classification of ECG signal features |
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