dc.contributor.authorAcharya, Jyotibdha
dc.contributor.authorBasu, Arindam
dc.contributor.authorSer, Wee
dc.date.accessioned2017-08-21T04:21:16Z
dc.date.available2017-08-21T04:21:16Z
dc.date.issued2017
dc.identifier.citationAcharya, J., Basu, A., & Ser, W. Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017).en_US
dc.identifier.urihttp://hdl.handle.net/10220/43612
dc.description.abstractPresence of wheezes in breathing sounds has been associated with several respiratory and pulmonary diseases. In this paper we present a novel low-complexity wheeze detection method based on frequency contour tracking for automatic wheeze detection. Two hardware friendly variants of the algorithm have also been proposed. Applying the proposed feature extraction algorithm we achieved very high classification accuracy (> 99%) at considerably low computational complexity (3X-6X) compared to earlier methods and the power consumption of the proposed method is shown to be significantly less (70X-100X) compared to ‘record and transmit’ strategy in wearable devices.en_US
dc.format.extent4 p.en_US
dc.language.isoenen_US
dc.rights© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectTime-frequency analysisen_US
dc.subjectSignal pattern classificationen_US
dc.titleFeature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearablesen_US
dc.typeConference Paper
dc.contributor.conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.description.versionAccepted versionen_US
dc.identifier.urlhttps://embs.papercept.net/conferences/conferences/EMBC17/program/EMBC17_ContentListWeb_5.html


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