Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85105
Title: Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables
Authors: Ser, Wee
Acharya, Jyotibdha
Basu, Arindam
Keywords: Time-frequency analysis
Signal pattern classification
Issue Date: 2017
Source: Acharya, 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).
Abstract: Presence 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.
URI: https://hdl.handle.net/10356/85105
http://hdl.handle.net/10220/43612
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
IGS Conference Papers

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