Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables

• Main Authors: Ser, Wee, Acharya, Jyotibdha, Basu, Arindam
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2017
• Subjects: Time-frequency analysis; Signal pattern classification
• Online Access: https://hdl.handle.net/10356/85105; http://hdl.handle.net/10220/43612; https://embs.papercept.net/conferences/conferences/EMBC17/program/EMBC17_ContentListWeb_5.html
• Institution: Nanyang Technological University

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.