Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables
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 hav...
محفوظ في:
| المؤلفون الرئيسيون: | , , |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | Conference or Workshop Item |
| اللغة: | English |
| منشور في: |
2017
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/85105 http://hdl.handle.net/10220/43612 https://embs.papercept.net/conferences/conferences/EMBC17/program/EMBC17_ContentListWeb_5.html |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| id |
sg-ntu-dr.10356-85105 |
|---|---|
| record_format |
dspace |
| spelling |
sg-ntu-dr.10356-851052020-11-01T04:43:26Z Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables Ser, Wee Acharya, Jyotibdha Basu, Arindam School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017) Time-frequency analysis Signal pattern classification 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. Accepted version 2017-08-21T04:21:16Z 2019-12-06T15:57:10Z 2017-08-21T04:21:16Z 2019-12-06T15:57:10Z 2017 Conference Paper 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). https://hdl.handle.net/10356/85105 http://hdl.handle.net/10220/43612 https://embs.papercept.net/conferences/conferences/EMBC17/program/EMBC17_ContentListWeb_5.html en © 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. 4 p. application/pdf |
| institution |
Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NTU Library |
| collection |
DR-NTU |
| language |
English |
| topic |
Time-frequency analysis Signal pattern classification |
| spellingShingle |
Time-frequency analysis Signal pattern classification Ser, Wee Acharya, Jyotibdha Basu, Arindam Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables |
| description |
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. |
| author2 |
School of Electrical and Electronic Engineering |
| author_facet |
School of Electrical and Electronic Engineering Ser, Wee Acharya, Jyotibdha Basu, Arindam |
| format |
Conference or Workshop Item |
| author |
Ser, Wee Acharya, Jyotibdha Basu, Arindam |
| author_sort |
Ser, Wee |
| title |
Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables |
| title_short |
Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables |
| title_full |
Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables |
| title_fullStr |
Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables |
| title_full_unstemmed |
Feature Extraction Techniques for Low-Power Ambulatory Wheeze Detection Wearables |
| title_sort |
feature extraction techniques for low-power ambulatory wheeze detection wearables |
| publishDate |
2017 |
| url |
https://hdl.handle.net/10356/85105 http://hdl.handle.net/10220/43612 https://embs.papercept.net/conferences/conferences/EMBC17/program/EMBC17_ContentListWeb_5.html |
| _version_ |
1683493636914806784 |