@inproceedings{39812d84743d462f9bc79b53f0146249,
title = "Automatic detection of inspiration related snoring signals from original audio recording",
abstract = "Inspiration related snoring signals (IRSS) are essential for doctors and researchers to develop further study and establishment of personal health database. How to detect IRSS automatically from original audio recording is significant in methods of acoustic based Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) diagnosis and monitoring. We proposed a systematic approach combining signal processing with machine learning techniques to detect IRSS from audio recording. Both the experimental results and computer studies demonstrate the efficiency of the proposed approach.",
keywords = "Apnea/Hypopnea Syndrome, Obstructive Sleep, inspiration related snoring signals, machine learning, signal processing",
author = "Kun Qian and Zhiyong Xu and Huijie Xu and Ng, {Boon Poh}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 ; Conference date: 09-07-2014 Through 13-07-2014",
year = "2014",
month = sep,
day = "3",
doi = "10.1109/ChinaSIP.2014.6889209",
language = "English",
series = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "95--99",
booktitle = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",
address = "United States",
}