@inproceedings{765e273a7dc046db8e27c5fb249fa80b,
title = "A cloud computing system for snore signals processing",
abstract = "Recently, snore signals (SS) have been demonstrated carrying significant information about the obstruction site and degree in the upper airway of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) suffers. To make this acoustic based method more accurate and robust, big SS data processing and analysis are necessary. Cloud computing has the potential to enhance decision agility and productivity while enabling greater efficiencies and reducing costs. We look to cloud computing as the structure to support processing big SS data. In this paper, we focused on the aspects of a Cloud environment that processing big SS data using software services hosted in the Cloud. Finally, we set up a group of comparable experiments to evaluate the performance of our proposed system with different system scales.",
keywords = "Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS), big data, cloud computing, signal processing, snore signals (SS)",
author = "Jian Guo and Kun Qian and Zhaomeng Zhu and Gongxuan Zhang and Huijie Xu",
year = "2013",
doi = "10.1007/978-3-642-45293-2_27",
language = "English",
isbn = "9783642452925",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "359--366",
booktitle = "Advanced Parallel Processing Technologies - 10th International Symposium, APPT 2013, Revised Selected Papers",
note = "10th International Symposium on Advanced Parallel Processing Technologies, APPT 2013 ; Conference date: 27-08-2013 Through 28-08-2013",
}