title: Snore related signals processing in a private cloud computing system.
abstract: Snore related signals (SRS) have been demonstrated to carry important information about the obstruction site and degree in the upper airway of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) patients in recent years. To make this acoustic signal analysis method more accurate and robust, big SRS data processing is inevitable. As an emerging concept and technology, cloud computing has motivated numerous researchers and engineers to exploit applications both in academic and industry field, which could have an ability to implement a huge blue print in biomedical engineering. Considering the security and transferring requirement of biomedical data, we designed a system based on private cloud computing to process SRS. Then we set the comparable experiments of processing a 5-hour audio recording of an OSAHS patient by a personal computer, a server and a private cloud computing system to demonstrate the efficiency of the infrastructure we proposed.
authors.affiliation: School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China, isignalprocessing@hotmail.com.
authors.forename: Kun
authors.initials: K
authors.lastname: Qian
authors.forename: Jian
authors.initials: J
authors.lastname: Guo
authors.forename: Huijie
authors.initials: H
authors.lastname: Xu
authors.forename: Zhaomeng
authors.initials: Z
authors.lastname: Zhu
authors.forename: Gongxuan
authors.initials: G
authors.lastname: Zhang
mesh_terms: D000162:Acoustics; D003205:Computing Methodologies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020181:Sleep Apnea, Obstructive / Q000503:physiopathology; D012913:Snoring; D013016:Sound