DocumentCode :
1796946
Title :
Automatic detection of inspiration related snoring signals from original audio recording
Author :
Kun Qian ; Zhiyong Xu ; Huijie Xu ; Boon Poh Ng
Author_Institution :
Sch. of Electron. & Opt. Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2014
fDate :
9-13 July 2014
Firstpage :
95
Lastpage :
99
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 :
acoustic signal processing; audio recording; bioacoustics; diseases; learning (artificial intelligence); medical signal processing; patient diagnosis; patient monitoring; sleep; acoustic based obstructive sleep apnea-hypopnea syndrome diagnosis; acoustic based obstructive sleep apnea-hypopnea syndrome monitoring; automatic inspiration related snoring signal detection; machine learning; original audio recording; personal health database establishment; signal processing; Abstracts; Accuracy; Databases; Educational institutions; Sensors; Sleep apnea; Training; Apnea/Hypopnea Syndrome; Obstructive Sleep; inspiration related snoring signals; machine learning; signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal and Information Processing (ChinaSIP), 2014 IEEE China Summit & International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4799-5401-8
Type :
conf
DOI :
10.1109/ChinaSIP.2014.6889209
Filename :
6889209
Link To Document :
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