Automatic detection of inspiration related snoring signals from original audio recording

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.

[1]  Zahra Moussavi,et al.  Automatic and Unsupervised Snore Sound Extraction From Respiratory Sound Signals , 2011, IEEE Transactions on Biomedical Engineering.

[2]  F. Cirignotta,et al.  Some epidemiological data on snoring and cardiocirculatory disturbances. , 1980, Sleep.

[3]  Ronald M. Aarts,et al.  The acoustics of snoring. , 2010, Sleep medicine reviews.

[4]  Yang Yu,et al.  A roller bearing fault diagnosis method based on EMD energy entropy and ANN , 2006 .

[5]  Wen-Hung Liao,et al.  Classification of Audio Signals in All-Night Sleep Studies , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[6]  M. Kryger,et al.  Sleep apnea: clinical investigations in humans. , 2007, Sleep medicine.

[7]  Masahito Yamamoto,et al.  Recognition of breathing route during snoring for simple monitoring of sleep apnea , 2010, Proceedings of SICE Annual Conference 2010.

[8]  Allan I Pack,et al.  Sleep-disordered breathing: access is the issue. , 2004, American journal of respiratory and critical care medicine.

[9]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[10]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Ronald W. Schafer,et al.  Theory and Applications of Digital Speech Processing , 2010 .

[13]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[14]  Zhiyong Xu,et al.  All Night Analysis of Snoring Signals by Formant Features , 2013 .

[15]  Qian Kun,et al.  Comparison of two acoustic features for classification of different snore signals , 2013 .

[16]  T. Young,et al.  The occurrence of sleep-disordered breathing among middle-aged adults. , 1993, The New England journal of medicine.