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Identification of Sleep Apnea Syndrome by Analyzing Sleep Sound Data Using a Clustering Method

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Sleep Apnea Syndrome (SAS) has attracted considerable attention in recent years because it is known to cause catastrophes when people suffering from it fall asleep while driving. In addition, approximately 70% of SAS patients have other lifestyle-related diseases, which are further complicated owing to SAS; therefore, its early detection is important. Moreover, the detection of pre-SAS, which is likely to turn into SAS in the future, may help in the early detection of SAS. In this study, a method to identify SAS and pre-SAS using only sound data has been proposed. In the proposed method, peculiar breathing sound patterns in SAS are analyzed using the k-means clustering method and perform discriminant analysis using quantification theory based on the results. Because respiratory sound data is extensive and non-uniform during sleep, an efficient analysis method is required. The presented analysis method focuses on the features of SAS sounds, and performs the analysis almost automatically; therefore, the time required for analysis is reduced and the uniformity of the analysis results is guaranteed. Using this method, it is possible to distinguish between non-SAS and SAS symptoms including pre-SAS symptoms, which is difficult to perform using the Apnea-Hypopnea Index, which is a measure of SAS that employs breathing sound data only. Thus, this method, which also analyzes pre-SAS, is convenient for the early detection and treatment of SAS and can also be expected to contribute toward the early detection of preliminary lifestyle-related diseases.
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Sleep Apnea Syndrome; k-Means Clustering; Sound Data; Apnea-Hypopnea Index

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Health Information for the Public. U.S. Department of Health and Human Services, Sleep Apnea: What Is Sleep Apnea?, July 2012, “”, Retrieved 24 January 2020.

N. Uchida, My Favorite Sleep Medicine, KODAN-Sya, April 2013, pp.79-91 (In Japanese).

S. Garbarino, O. Guglielmi, A. Sanna, G.L. Mancardi and N. Magnavita, Risk of Occupational Accidents in Workers with Obstructive Sleep Apnea: Systematic Review and Meta-analysis, Sleep, Vol. 39, No. 6, June 2016, pp. 1211–1218.

A. Noda, F. Yasuma, S. Miyata, K. Iwamoto, Y. Yasuda and N. Ozaki, Sleep Fragmentation and Risk of Automobile Accidents in Patients with Obstructive Sleep Apnea - Sleep Fragmentation and Automobile Accidents in OSA, Health, Vol.11 No.2, February 2019, pp. 171-181.

Japanese Respiratory Society, Sleep Apnea syndrome, February 2017.”, Retrieved 24 January 2020 (In Japanese).

R. Morimoto and S. Ito, Pathophysiology and treatment of sleep apnea syndrome as endocrine hypertension, Japanese Society of Internal Medicine, Vol. 107, No. 4, April 2018, pp. 696-702.

Sleep Apnea Syndrome Support Center, What is sleep apnea?, “”, Retrieved 24 January 2020 (In Japanese).

Japan Ministry of Health, Labour and Welfare, Prevention of Lifestyle-Related diseases,”, Retrieved 24 January 2020 (In Japanese).

The Pharmaceutical Society of Japan, Pharmaceutical Glossary: Lifestyle-Related-Diseases,”, Retrieved 24 January 2020 (In Japanese).

A.Noda and Y Koike, Polysomnography, Transactions of the Japanese Society for Medical and Biological Engineering, May 2008, pp. 134-143 (In Japanese).

Joint research group participation society (Japanese Society of Cardiology, Japanese Respiratory Society, Japanese Society of Respiratory Care and Rehabilitation, Japanese Society of Hypertension, Japanese Heart Association, Japanese Heart Failure Society, Japanese Cardiac Rehabilitation Society, Japanese Society of Sleep Science), Guidelines for Diagnosis and Treatment of Sleep Disordered Breathing in Cardiovascular Disease, Circulation Journal Vol. 74, ,November 2012, pp. 1064-1067 (In Japanese).

Japan Ministry of Land, Infrastructure, Transport and Tourism Beware of "Sleep Apnea Syndrome"!, June 2019,, Retrieved 24 January 2020 (In Japanese).

A. Murakami, Diagnosis and Treatment of Sleep Apnea Syndrome: Sleep Apnea Syndrome Triggers Life-Threatening Disorders During Sleep, Journal of the Japan Medical College, March 2007, pp. 96-101 (In Japanese).

A. M. Benavides, J. L. B. Murillo, R. F. Pozo, F. E. Cuadros, D. T. Toledano, J. D. Alcázar-Ramírez and L. A. H. Gómez, Formant Frequencies and Bandwidths in Relation to Clinical Variables in an Obstructive Sleep Apnea Population, Journal of Voice, Vol. 30, No. 1, January 2016, pp. 21-29.

K. Qian, C. Janott, Z. Zhang, C. Heiser and B. Schuller. Wavelet features for classification of vote snore sounds, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, March 2016.

R. Soltanzadeh, J. Winkler and C. Shafai, Tracheal Sounds Features Changes in Different Sleep Stages Based on Complex Wavelet Analysis, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 2018,

T. Kasahara, K. Nomura, Y. Ueda, Y. Yonezawa, M. Saito, H. Toga, Y. Fujimoto, K. Kojima, H. Kimura and H. Nambo, Integration of Sound and Image Data for Detection of Sleep Apnea, International Conference on Management Science and Engineering Management, June 2017, pp. 804-813.

R. Hummel, T. D. Bradley, D. Packer and H. Alshaer, Distinguishing obstructive from central sleep apneas and hypopneas using linear SVM and acoustic features, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 2016.

H. Nakano, T. Furukawa and T. Tanigawa, Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea, Journal of Clinical Sleep Medicine, August 2019.

M. Shirasuna, Z. Zhang, H. Toda, Y. Ishikawa, T. Sakaguchi and T. Akiduki, Proposal for Sleep Apnea Syndrome discriminating method and discovery process pre-SAS, The Japan Society of Mechanical Engineers, December 2018 (In Japanese).

Y. Hirai, Your First Pattern Recognition, MORIKITA-Syuppan, July 2012, pp. 155-157 (In Japanese).

C. C. Aggarwal, Data Mining: The Textbook, Springer, October 2016, pp. 162-163.

T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, pp. 509-514.

Y. Nagata and Y. Munechika, Introduction to Multivariate Analysis, SAIENSU-Sya, April 2001, pp. 119-131 (In Japanese).

H. Yanai and Y. Takane, Modern Statistics HAYASHI MICHIO 2 New edition Multivariate analysis, ASAKURA-Syoten, September 1977, pp. 174-179 (In Japanese).

Y. Tanaka and K. Wakimoto, Method of Multivariate Statistical Analysis, GENDAI SUGAKU-Sya, May 1983, pp.101-136 (In Japanese).

S. Aoki, Statistical Analysis by R, Ohmu-Sya, April 2009, pp. 188-191 (In Japanese).

Uman Putra, D., Penangsang, O., Soeprijanto, A., Non-Intrusive Load Monitoring Design Using K-Means Clustering Extreme Learning Machine, (2018) International Review on Modelling and Simulations (IREMOS), 11 (4), pp. 215-220.

Kazsoki, A., Hartmann, B., Data Analysis and Data Generation Techniques for Comparative Examination of Distribution Network Topologies, (2019) International Review of Electrical Engineering (IREE), 14 (1), pp. 32-42.


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