Detection of Apnea Event with ANN Using Acceleration Data

References

1. B. Xie, H. Minn, “Real-Time Sleep Apnea Detection by Classifier Combination,” IEEE Transactions On Information Technology In Biomedicine 2012; 16: 3:469-477.
2. P. Várady, T. Micsik, S. Benedek, Z. Benyó, “A Novel Method for the Detection of Apnea and Hypopnea Events in Respiration Signals,” IEEE Transactions On Biomedical Engineering 2002; 49: 9: 936-942.
3. G. Cetintas, “Apne-hipopne indeksi ile akciğer volümleri ve hava yolu rezistansı arasındaki ilişkinin tanımlanması,” T.C. Sağlık Bakanlığı Süreyyapaşa Göğüs Hastalıkları Ve Göğüs Cerrahisi E.A. Hastanesi, İstanbul, Türkiye, 2008.
4. M.E.Tagluk, M. Akin, N. Sezgin, “Classification of sleep apnea by using wavelet transform and artificial neural networks,” Expert Systems with Applications 2010; 37:2:1600-1607.
5. S.F. Güven, “Solunum Kayıtlarının Skorlanması,” Eurasian J Pulmonol 2013; 15: 30-34.
6. M. A. S. Serdaroğlu, "A measurement system for human movement analysis," Master of Science Thesis, Department of Signals and System Division of Biomedical Engineering Chalmers Unıversity Of Technology, Göteborg, Sweden, (2011).
7. J. Ning, “Detecting human falls with a 3-axis digital accelerometer,” Analog Dialogue 2009:43-07, [Online]. Available: http://www.analog.com/library/analogdialogue/archives/43-07/fall_detector.html, access date: 11/08/2017, time: 11:51.
8. Sümbül, H., Yüzer A. H., “Development of diagnostic device for COPD: A Mems based approach, “ IJCSNS International Journal of Computer Science and Network Security, Vol:17, No:7, pp. 196-203,July 2017.
9. Sümbül, H., Yüzer A. H.,“3D Monitoring of Lying Position for Patients with Positional Sleep Apnea Syndrome”, Journal of New Results in Science, vol. 12, no. 2016, pp. 59–70, Nov. 2016. ISSN:1304-7981.
10. Sümbül, H., “Uyku apnesi ve koah hastaliklari için tani yöntemi geliştirilmesi,” Ph. D. Thesis, Karabuk University Graduate School of Natural and Applied Sciences Department of Electrical-Electronics Engineering, Karabuk, Turkey, September 2017.
11. I. Saritas, I. A. Ozkan and I. U. Sert, "Prognosis of prostate cancer by artificial neural networks", Expert Systems with Applications, 37(9):6646-6650 (2010).
12. I. A. Ozkan, S. Herdem, I. Saritas, "FPGA-based self-organizing fuzzy controller for electromagnetic filter", Neural Computing and Applications, 28(9): 2535-2543 (2017).