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Research Article
IoT Based Monitoring System of Life Saving Drug's Infusion
Dr. Rajeswari P1
Boopathy T2
Khadar Basha N3
Sasirekha R4
1Department of ECE, Associate Professor, Dhanalakshmi Srinivasan Engineering College, Perambalur, Tamilnadu, India. 23Department of ECE, Assistant Professor, Dhanalakshmi Srinivasan Engineering College, Perambalur, Tamilnadu, India. 4Department of ECE, PG Student, Dhanalakshmi Srinivasan Engineering College, Perambalur, Tamilnadu, India
Published Online: May-June 2023
Pages: 128-134
Cite this article
No DOIReferences
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Journal, vol. 29, no. 4, pp. 728–736, 2007.
14. J. F. Morales et al, “Sleep apnea hypopnea syndrome classification in spo 2 signals using wavelet decomposition and phase space
reconstruction,” in Wearable and Implantable Body Sensor Networks (BSN), 2017 IEEE 14th International Conference on. IEEE,
2017, pp. 43–46.
15. R. Rol´on et al, “Discriminative methods based on sparse representations of pulse oximetry signals for sleep apnea–hypopnea
detection,” Biomedical Signal Processing and Control, vol. 33, pp. 358–367, 2017.
techniques in clinical research,” Sleep, vol. 22, no. 5, pp. 667–689, 1999.
2. T. Young et al, “Epidemiology of obstructive sleep apnea: a population health perspective,” American journal of respiratory and
critical care medicine, vol. 165, no. 9, pp. 1217–1239, 2002.
3. T. D. Bradley and J. S. Floras, “Obstructive sleep apnoea and its cardiovascular consequences,” The Lancet, vol. 373, no. 9657, pp.
82– 93, 2009.
4. J. M. Marin et al, “Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment
with continuous positive airway pressure: an observational study,” The Lancet, vol. 365, no. 9464, pp. 1046–1053, 2005.
5. C. Iber et al, “The aasm manual for the scoring of sleep and associated events: rules, terminology and technical specifications,”
American Academy of Sleep Medicine, 2007.
6. R. B. Berry et al, “Rules for scoring respiratory events in sleep: update of the 2007 aasm manual for the scoring of sleep and
associated events,” J Clin Sleep Med, vol. 8, no. 5, pp. 597–619, 2012.
7. P. E. Peppard et al, “Increased prevalence of sleep-disordered breathing in adults,” American journal of epidemiology, vol. 177,
no. 9, pp. 1006– 1014, 2013.
8. J. Verbraecken, “Applications of evolving technologies in sleep medicine,” Breathe, vol. 9, no. 6, pp. 442–455, 2013.
9. C. Varon et al, “A novel algorithm for the automatic detection of sleep apnea from single-lead ecg,” IEEE Transactions on
Biomedical Engineering, vol. 62, no. 9, pp. 2269–2278, 2015.
10. P. De Chazal et al, “Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea,”IEEE Transactions on Biomedical Engineering, vol. 50, no. 6, pp. 686–696, 2003.
11. J. L´azaro et al, “Pulse rate variability analysis for discrimination of sleep-apnea-related decreases in the amplitude fluctuations of
pulse photoplethysmography signal in children,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 1, pp. 240–246,
2014.
12. N. Ben-Israel et al, “Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults,” Sleep, vol.
35, no. 9, pp. 1299–1305, 2012.
13. H. Nakano et al, “Automatic detection of sleep-disordered breathing from a single-channel airflow record,” European Respiratory
Journal, vol. 29, no. 4, pp. 728–736, 2007.
14. J. F. Morales et al, “Sleep apnea hypopnea syndrome classification in spo 2 signals using wavelet decomposition and phase space
reconstruction,” in Wearable and Implantable Body Sensor Networks (BSN), 2017 IEEE 14th International Conference on. IEEE,
2017, pp. 43–46.
15. R. Rol´on et al, “Discriminative methods based on sparse representations of pulse oximetry signals for sleep apnea–hypopnea
detection,” Biomedical Signal Processing and Control, vol. 33, pp. 358–367, 2017.
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