References
- R. Priyatharshini and S. Chitrakala, “A Self-learning fuzzy rule-based system for risk-level assessment of coronary heart disease,” IETE. J. Res., 1–10, Feb. 2018. https://doi.org/https://doi.org/10.1080/03772063.2018.1431062.
- E. T. Tan and Z. A. Halim, “Health care monitoring system and analytics based on internet of things framework,” IETE. J. Res., 1–8, May 2018. https://doi.org/https://doi.org/10.1080/03772063.2018.1447402.
- B. G. Celler and R. S. Sparks, “Home telemonitoring of vital signs – technical challenges and future directions,” IEEE. J. Biomed. Health. Inform., Vol. 19, no. 1, pp. 82–91, Jan. 2015. doi: https://doi.org/10.1109/JBHI.2014.2351413
- B.-H. Yang, S. Rhee, and H. Asada. “A twenty-four hour tele-nursing system using a ring sensor,” In Proc. IEEE Int. Conf. Rob. Autom, pp. 387–92, 1998.
- J. Allen, “Photoplethysmography and its application in clinical physiological measurement,” Physiol. Meas., Vol. 28, no. 3, pp. R1–R39, Feb. 2007. doi: https://doi.org/10.1088/0967-3334/28/3/R01
- K. Reddy, B. George, N. Mohan, and V. Kumar, “A novel calibration-free method of measurement of oxygen saturation in arterial blood,” IEEE Trans. Instrum. Meas., Vol. 58, no. 5, pp. 1699–705, May 2009. doi: https://doi.org/10.1109/TIM.2009.2012934
- X. He, R. A. Goubran, and X. P. Liu, “Secondary peak detection of PPG signal for continuous cuffless arterial blood pressure measurement,” IEEE Trans. Instrum. Meas., Vol. 63, no. 6, pp. 1431–9, Jun. 2014. doi: https://doi.org/10.1109/TIM.2014.2299524
- M. T. Islam, I. Zabir, S. T. Ahamed, M. T. Yasar, C. Shahnaz, and S. A. Fattah, “A time-frequency domain approach of heart rate estimation from photoplethysmographic (PPG) signal,” Biomed. Signal. Process. Control., Vol. 36, pp. 146–54, Jul. 2017. doi: https://doi.org/10.1016/j.bspc.2017.03.020
- K. H. Chon, S. Dash, and K. Ju, “Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation,” IEEE Trans. Biomed. Eng., Vol. 56, no. 8, pp. 2054–63, Aug. 2009. doi: https://doi.org/10.1109/TBME.2009.2019766
- A. Goshvarpour and A. Goshvarpour, “Poincaré’s section analysis for PPG-based automatic emotion recognition,” Chaos Solitons Fractals, Vol. 114, pp. 400–7, Sep. 2018. doi: https://doi.org/10.1016/j.chaos.2018.07.035
- A. Romem, A. Romem, D. Koldobskiy, and S. M. Scharf, “Diagnosis of obstructive sleep apnea using pulse oximeter derived photoplethysmographic signals,” J. Clin. Sleep Med., Vol. 10, no. 8, pp. 285–90, 2014. doi: https://doi.org/10.5664/jcsm.3530
- N. Mahri, K. B. Gan, R. Meswari, M. H. Jaafar, and M. A. Mohd. Ali, “Utilization of second derivative photoplethysmographic features for myocardial infarction classification,” J. Med. Eng. Technol., Vol. 41, no. 4, pp. 298–308, May 2017. doi: https://doi.org/10.1080/03091902.2017.1299229
- A. Sološenko, A. Petrėnas, V. Marozas, and L. Sörnmo, “Modeling of the photoplethysmogram during atrial fibrillation,” Comput. Biol. Med., Vol. 81, pp. 130–8, Feb. 2017. doi: https://doi.org/10.1016/j.compbiomed.2016.12.016
- A. Sološenko, A. Petrėnas, and V. Marozas, “Photoplethysmography-based method for automatic detection of premature ventricular contractions,” IEEE Trans. Biomed. Circuits Syst., Vol. 9, no. 5, pp. 662–9, Oct. 2015. doi: https://doi.org/10.1109/TBCAS.2015.2477437
- A. Reisner, P. A. Shaltis, D. McCombie, and H. H. Asada, “Utility of the photoplethysmogram in circulatory monitoring,” Anesthesiology, Vol. 108, no. 5, pp. 950–8, May 2008. doi: https://doi.org/10.1097/ALN.0b013e31816c89e1
- M. Elgendi, “On the analysis of fingertip photoplethysmogram signals,” Curr. Cardiol. Rev., Vol. 8, no. 1, pp. 14–25, Jan. 2012. doi: https://doi.org/10.2174/157340312801215782
- B. R. Ferro, A. R. Aguilera, and R. r. Fernández De La Vara Prieto, “Automated detection of the onset and systolic peak in the pulse wave using Hilbert transform,” Biomed. Signal. Process. Control., Vol. 20, pp. 78–84, Jul. 2015. doi: https://doi.org/10.1016/j.bspc.2015.04.009
- S. Vadrevu and M. S. Manikandan, “A robust pulse onset and peak detection method for automated PPG signal analysis system,” IEEE Trans. Instrum. Meas., Vol. 68, no. 3, pp. 807–17, Mar. 2019. doi: https://doi.org/10.1109/TIM.2018.2857878
- M. J. Oppenheim and D. F. Sittig, “An innovative dicrotic notch detection algorithm which combines rule-based logic with digital signal processing techniques,” Comput. Biomed. Res., Vol. 28, no. 2, pp. 154–70, Apr. 1995. doi: https://doi.org/10.1006/cbmr.1995.1011
- B. N. Li, M. C. Dong, and M. I. Vai, “On an automatic delineator for arterial blood pressure waveforms,” Biomed. Signal. Process. Control., Vol. 5, no. 1, pp. 76–81, Jan. 2010. doi: https://doi.org/10.1016/j.bspc.2009.06.002
- M. Elgendi, “Detection of c, d, and e waves in the acceleration photoplethysmogram,” Comput. Methods Programs Biomed., Vol. 117, no. 2, pp. 125–36, Nov. 2014. doi: https://doi.org/10.1016/j.cmpb.2014.08.001
- M. Elgendi, I. Norton, M. Brearley, D. Abbott, and D. Schuurmans, “Detection of a and b waves in the acceleration photoplethysmogram,” Biomed. Eng. Online, Vol. 13, no. 1, pp. 139(1–18), Sep. 2014. https://doi.org/https://doi.org/10.1186/1475-925X-13-139.
- M. Soundararajan, S. Arunagiri, and S. Alagala, “An adaptive delineator for photoplethysmography waveforms,” Biomed. Eng./Biomedizinische Technik, Vol. 61, no. 6, pp. 645–55, Jan. 2016.
- D. G. Jang, S. H. Park, and M. Hahn, “Framework for automatic delineation of second derivative of photoplethysmogram: A knowledge-based approach,” J. Med. Biol. Eng., Vol. 34, no. 6, pp. 547–53, Jan. 2014.
- H. S. Shin, C. Lee, and M. Lee, “Adaptive threshold method for the peak detection of photoplethysmographic waveform,” Comput. Biol. Med., Vol. 39, no. 12, pp. 1145–52, Dec. 2009. doi: https://doi.org/10.1016/j.compbiomed.2009.10.006
- M. Elgendi, I. Norton, M. Brearley, D. Abbott, and D. Schuurmans, “Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions,” PLoS ONE, Vol. 8, no. 10, e76585 (pp. 1–11), Oct. 2013. doi: https://doi.org/10.1371/journal.pone.0076585
- P. Kinias, H. Fozzard, and M. Norusis, “A real-time pressure algorithm,” Comput. Biol. Med., Vol. 11, no. 4, pp. 211–20, Jun. 1981. doi: https://doi.org/10.1016/S0010-4825(81)80023-6
- J. A. Sukor, S. J. Redmond, and N. H. Lovell, “Signal quality measures for pulse oximetry through waveform morphology analysis,” Physiol. Meas., Vol. 32, no. 3, pp. 369–84, Mar. 2011. doi: https://doi.org/10.1088/0967-3334/32/3/008
- S. K. Mukhopadhyay, M. Mitra, and S. Mitra, “ECG feature extraction using differentiation, Hilbert transform, variable threshold and slope reversal approach,” J. Med. Eng. Technol., Vol. 36, no. 7, pp. 372–86, May 2012. doi: https://doi.org/10.3109/03091902.2012.713438
- A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, PhysioToolkit, and PhysioNet,” Circulation, Vol. 101, no. 23, pp. e215–e220, 2000. doi: https://doi.org/10.1161/01.CIR.101.23.e215