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References
-
-
1)
-
16. Lin, W.-H., Wu, D., Li, C., et al: ‘Comparison of heart rate variability from PPG with that from ECG’. The Int. Conf. Health Informatics, 2014, pp. 213–215.
-
2)
-
14. Pomeranz, B., Macaulay, R.J., Caudill, M.A., et al: ‘Assessment of autonomic function in humans by heart rate spectral analysis’, Am. J. Physiol. – Heart Circulatory Physiol., 1985, 248, (1), pp. H151–H153 (doi: 10.1152/ajpheart.1985.248.1.H151).
-
3)
-
21. Peng, R.-C., Zhou, X.-L., Lin, W.-H., et al: ‘Extraction of heart rate variability from smartphone photoplethysmograms’, Comput. Math. Meth. Med., 2015, 2015, , pp. 1–11 (doi: 10.1155/2015/516826).
-
4)
-
19. Selvaraj, N., Jaryal, A., Santhosh, J., et al: ‘Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography’, J. Med. Eng. Technol., 2008, 32, (6), pp. 479–484 (doi: 10.1080/03091900701781317).
-
5)
-
18. Russoniello, C.V., Pougtachev, V., Zhirnov, E., et al: ‘A measurement of electrocardiography and photoplethesmography in obese children’, Appl. Psychophysiol. Biofeedback, 2010, 35, (3), pp. 257–259 (doi: 10.1007/s10484-010-9136-8).
-
6)
-
13. Jeyhani, V., Mahdiani, S., Peltokangas, M., et al: ‘Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals’. IEEE 2015 37th Annual Int. Conf. on Engineering in Medicine and Biology Society (EMBS), 2015, pp. 5952–5955.
-
7)
-
3. Clifton, D.A., Pimentel, M.A.F., Niehaus, K., et al: ‘Intelligent electronic health systems’, in Eren, H., Webster, J.G. (Eds.): ‘Telemedicine and electronic medicine’ (CRC Press, 2015), pp. 73–97.
-
8)
-
11. Stein, P.K., Kleiger, R.E., Rottman, J.N.: ‘Differing effects of age on heart rate variability in men and women’, Am. J. Cardiol., 1997, 80, (3), pp. 302–305 (doi: 10.1016/S0002-9149(97)00350-0).
-
9)
-
1. Clifford, G.D., Clifton, D.A.: ‘Annual review: wireless technology in disease state management and medicine’, Annu. Rev. Med., 2012, 63, pp. 479–492 (doi: 10.1146/annurev-med-051210-114650).
-
10)
-
8. Han, H., Kim, M.-J., Kim, J.: ‘Development of real-time motion artifact reduction algorithm for a wearable photoplethysmography’. IEEE 29th Annual Int. Conf. on Engineering in Medicine and Biology Society (EMBS, 2007), 2007, pp. 1538–1541.
-
11)
-
7. Han, H., Kim, J.: ‘Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method’, Comput. Biol. Med., 2012, 42, (4), pp. 387–393 (doi: 10.1016/j.compbiomed.2011.12.005).
-
12)
-
6. Orphanidou, C., Bonnici, T., Charlton, P., et al: ‘Signal quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring’, IEEE J. Biomed. Health Inf., 2015, 19, (3), pp. 832–838.
-
13)
-
15. Bolanos, M., Nazeran, H., Haltiwanger, E.: ‘Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals’. IEEE 28th Annual Int. Conf. on Engineering in Medicine and Biology Society (EMBS'06, 2006), 2006, pp. 4289–4294.
-
14)
-
12. Berntson, G.G., Thomas Bigger, J., Eckberg, D.L., et al: ‘Heart rate variability: origins, methods, and interpretive caveats’, Psychophysiology, 1997, 34, (6), pp. 623–648 (doi: 10.1111/j.1469-8986.1997.tb02140.x).
-
15)
-
10. Lee, J., Matsumura, K., Yamakoshi, K.-i., et al: ‘Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion’. IEEE 2013 35th Annual Int. Conf. on Engineering in Medicine and Biology Society (EMBS), 2013, pp. 1724–1727.
-
16)
-
17. Lu, G., Yang, F., Taylor, J.A., et al: ‘A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects’, J. Med. Eng. Technol., 2009, 33, (8), pp. 634–641 (doi: 10.3109/03091900903150998).
-
17)
-
20. Teng, X.F., Zhang, Y.T.: ‘Study on the peak interval variability of photoplethysmogtaphic signals’. 2003 IEEE Engineering in Medicine and Biology Society (EMBS) Asian-Pacific Conf. on Biomedical Engineering, 2003, pp. 140–141.
-
18)
-
4. Schäfer, A., Vagedes, J.: ‘How accurate is pulse rate variability as an estimate of heart rate variability?: a review on studies comparing photoplethysmographic technology with an electrocardiogram’, Int. J. Cardiol., 2013, 166, (1), pp. 15–29 (doi: 10.1016/j.ijcard.2012.03.119).
-
19)
-
9. Lee, C.M., Zhang, Y.T.: ‘Reduction of motion artifacts from photoplethysmographic recordings using a wavelet denoising approach’. IEEE 2003, Medicine and Biology Society (EMBS), Asian-Pacific Conf. on Biomedical Engineering, 2003, pp. 194–195.
-
20)
-
5. Shah, S.A., Velardo, C., Farmer, A., et al: ‘Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system’, J. Med. Internet Res., 2017, 19, (3), p. e69 (doi: 10.2196/jmir.7207).
-
21)
-
2. Tarassenko, L., Clifton, D.A.: ‘Semiconductor wireless technology for chronic disease management’, Electron. Lett., 2011, S30, pp. 30–32 (doi: 10.1049/el.2011.2679).
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