References
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standard for electronic or automated sphygmomanometers. In: ANSI/AAMISP10-1992/A1,
Arlington,VA, USA
2. Teng XF, Zhang YT Continuous and noninvasive estimation of arterial blood pressure using
a photoplethysmographic approach. In: Presented at the 25th annual international conference
of the IEEE engineering in medicine and biology society
3. Kurylyak Y, Lamonaca F, Grimaldi D (2013) A neural network-based method for
continuous blood pressure estimation from a PPG signal. In: Presented at 2013 IEEE I2MTC
4. Gao SC, Wittek P, Zhao L, Jiang WJ Data-driven estimation of blood pressure using
photoplethysmographic signals. In: Presented at IEEE EMBC annual international conference
5. Wang L, Zhou W, Xing Y et al (2018) A novel neural network model for blood pressure
estimation using photoplethesmography without electrocardiogram. J Healthc Eng 1:1–9
6. Wu YD, Zhong SC, Shen YC (2017) Noninvasive continuous blood pressure measurement
method based on SWT and ANN. Inf Med Equip 5(32):22–27
7. Wen L, Li ZB, Chen JP et al (2014) Blood pressure measurement algorithm of neural
network based on Gaussian fitting. Transducer Microsyst Technol 4(33), 132–134 + 138
8. Goldberger AL, Amaral LA, Glass L et al (2000) PhysioBank, PhysioToolkit, and
PhysioNet: components of a new research resource for complex physiologic signals.
Circulation 101(23):E215–E220
9. Moody GB, Mark RG (1996) A database to support development and evaluation of
intelligent intensive care monitoring. Comput Cardiol 23:657–660
10. Krizhevsky A, Sutskever I, Hinton GE ImageNet classification with deep convolutional
neural networks. In: Presented at international conference on NIPS
11. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image
recognition. Comput Sci
12. He K, Zhang X, Ren S et al Deep residual learning for image recognition. In: Presented at
IEEE conference on CVPR
13. Cho K, Van Merrienboer B, Gulcehre C et al (2014) Learning phrase representations using
RNN encoder-decoder for statistical machine translation. Comput Sci
14. Amodei D, Anubhai R, Battenberg E et al (2015) Deep speech 2: end-to-end speech
recognition in English and Mandarin. Comput Sci
15. Graves A (2013) Generating sequences with recurrent neural networks. Comput Sci
16. Graves A, Jaitly N. Towards end-to-end speech recognition with recurrent neural networks.
In: Presented at the 31st international conference on ML
17. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–
1780
18. Jozefowicz R, Zaremba W, Sutskever I An empirical exploration of recurrent network
architectures. In: Presented at international conference on machine learning
19. Chung J, Gulcehre C, Cho KH et al (2014) Empirical evaluation of gated recurrent neural
networks on sequence modeling. Comput Sci
20. Zeiler MD (2012) ADADELTA: an adaptive learning rate method. Comput Sci
21. Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural
networks from overfitting. J Mach Learn Res 15(1):1929–1958
22. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by
reducing internal covariate shift. Comput Sci
23. Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison
studies. The Statistician 32(3):307–317
272
C. Wang et al.
1. Association for the Advancement of Medical Instrumentation (1996) American national
standard for electronic or automated sphygmomanometers. In: ANSI/AAMISP10-1992/A1,
Arlington,VA, USA
2. Teng XF, Zhang YT Continuous and noninvasive estimation of arterial blood pressure using
a photoplethysmographic approach. In: Presented at the 25th annual international conference
of the IEEE engineering in medicine and biology society
3. Kurylyak Y, Lamonaca F, Grimaldi D (2013) A neural network-based method for
continuous blood pressure estimation from a PPG signal. In: Presented at 2013 IEEE I2MTC
4. Gao SC, Wittek P, Zhao L, Jiang WJ Data-driven estimation of blood pressure using
photoplethysmographic signals. In: Presented at IEEE EMBC annual international conference
5. Wang L, Zhou W, Xing Y et al (2018) A novel neural network model for blood pressure
estimation using photoplethesmography without electrocardiogram. J Healthc Eng 1:1–9
6. Wu YD, Zhong SC, Shen YC (2017) Noninvasive continuous blood pressure measurement
method based on SWT and ANN. Inf Med Equip 5(32):22–27
7. Wen L, Li ZB, Chen JP et al (2014) Blood pressure measurement algorithm of neural
network based on Gaussian fitting. Transducer Microsyst Technol 4(33), 132–134 + 138
8. Goldberger AL, Amaral LA, Glass L et al (2000) PhysioBank, PhysioToolkit, and
PhysioNet: components of a new research resource for complex physiologic signals.
Circulation 101(23):E215–E220
9. Moody GB, Mark RG (1996) A database to support development and evaluation of
intelligent intensive care monitoring. Comput Cardiol 23:657–660
10. Krizhevsky A, Sutskever I, Hinton GE ImageNet classification with deep convolutional
neural networks. In: Presented at international conference on NIPS
11. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image
recognition. Comput Sci
12. He K, Zhang X, Ren S et al Deep residual learning for image recognition. In: Presented at
IEEE conference on CVPR
13. Cho K, Van Merrienboer B, Gulcehre C et al (2014) Learning phrase representations using
RNN encoder-decoder for statistical machine translation. Comput Sci
14. Amodei D, Anubhai R, Battenberg E et al (2015) Deep speech 2: end-to-end speech
recognition in English and Mandarin. Comput Sci
15. Graves A (2013) Generating sequences with recurrent neural networks. Comput Sci
16. Graves A, Jaitly N. Towards end-to-end speech recognition with recurrent neural networks.
In: Presented at the 31st international conference on ML
17. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–
1780
18. Jozefowicz R, Zaremba W, Sutskever I An empirical exploration of recurrent network
architectures. In: Presented at international conference on machine learning
19. Chung J, Gulcehre C, Cho KH et al (2014) Empirical evaluation of gated recurrent neural
networks on sequence modeling. Comput Sci
20. Zeiler MD (2012) ADADELTA: an adaptive learning rate method. Comput Sci
21. Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural
networks from overfitting. J Mach Learn Res 15(1):1929–1958
22. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by
reducing internal covariate shift. Comput Sci
23. Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison
studies. The Statistician 32(3):307–317
272
C. Wang et al.