Data-driven assessment of cardiovascular ageing through multisite photoplethysmography and electrocardiography

Medical Engineering & Physics - Tập 73 - Trang 39-50 - 2019
Antonio M. Chiarelli1, Francesco Bianco1,2, David Perpetuini1, Valentina Bucciarelli1,2, Chiara Filippini1, Daniela Cardone1, Filippo Zappasodi1, Sabina Gallina1,2, Arcangelo Merla1
1Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti 66100, Italy
2Institute of Cardiology, University G. D’Annunzio of Chieti-Pescara, Via Dei Vestini 5, 66100, Chieti, Italy

Tài liệu tham khảo

2011 Arnett, 1994, Arterial stiffness: a new cardiovascular risk factor?, Am J Epidemiol, 140, 669, 10.1093/oxfordjournals.aje.a117315 Laurent, 2006, Expert consensus document on arterial stiffness: methodological issues and clinical applications, Eur Heart J, 27, 2588, 10.1093/eurheartj/ehl254 Mattace-Raso, 2006, Arterial stiffness and risk of coronary heart disease and stroke: the Rotterdam study, Circulation, 113, 657, 10.1161/CIRCULATIONAHA.105.555235 Vlachopoulos, 2010, Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis, J Am Coll Cardiol, 55, 1318, 10.1016/j.jacc.2009.10.061 Yousef, 2012, The analysis of PPG morphology: investigating the effects of aging on arterial compliance, Meas Sci Rev, 12, 266, 10.2478/v10048-012-0036-3 Hansen, 2006, Prognostic value of aortic pulse wave velocity as index of arterial stiffness in the general population, Circulation, 113, 664, 10.1161/CIRCULATIONAHA.105.579342 O'Rourke, 2007, Mechanical factors in arterial aging: a clinical perspective, J Am Coll Cardiol, 50, 1, 10.1016/j.jacc.2006.12.050 Anliker, 1968, Transmission characteristics of axial waves in blood vessels, J Biomech, 1, 235, 10.1016/0021-9290(68)90019-5 Avolio, 1983, Effects of aging on changing arterial compliance and left ventricular load in a northern Chinese urban community, Circulation, 68, 50, 10.1161/01.CIR.68.1.50 Nitzan, 2002, The difference in pulse transit time to the toe and finger measured by photoplethysmography, Physiol Meas, 23, 85, 10.1088/0967-3334/23/1/308 Pilt, 2013, New photoplethysmographic signal analysis algorithm for arterial stiffness estimation, Sci World J, 10.1155/2013/169035 Mackenzie, 1902, The study of the pulse, arterial, venous and hepatic and of the movements of the heart, J Am Med Assoc, 648 Kelly, 1989, Noninvasive determination of age-related changes in the human arterial pulse, Circulation, 80, 1652, 10.1161/01.CIR.80.6.1652 Allen, 2007, Photoplethysmography and its application in clinical physiological measurement, Physiol Meas, 28, R1, 10.1088/0967-3334/28/3/R01 Mancini, 1994, Validation of near-infrared spectroscopy in humans, J Appl Physiol, 77, 2740, 10.1152/jappl.1994.77.6.2740 Rusch, 1996, Signal processing methods for pulse oximetry, Comput Biol Med, 26, 143, 10.1016/0010-4825(95)00049-6 Chiarelli, 2016, Combining energy and Laplacian regularization to accurately retrieve the depth of brain activity of diffuse optical tomographic data, J Biomed Opt, 21, 36008, 10.1117/1.JBO.21.3.036008 Takazawa, 1998, Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform, Hypertension, 32, 365, 10.1161/01.HYP.32.2.365 Monte-Moreno, 2011, Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques, Artif Intell Med, 53, 127, 10.1016/j.artmed.2011.05.001 Nasrabadi, 2007, Pattern recognition and machine learning, J Electron Imaging, 16 Bianchini, 2014, On the complexity of neural network classifiers: a comparison between shallow and deep architectures, IEEE Trans Neural Netw Learn Syst, 25, 1553, 10.1109/TNNLS.2013.2293637 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Hecht-Nielsen, 1992, III.3 – Theory of the backpropagation neural network*, 65 Kingma DP, Ba J. Adam: a method for stochastic optimization. ArXiv:14126980 Cs 2014. Maas, 2013, Rectifier nonlinearities improve neural network acoustic models, 30, 3 Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks. ArXiv:12115063 Cs2012. Chiarelli, 2018, Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification, J Neural Eng, 15, 10.1088/1741-2552/aaaf82 Collobert, 2008, A unified architecture for natural language processing: deep neural networks with multitask learning, 160 Hinton, 2012, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE Signal Process Mag, 29, 82, 10.1109/MSP.2012.2205597 Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, 25, 1097 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv:14091556 Cs2014. Ciresan, 2012, Deep neural networks segment neuronal membranes in electron microscopy images, 25, 2843 Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. ArXiv:150504597 Cs2015. Croce, 2018, Deep convolutional neural networks for feature-less automatic classification of independent components in multi-channel electrophysiological brain recordings, IEEE Trans Biomed Eng Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences. ArXiv:14042188 Cs2014. Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 Chiarelli, 2017, Low-resolution mapping of the effective attenuation coefficient of the human head: a multidistance approach applied to high-density optical recordings, Neurophotonics, 4, 10.1117/1.NPh.4.2.021103 Zijlstra, 1991, Absorption spectra of human fetal and adult oxyhemoglobin, de-oxyhemoglobin, carboxyhemoglobin, and methemoglobin, Clin Chem, 37, 1633, 10.1093/clinchem/37.9.1633 Chiarelli, 2017, Simultaneous functional near-infrared spectroscopy and electroencephalography for monitoring of human brain activity and oxygenation: a review, Neurophotonics, 4, 10.1117/1.NPh.4.4.041411 Harris, 1994, NIRS in adults – effects of increasing optode separation, 837 Chiarelli, 2017, Characterization of a fiber-less, multichannel optical probe for continuous wave functional near-infrared spectroscopy based on silicon photomultipliers detectors: in-vivo assessment of primary sensorimotor response, Neurophotonics, 4, 10.1117/1.NPh.4.3.035002 Witten, 2016 Sutskever, 2013, On the importance of initialization and momentum in deep learning, 1139 Hastie, 2009 Kohavi, 1995, A study of cross-validation and bootstrap for accuracy estimation and model selection, 1137 Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. ArXiv:160304467 Cs2016. Johnson, 2006, Multivariate analysis, Encycl Stat Sci Boyd, 2011, Atrial strain rate is a sensitive measure of alterations in atrial phasic function in healthy ageing, Heart, 97, 1513, 10.1136/heartjnl-2011-300134 Möller-Leimkühler, 2007, Gender differences in cardiovascular disease and comorbid depression, Dialogues Clin Neurosci, 9, 71, 10.31887/DCNS.2007.9.1/ammoeller McEniery, 2005, Normal vascular aging: differential effects on wave reflection and aortic pulse wave velocity: the anglo-cardiff collaborative trial (ACCT), J Am Coll Cardiol, 46, 1753, 10.1016/j.jacc.2005.07.037 Hsu, 2012, Association of arterial stiffness and electrocardiography-determined left ventricular hypertrophy with left ventricular diastolic dysfunction, PLoS One, 7, 10.1371/journal.pone.0049100 Rossouw, 2002, Hormones, genetic factors, and gender differences in cardiovascular disease, Cardiovasc Res, 53, 550, 10.1016/S0008-6363(01)00478-3