Stratification of cardiopathies using photoplethysmographic signals
Tài liệu tham khảo
Organization
Fan, 2017, A motion-tolerant approach for monitoring spo2 and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion, Comput Biol Med, 91, 291, 10.1016/j.compbiomed.2017.10.017
Birrenkott, 2017, A robust fusion model for estimating respiratory rate from photoplethysmography and electrocardiography, IEEE (Inst Electr Electron Eng) Trans Biomed Eng, 65, 2033
Dao, 2017, A robust motion artifact detection algorithm for accurate detection of heart rates from photoplethysmographic signals using time–frequency spectral features, IEEE Journal of Biomedical and Health Informatics, 21, 1242, 10.1109/JBHI.2016.2612059
Papini, 2017, Photoplethysmography beat detection and pulse morphology quality assessment for signal reliability estimation, 117
Massaroni, 2019, Contactbased methods for measuring respiratory rate, Sensors, 19, 908, 10.3390/s19040908
Hemon, 2016, Comparison of foot finding methods for deriving instantaneous pulse rates from photoplethysmographic signals, J Clin Monit Comput, 30, 157, 10.1007/s10877-015-9695-6
Charlton, 2017, Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review, IEEE reviews in biomedical engineering, 11, 2, 10.1109/RBME.2017.2763681
Peter, 2016, vol. 49, 284
Bhattacharya, 2001, Analysis and characterization of photoplethysmographic signal, IEEE (Inst Electr Electron Eng) Trans Biomed Eng, 48, 5
Meredith, 2012, Photoplethysmographic derivation of respiratory rate: a review of relevant physiology, J Med Eng Technol, 36, 1, 10.3109/03091902.2011.638965
Lu, 2009, A comparison of photoplethysmography and ecg recording to analyse heart rate variability in healthy subjects, J Med Eng Technol, 33, 634, 10.3109/03091900903150998
Lin, 2014, Comparison of heart rate variability from ppg with that from ecg, 213
Wu, 2013, Multiscale cross-approximate entropy analysis as a measurement of complexity between ecg rr interval and ppg pulse amplitude series among the normal and diabetic subjects, Computational and mathematical methods in medicine, 2013
D. Zhao, Y. Sun, S. Wan, F. Wang, SFST: a robust framework for heart rate monitoring from photoplethysmography signals during physical activities, Biomed Signal Process Contr 33 316–324.
Pradhan, 2017, Classification of the quality of wristbandbased photoplethysmography signals, 269
Moraes, 2018, Advances in photopletysmography signal analysis for biomedical applications, Sensors, 18, 1894, 10.3390/s18061894
Wu, 2018, Adaptive computing-based biometric security for intelligent medical applications, Neural Comput Appl, 1
Dec, 1994, Idiopathic dilated cardiomyopathy, N Engl J Med, 331, 1564, 10.1056/NEJM199412083312307
Morillo, 2015, Randomized trial of benznidazole for chronic chagas' cardiomyopathy, N Engl J Med, 373, 1295, 10.1056/NEJMoa1507574
Beltrami, 1994, Structural basis of end-stage failure in ischemic cardiomyopathy in humans, Circulation, 89, 151, 10.1161/01.CIR.89.1.151
Chaves, 2018, Nutritional status and quality of life of candidates for heart transplantation, Rev Bras em Promoção Saúde, 31
Mont'Alverne, 2012, Clinical and functional capacity of patients with dilated cardiomyopathy after four years of transplantation, Braz J Cardiovasc Surg, 27, 562, 10.5935/1678-9741.20120098
de Oliveira Carlos, 2008, Impact of weight variation on the metabolic stability of cardiac transplant patients in the state of ceara, Arq Bras Cardiol, 90, 268
Custódio, 2013, Results of medium-term survival in patients undergoing cardiac transplantation: institutional experience, Braz J Cardiovasc Surg, 28, 470, 10.5935/1678-9741.20130077
Nour, 2020, Automatic classification of hypertension types based on personal features by machine learning algorithms, Math Probl Eng, 2020
Uçar, 2017, Automatic detection of respiratory arrests in osa patients using ppg and machine learning techniques, Neural Comput Appl, 28, 2931, 10.1007/s00521-016-2617-9
Du, 2018, Levenberg-marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor, Sensors, 18, 2322, 10.3390/s18072322
Shobitha, 2016, Recognizing cardiovascular risk from photoplethysmogram signals using elm, 1
Paradkar, 2017, Coronary artery disease detection using photoplethysmography, 100
Silvetti, 2001, Heart rate variability in healthy children and adolescents is partially related to age and gender, Int J Cardiol, 81, 169, 10.1016/S0167-5273(01)00537-X
Vanderlei, 2009, Basic notions of heart rate variability and its clinical applicability, Braz J Cardiovasc Surg, 24, 205, 10.1590/S0102-76382009000200018
D. Nunan, G. R. Sandercock, D. A. Brodie, A quantitative systematic review of normal values for short-term heart rate variability in healthy adults, Pacing Clin Electrophysiol 33 (11).
Hejjel, 2001, Heart rate variability analysis, Acta Physiol Hung, 88, 219, 10.1556/APhysiol.88.2001.3-4.4
Incorporated
Pflugradt, 2017, A fast multimodal ectopic beat detection method applied for blood pressure estimation based on pulse wave velocity measurements in wearable sensors, Sensors, 17
Krzywinski, 2013
Task, 1996, Force of the european society of cardiology, heart rate variability, standards of measurement, physiological interpretation, and clinical use, Circulation, 93, 1043, 10.1161/01.CIR.93.5.1043
Gardner, 1998, Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences, Atmos Environ, 32, 2627, 10.1016/S1352-2310(97)00447-0
Kavsaoğlu, 2015, Non-invasive prediction of hemoglobin level using machine learning techniques with the ppg signal's characteristics features, Appl Soft Comput, 37, 983, 10.1016/j.asoc.2015.04.008
Li, 2012, Dynamic time warping and machine learning for signal quality assessment of pulsatile signals, Physiol Meas, 33, 1491, 10.1088/0967-3334/33/9/1491
Shankaracharya, 2012, Computational intelligence-based diagnosis tool for the detection of prediabetes and type 2 diabetes in India, the review of diabetic studies, Reg Dev Stud, 9, 55
Melssen, 2006, Supervised kohonen networks for classification problems, Chemometr Intell Lab Syst, 83, 99, 10.1016/j.chemolab.2006.02.003
Nousou, 2006, Classification of acceleration plethysmogram using self-organizing map, 681
Dhillon, 2004, Kernel k-means: spectral clustering and normalized cuts, 551