Heart Rate Variability Based Prediction of Personalized Drug Therapeutic Response: The Present Status and the Perspectives

Author(s): Zejun Pei, Manhong Shi, Junping Guo*, Bairong Shen*

Journal Name: Current Topics in Medicinal Chemistry

Volume 20 , Issue 18 , 2020

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Heart rate variability (HRV) signals are reported to be associated with the personalized drug response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc. But the relationships between HRV signals and the personalized drug response in different diseases and patients are complex and remain unclear. With the fast development of modern smart sensor technologies and the popularization of big data paradigm, more and more data on the HRV and drug response will be available, it then provides great opportunities to build models for predicting the association of the HRV with personalized drug response precisely. We here review the present status of the HRV data resources and models for predicting and evaluating of personalized drug responses in different diseases. The future perspectives on the integration of knowledge and personalized data at different levels such as, genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of drug therapy and their response will be provided.

Keywords: Heart rate variability, Computational models, Personalized drug therapy, Prediction of drug response, Brain activity, Respiration signals.

Tsoli, M.; Wadham, C.; Pinese, M.; Failes, T.; Joshi, S.; Mould, E.; Yin, J.X.; Gayevskiy, V.; Kumar, A.; Kaplan, W.; Ekert, P.G.; Saletta, F.; Franshaw, L.; Liu, J.; Gifford, A.; Weber, M.A.; Rodriguez, M.; Cohn, R.J.; Arndt, G.; Tyrrell, V.; Haber, M.; Trahair, T.; Marshall, G.M.; McDonald, K.; Cowley, M.J.; Ziegler, D.S. Integration of genomics, high throughput drug screening, and personalized xenograft models as a novel precision medicine paradigm for high risk pediatric cancer. Cancer Biol. Ther., 2018, 19(12), 1078-1087.
[http://dx.doi.org/10.1080/15384047.2018.1491498] [PMID: 30299205]
Rhine, C.L.; Neil, C.; Glidden, D.T.; Cygan, K.J.; Fredericks, A.M.; Wang, J.; Walton, N.A.; Fairbrother, W.G. Future directions for high-throughput splicing assays in precision medicine. Hum. Mutat., 2019, 40(9), 1225-1234.
[http://dx.doi.org/10.1002/humu.23866] [PMID: 31297895]
Kumari, P.; Mathew, L.; Syal, P. Increasing trend of wearables and multimodal interface for human activity monitoring: A review. Biosens. Bioelectron., 2017, 90, 298-307.
[http://dx.doi.org/10.1016/j.bios.2016.12.001] [PMID: 27931004]
Horgan, D.; Romao, M.; Morré, S.A.; Kalra, D. Artificial intelligence: power for civilisation - and for better healthcare. Public Health Genomics, 2019, 22(5-6), 145-161.
[http://dx.doi.org/10.1159/000504785] [PMID: 31838476]
Bai, J.; Shen, L.; Sun, H.; Shen, B. Physiological informatics: collection and analyses of data from wearable sensors and smartphone for healthcare. Adv. Exp. Med. Biol., 2017, 1028, 17-37.
[http://dx.doi.org/10.1007/978-981-10-6041-0_2] [PMID: 29058214]
Kos, M.; Xuan, Li.; Khaghani-Far, I.; Gordon, C.M.; Pavel, M.; Jimison, H.B. Can accelerometry data improve estimates of heart rate variability from wrist pulse PPG sensors? Conf. Proc. IEEE Eng. Med. Biol. Soc., 2017, 2017, 1587-1590.
[http://dx.doi.org/10.1109/EMBC.2017.8037141] [PMID: 29060185]
Davila, M.I.; Lewis, G.F.; Porges, S.W. The physiocam: a novel non-contact sensor to measure heart rate variability in clinical and field applications. Front. Public Health, 2017, 5, 300.
[http://dx.doi.org/10.3389/fpubh.2017.00300] [PMID: 29214150]
Aygun, A.; Ghasemzadeh, H.; Jafari, R. Robust interbeat interval and heart rate variability estimation method from various morphological features using wearable sensors. IEEE J. Biomed. Health Inform, 2019. [Online ahead of Print]
[PMID: 31899444]
Yuan, Y.; Van Allen, E.M.; Omberg, L.; Wagle, N.; Amin-Mansour, A.; Sokolov, A.; Byers, L.A.; Xu, Y.; Hess, K.R.; Diao, L.; Han, L.; Huang, X.; Lawrence, M.S.; Weinstein, J.N.; Stuart, J.M.; Mills, G.B.; Garraway, L.A.; Margolin, A.A.; Getz, G.; Liang, H. Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat. Biotechnol., 2014, 32(7), 644-652.
[http://dx.doi.org/10.1038/nbt.2940] [PMID: 24952901]
Arbo, J.E.; Lessing, J.K.; Ford, W.J.H.; Clark, S.; Finkelsztein, E.; Schenck, E.J.; Sharma, R.; Heerdt, P.M. Heart rate variability measures for prediction of severity of illness and poor outcome in ED patients with sepsis. Am. J. Emerg. Med., 2020. In Press
[http://dx.doi.org/10.1016/j.ajem.2020.01.012] [PMID: 31982224]
Zhang, L.; Wu, H.; Zhang, X.; Wei, X.; Hou, F.; Ma, Y. Sleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes. Sleep Med., 2020, 67, 217-224.
[http://dx.doi.org/10.1016/j.sleep.2019.11.1259] [PMID: 31972509]
Wang, Y.; Zhang, C.; Chen, S.; Liu, P.; Wang, Y.; Tang, C.; Jin, H.; Du, J. Heart rate variability predicts therapeutic response to metoprolol in children with postural tachycardia syndrome. Front. Neurosci., 2019, 13, 1214.
[http://dx.doi.org/10.3389/fnins.2019.01214] [PMID: 31780890]
Kircanski, K.; Williams, L.M.; Gotlib, I.H. Heart rate variability as a biomarker of anxious depression response to antidepressant medication. Depress. Anxiety, 2019, 36(1), 63-71.
[http://dx.doi.org/10.1002/da.22843] [PMID: 30311742]
Billeci, L.; Marino, D.; Insana, L.; Vatti, G.; Varanini, M. Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis. PLoS One, 2018, 13(9)e0204339
[http://dx.doi.org/10.1371/journal.pone.0204339] [PMID: 30252915]
Razjouyan, J.; Grewal, G.S.; Rishel, C.; Parthasarathy, S.; Mohler, J.; Najafi, B. Activity monitoring and heart rate variability as indicators of fall risk: proof-of-concept for application of wearable sensors in the acute care setting. J. Gerontol. Nurs., 2017, 43(7), 53-62.
[http://dx.doi.org/10.3928/00989134-20170223-01] [PMID: 28253410]
Fantozzi, M.P.T.; Artoni, F.; Faraguna, U. Heart rate variability at bedtime predicts subsequent sleep features. Conf. Proc. IEEE Eng. Med. Biol. Soc., 2019, 2019, 6784-6788.
[http://dx.doi.org/10.1109/EMBC.2019.8857844] [PMID: 31947398]
Choudhary, A.K.; Alam, T.; Jiwane, R.; Kishanrao, S.S. A comparative analysis of dietary habits on sensory motor association and heart rate variability during menstrual cycle. J. Clin. Diagn. Res., 2016, 10(1), CC04-CC08.
[http://dx.doi.org/10.7860/JCDR/2016/16421.7068] [PMID: 26894059]
Buendia, R.; Forcolin, F.; Karlsson, J.; Arne Sjöqvist, B.; Anund, A.; Candefjord, S. Deriving heart rate variability indices from cardiac monitoring-An indicator of driver sleepiness. Traffic Inj. Prev., 2019, 20(3), 249-254.
[http://dx.doi.org/10.1080/15389588.2018.1548766] [PMID: 30978124]
Estévez-Báez, M.; Carricarte-Naranjo, C.; Jas-García, J.D.; Rodríguez-Ríos, E.; Machado, C.; Montes-Brown, J.; Leisman, G.; Schiavi, A.; Machado-García, A.; Luaces, C.S.; Pié, E.A. Influence of heart rate, age, and gender on heart rate variability in adolescents and young adults. Adv. Exp. Med. Biol., 2019, 1133, 19-33.
[http://dx.doi.org/10.1007/5584_2018_292] [PMID: 30414070]
Jeon, S.; Oh, S.; Cho, S.J.; Lee, Y.J.; Kim, S.J. Association between snoring and heart rate variability in adolescents: effects of gender and insufficient sleep. Sleep Breath., 2019.
[http://dx.doi.org/10.1007/s11325-019-01883-7] [PMID: 31332620]
Kumral, D.; Schaare, H.L.; Beyer, F.; Reinelt, J.; Uhlig, M.; Liem, F.; Lampe, L.; Babayan, A.; Reiter, A.; Erbey, M.; Roebbig, J.; Loeffler, M.; Schroeter, M.L.; Husser, D.; Witte, A.V.; Villringer, A.; Gaebler, M. The age-dependent relationship between resting heart rate variability and functional brain connectivity. Neuroimage, 2019, 185, 521-533.
[http://dx.doi.org/10.1016/j.neuroimage.2018.10.027] [PMID: 30312808]
Goldberger, A.L.; Amaral, L.A.; Glass, L.; Hausdorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietus, J.E.; Moody, G.B.; Peng, C.K.; Stanley, H.E. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23), E215-E220.
[http://dx.doi.org/10.1161/01.CIR.101.23.e215] [PMID: 10851218]
Banerjee, S.; Palit, S.K.; Mukherjee, S.; Ariffin, M.R.; Rondoni, L. Complexity in congestive heart failure: A time-frequency approach. Chaos, 2016, 26(3)033105
[http://dx.doi.org/10.1063/1.4941374] [PMID: 27036183]
de Chazal, P.; O’Dwyer, M.; Reilly, R.B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng., 2004, 51(7), 1196-1206.
[http://dx.doi.org/10.1109/TBME.2004.827359] [PMID: 15248536]
Lin, K.P.; Chang, W.H. QRS feature extraction using linear prediction. IEEE Trans. Biomed. Eng., 1989, 36(10), 1050-1055.
[http://dx.doi.org/10.1109/10.40806] [PMID: 2793197]
Ji, L.; Liu, C.; Li, P.; Wang, X.; Yan, C.; Liu, C. Comparison of heart rate variability between resting state and external-cuff-inflation-and-deflation state: a pilot study. Physiol. Meas., 2015, 36(10), 2135-2146.
[http://dx.doi.org/10.1088/0967-3334/36/10/2135] [PMID: 26333766]
Afonso, V.X.; Tompkins, W.J.; Nguyen, T.Q.; Luo, S. ECG beat detection using filter banks. IEEE Trans. Biomed. Eng., 1999, 46(2), 192-202.
[http://dx.doi.org/10.1109/10.740882] [PMID: 9932341]
Khazaee, A.; Ebrahimzadeh, A. Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features. Biomed. Signal Process. Control, 2010, 5, 252-263.
Minami, K.; Nakajima, H.; Toyoshima, T. Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans. Biomed. Eng., 1999, 46(2), 179-185.
[http://dx.doi.org/10.1109/10.740880] [PMID: 9932339]
Chae, J.H.; Jeong, J.; Peterson, B.S.; Kim, D.J.; Bahk, W.M.; Jun, T.Y.; Kim, S.Y.; Kim, K.S. Dimensional complexity of the EEG in patients with posttraumatic stress disorder. Psychiatry Res., 2004, 131(1), 79-89.
[http://dx.doi.org/10.1016/j.pscychresns.2003.05.002] [PMID: 15246457]
Nikolaev, A.; Gong, P.; Leeuwen, C. Dynamical properties of global phase synchronization patterns in human EEG. Phys. Rev. B, 2005, 62, 2321-2329.
Müller, V.; Lutzenberger, W.; Preissl, H.; Pulvermüller, F.; Birbaumer, N. Complexity of visual stimuli and non-linear EEG dynamics in humans. Brain Res. Cogn. Brain Res., 2003, 16(1), 104-110.
[http://dx.doi.org/10.1016/S0926-6410(02)00225-2] [PMID: 12589895]
Klonowski, W.; Jernajczyk, W.; Niedzielska, K.; Rydz, A.; Stepień, R. Quantitative measure of complexity of EEG signal dynamics. Acta Neurobiol. Exp. (Warsz.), 1999, 59(4), 315-321.
[PMID: 10645636]
Acharya, U.R.; Kannathal, N.; Krishnan, S.M. Comprehensive analysis of cardiac health using heart rate signals. Physiol. Meas., 2004, 25(5), 1139-1151.
[http://dx.doi.org/10.1088/0967-3334/25/5/005] [PMID: 15535180]
Martis, R.J.; Acharya, U.R.; Tan, J.H.; Petznick, A.; Yanti, R.; Chua, C.K.; Ng, E.Y.; Tong, L. Application of empirical mode decomposition (emd) for automated detection of epilepsy using EEG signals. Int. J. Neural Syst., 2012, 22(6)1250027
[http://dx.doi.org/10.1142/S012906571250027X] [PMID: 23186276]
Pincus, S.M. Assessing serial irregularity and its implications for health. Ann. N. Y. Acad. Sci., 2001, 954, 245-267.
[http://dx.doi.org/10.1111/j.1749-6632.2001.tb02755.x] [PMID: 11797860]
Fleisher, L.A.; Pincus, S.M.; Rosenbaum, S.H. Approximate entropy of heart rate as a correlate of postoperative ventricular dysfunction. Anesthesiology, 1993, 78(4), 683-692.
[http://dx.doi.org/10.1097/00000542-199304000-00011] [PMID: 8466069]
Pincus, S.M.; Keefe, D.L. Quantification of hormone pulsatility via an approximate entropy algorithm. Am. J. Physiol., 1992, 262(5 Pt 1), E741-E754.
[PMID: 1590385]
Gonçalves, H.; Henriques-Coelho, T.; Bernardes, J.; Rocha, A.P.; Nogueira, A.; Leite-Moreira, A. Linear and nonlinear heart-rate analysis in a rat model of acute anoxia. Physiol. Meas., 2008, 29(9), 1133-1143.
[http://dx.doi.org/10.1088/0967-3334/29/9/010] [PMID: 18784391]
Liu, C.; Li, K.; Zhao, L.; Liu, F.; Zheng, D.; Liu, C.; Liu, S. Analysis of heart rate variability using fuzzy measure entropy. Comput. Biol. Med., 2013, 43(2), 100-108.
[http://dx.doi.org/10.1016/j.compbiomed.2012.11.005] [PMID: 23273774]
Xiang, J.; Li, C.; Li, H.; Cao, R.; Wang, B.; Han, X.; Chen, J. The detection of epileptic seizure signals based on fuzzy entropy. J. Neurosci. Methods, 2015, 243, 18-25.
[http://dx.doi.org/10.1016/j.jneumeth.2015.01.015] [PMID: 25614384]
Kumbhakar, M.; Ghoshal, K. One-Dimensional velocity distribution in open channels using Renyi entropy. Stochastic Environ. Res. Risk Assess., 2016, 31, 949-959.
Cornforth, D.J.; Tarvainen, M.P.; Jelinek, H.F. How to calculate renyi entropy from heart rate variability, and why it matters for detecting cardiac autonomic neuropathy. Front. Bioeng. Biotechnol., 2014, 2, 34.
[http://dx.doi.org/10.3389/fbioe.2014.00034] [PMID: 25250311]
Bandt, C.; Pompe, B. Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett., 2002, 88(17)174102
[http://dx.doi.org/10.1103/PhysRevLett.88.174102] [PMID: 12005759]
Manis, G.; Aktaruzzaman, M.; Sassi, R. Bubble Entropy: an entropy almost free of parameters. IEEE Trans. Biomed. Eng., 2017, 64(11), 2711-2718.
[http://dx.doi.org/10.1109/TBME.2017.2664105] [PMID: 28182552]
Liu, T.; Yao, W.; Wu, M.; Shi, Z.; Wang, J.; Ning, X. Multiscale permutation entropy analysis of electrocardiogram. Physica A, 2017, 471, 492-498.
Zheng, J.; Pan, H.; Yang, S.; Cheng, J. Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis. Mech. Syst. Signal Process., 2018, 99, 229-243.
Azami, H.; Fernández, A.; Escudero, J. Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis. Med. Biol. Eng. Comput., 2017, 55(11), 2037-2052.
[http://dx.doi.org/10.1007/s11517-017-1647-5] [PMID: 28462498]
Oresko, J.J.; Duschl, H.; Cheng, A.C. A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Trans. Inf. Technol. Biomed., 2010, 14(3), 734-740.
[http://dx.doi.org/10.1109/TITB.2010.2047865] [PMID: 20388600]
He, H.; Tan, Y. Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering. Appl. Soft Comput., 2017, 55, 238-252.
He, T.; Clifford, G.; Tarassenko, L. Application of ICA in removing artefacts from the ECG. Neural Process. Lett., 2006, 15, 105-116.
Henzel, N. ECG baseline wander and power line interference reduction using nonlinear filter bank. Signal Processing, 2005, 85(4), 781-793.
Daqrouq, K. ECG signal denoising by wavelet transform thresholdin. Am. J. Appl. Sci., 2008, 5(3), 5.
Kabir, M.; Shahnaz, C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed. Signal Process. Control, 2012, 7(5), 481-489.
Dragomiretskiy, K. Variational mode decomposition. IEEE Trans. Signal Process., 2014, 62, 531-544.
Lahmiri, S. Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthc. Technol. Lett., 2014, 1(3), 104-109.
[http://dx.doi.org/10.1049/htl.2014.0073] [PMID: 26609387]
Rakshit, M.; Panigrahy, D.; Sahu, P. An improved method for R-peak detection by using Shannon energy envelope. Sadhana, 2016, 41, 469-477.
Kadambe, S.; Murray, R.; Boudreaux-Bartels, G.F. Wavelet transform-based QRS complex detector. IEEE Trans. Biomed. Eng., 1999, 46(7), 838-848.
[http://dx.doi.org/10.1109/10.771194] [PMID: 10396902]
Martínez, J.P.; Almeida, R.; Olmos, S.; Rocha, A.P.; Laguna, P. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng., 2004, 51(4), 570-581.
[http://dx.doi.org/10.1109/TBME.2003.821031] [PMID: 15072211]
Okada, M. A digital filter for the QRS complex detection. IEEE Trans. Biomed. Eng., 1979, 26(12), 700-703.
[http://dx.doi.org/10.1109/TBME.1979.326461] [PMID: 544443]
Nygårds, M.E.; Sörnmo, L. Delineation of the QRS complex using the envelope of the e.c.g. Med. Biol. Eng. Comput., 1983, 21(5), 538-547.
[http://dx.doi.org/10.1007/BF02442378] [PMID: 6633003]
Coast, D.A.; Stern, R.M.; Cano, G.G.; Briller, S.A. An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans. Biomed. Eng., 1990, 37(9), 826-836.
[http://dx.doi.org/10.1109/10.58593] [PMID: 2227969]
Pan, J.; Tompkins, W.J. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng., 1985, 32(3), 230-236.
[http://dx.doi.org/10.1109/TBME.1985.325532] [PMID: 3997178]
Raj, S.; Ray, K.C.; Shankar, O. Development of robust, fast and efficient QRS complex detector: a methodological review. Australas. Phys. Eng. Sci. Med., 2018, 41(3), 581-600.
[http://dx.doi.org/10.1007/s13246-018-0670-7] [PMID: 30117043]
Acharya, U.R.; Fujita, H.; Sudarshan, V.K.; Lih Oh, S.; Muhammad, A.; Koh, J.E.W.; Hong, Tan. J.; Chua, C.K.; Poo Chua, K.; San Tan, R. Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals. Neural Comput. Appl., 2016, 28, 3073-3094.
Kumar, M.; Pachori, R.B.; Acharya, U.R. An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals. Expert Syst. Appl., 2016, 63, 165-172.
Tripathy, R.K.; Sharma, L.N.; Dandapat, S. Detection of shockable ventricular arrhythmia using variational mode decomposition. J. Med. Syst., 2016, 40(4), 79.
[http://dx.doi.org/10.1007/s10916-016-0441-5] [PMID: 26798076]
Acharya, U.R.; Fujita, H.; Sudarshan, V.K.; Oh, S.L.; Adam, M.; Koh, J.E.W.; Tan, J.H.; Ghista, D.N.; Martis, R.J.; Chua, C.K.; Poo, C.K.; Tan, R.S. Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl. Base. Syst., 2016, 99, 146-156.
Acharya, U.R.; Fujita, H.; Sudarshan, V.K.; Oh, S.L.; Adam, M.; Tan, C.; Koo, J.H.; Jain, A.; Lim, C.M.; Chua, C.K. Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal. Knowl. Base. Syst., 2017, 132(5), 156-166.
Amann, A.; Tratnig, R.; Unterkofler, K. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. Biomed. Eng. Online, 2005, 4, 60.
[http://dx.doi.org/10.1186/1475-925X-4-60] [PMID: 16253134]
Rajesh, K.N.V.P.S.; Dhuli, R. Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. Comput. Biol. Med., 2017, 87, 271-284.
[http://dx.doi.org/10.1016/j.compbiomed.2017.06.006] [PMID: 28624712]
Taiyong Li1, Min Zhou1. ECG classification using wavelet packet entropy and random forests. Entropy (Basel), 2016, 16(8), 285.
Shi, M.; Zhan, C.; He, H.; Jin, Y.; Wu, R.; Sun, Y.; Shen, B. Renyi Distribution entropy analysis of short-term heart rate variability signals and its application in coronary artery disease detection. Front. Physiol., 2019, 10, 809.
[http://dx.doi.org/10.3389/fphys.2019.00809] [PMID: 31293457]
Bugalho, P.; Mendonça, M.; Lampreia, T.; Miguel, R.; Barbosa, R.; Salavisa, M. Heart rate variability in Parkinson disease and idiopathic REM sleep behavior disorder. Clin. Auton. Res., 2018, 28(6), 557-564.
[http://dx.doi.org/10.1007/s10286-018-0557-4] [PMID: 30128681]
Kim, M.S.; Yoon, J.H.; Hong, J.M. Early differentiation of dementia with Lewy bodies and Alzheimer’s disease: Heart rate variability at mild cognitive impairment stage. Clin. Neurophysiol., 2018, 129(8), 1570-1578.
[http://dx.doi.org/10.1016/j.clinph.2018.05.004] [PMID: 29883835]
Ray, J.M.; Pyne, J.M.; Gevirtz, R.N. Alcohol use disorder moderates the effect of age on heart rate variability in veterans with posttraumatic stress disorder. J. Nerv. Ment. Dis., 2017, 205(10), 793-800.
[http://dx.doi.org/10.1097/NMD.0000000000000718] [PMID: 28727660]
Kloter, E.; Barrueto, K.; Klein, S.D.; Scholkmann, F.; Wolf, U. Heart rate variability as a prognostic factor for cancer survival - a systematic review. Front. Physiol., 2018, 9, 623.
[http://dx.doi.org/10.3389/fphys.2018.00623] [PMID: 29896113]
Albarado-Ibañez, A.; Arroyo-Carmona, R.E.; Sánchez-Hernández, R.; Ramos-Ortiz, G.; Frank, A.; García-Gudiño, D.; Torres-Jácome, J. The role of the autonomic nervous system on cardiac rhythm during the evolution of diabetes mellitus using heart rate variability as a biomarker. J. Diabetes Res., 2019, 20195157024
[http://dx.doi.org/10.1155/2019/5157024] [PMID: 31211146]
Ha, D.; Malhotra, A.; Ries, A.L.; O’Neal, W.T.; Fuster, M.M. Heart rate variability and heart rate recovery in lung cancer survivors eligible for long-term cure. Respir. Physiol. Neurobiol., 2019, 269103264
[http://dx.doi.org/10.1016/j.resp.2019.103264] [PMID: 31376471]
Luo, H.; Wei, J.; Yasin, Y.; Wu, S.J.; Barszczyk, A.; Feng, Z.P.; Lee, K. Stress determined through heart rate variability predicts immune function. Neuroimmunomodulation, 2019, 26(4), 167-173.
[http://dx.doi.org/10.1159/000500863] [PMID: 31408866]
Shi, B.; Wang, L.; Yan, C.; Chen, D.; Liu, M.; Li, P. Nonlinear heart rate variability biomarkers for gastric cancer severity: A pilot study. Sci. Rep., 2019, 9(1), 13833.
[http://dx.doi.org/10.1038/s41598-019-50358-y] [PMID: 31554856]
Souza, R.A.; Beltran, O.A.B.; Zapata, D.M.; Silva, E.; Freitas, W.Z.; Junior, R.V.; da Silva, F.F.; Higino, W.P. Heart rate variability, salivary cortisol and competitive state anxiety responses during pre-competition and pre-training moments. Biol. Sport, 2019, 36(1), 39-46.
[http://dx.doi.org/10.5114/biolsport.2018.78905] [PMID: 30899138]
Vinayagam, S.; Panta, S.B.; Badhe, A.S.; Sharma, V.K. Heart rate variability as a predictor of hypotension after spinal anaesthesia in patients with diabetes mellitus. Indian J. Anaesth., 2019, 63(8), 671-673.
[http://dx.doi.org/10.4103/ija.IJA_13_19] [PMID: 31462816]
Hamm, W.; Bogner-Flatz, V.; Bauer, A.; Brunner, S. FIFA World Cup 2018: effect of emotional stress on conventional heart rate variability metrics. Clin. Res. Cardiol., 2020, 109(2), 266-270.
[http://dx.doi.org/10.1007/s00392-019-01533-8] [PMID: 31388740]
Lebech, A.M.; Kristoffersen, U.S.; Mehlsen, J.; Wiinberg, N.; Petersen, C.L.; Hesse, B.; Gerstoft, J.; Kjaer, A. Autonomic dysfunction in HIV patients on antiretroviral therapy: studies of heart rate variability. Clin. Physiol. Funct. Imaging, 2007, 27(6), 363-367.
[http://dx.doi.org/10.1111/j.1475-097X.2007.00760.x] [PMID: 17944658]
Zeng-Treitler, Q.; Nelson, S.J. Will artificial intelligence translate big data into improved medical care or be a source of confusing intrusion? a discussion between a (Cautious) physician informatician and an (optimistic) medical informatics researcher. J. Med. Internet Res., 2019, 21(11)e16272
[http://dx.doi.org/10.2196/16272] [PMID: 31774409]
Elenko, E.; Underwood, L.; Zohar, D. Defining digital medicine. Nat. Biotechnol., 2015, 33(5), 456-461.
[http://dx.doi.org/10.1038/nbt.3222] [PMID: 25965750]
Jia, Z.; Zeng, X.; Duan, H.; Lu, X.; Li, H. A patient-similarity-based model for diagnostic prediction. Int. J. Med. Inform., 2020, 135104073
[http://dx.doi.org/10.1016/j.ijmedinf.2019.104073] [PMID: 31923816]
Gao, M.; Igata, H.; Takeuchi, A.; Sato, K.; Ikegaya, Y. Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds. J. Pharmacol. Sci., 2017, 133(2), 70-78.
[http://dx.doi.org/10.1016/j.jphs.2017.01.003] [PMID: 28215473]
Liu, P.; Li, H.; Li, S.; Leung, K.S. Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network. BMC Bioinformatics, 2019, 20(1), 408.
[http://dx.doi.org/10.1186/s12859-019-2910-6] [PMID: 31357929]
Chesnokov, Y.V. Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods. Artif. Intell. Med., 2008, 43(2), 151-165.
[http://dx.doi.org/10.1016/j.artmed.2008.03.009] [PMID: 18455375]
Riganello, F.; Candelieri, A.; Quintieri, M.; Conforti, D.; Dolce, G. Heart rate variability: an index of brain processing in vegetative state? An artificial intelligence, data mining study. Clin. Neurophysiol., 2010, 121(12), 2024-2034.
[http://dx.doi.org/10.1016/j.clinph.2010.05.010] [PMID: 20566300]
Shen, L.; Lin, Y.; Sun, Z.; Yuan, X.; Chen, L.; Shen, B. Knowledge-guided bioinformatics model for identifying autism spectrum disorder diagnostic microRNA biomarkers. Sci. Rep., 2016, 6, 39663.
[http://dx.doi.org/10.1038/srep39663] [PMID: 28000768]
Zhan, C.; Shi, M.; Wu, R.; He, H.; Liu, X.; Shen, B. MIRKB: a myocardial infarction risk knowledge base. Database (Oxford), 2019, 2019baz125
Algra, A.; Tijssen, J.G.; Roelandt, J.R.; Pool, J.; Lubsen, J. QTc prolongation measured by standard 12-lead electrocardiography is an independent risk factor for sudden death due to cardiac arrest. Circulation, 1991, 83(6), 1888-1894.
[http://dx.doi.org/10.1161/01.CIR.83.6.1888] [PMID: 2040041]
Jain, S.K.; Bhaumik, B. An energy efficient ECG signal processor detecting cardiovascular diseases on smartphone. IEEE Trans. Biomed. Circuits Syst., 2017, 11(2), 314-323.
[http://dx.doi.org/10.1109/TBCAS.2016.2592382] [PMID: 28114077]
Tang, S.C.; Jen, H.I.; Lin, Y.H.; Hung, C.S.; Jou, W.J.; Huang, P.W.; Shieh, J.S.; Ho, Y.L.; Lai, D.M.; Wu, A.Y.; Jeng, J.S.; Chen, M.F. Complexity of heart rate variability predicts outcome in intensive care unit admitted patients with acute stroke. J. Neurol. Neurosurg. Psychiatry, 2015, 86(1), 95-100.
[http://dx.doi.org/10.1136/jnnp-2014-308389] [PMID: 25053768]
Tajiri, K.; Guichard, J.B.; Qi, X.; Xiong, F.; Naud, P.; Tardif, J.C.; Costa, A.D.; Aonuma, K.; Nattel, S. An N-/L-type calcium channel blocker, cilnidipine, suppresses autonomic, electrical, and structural remodelling associated with atrial fibrillation. Cardiovasc. Res., 2019, 115(14), 1975-1985.
[http://dx.doi.org/10.1093/cvr/cvz136] [PMID: 31119260]
Myers, K.A.; Bello-Espinosa, L.E.; Symonds, J.D.; Zuberi, S.M.; Clegg, R.; Sadleir, L.G.; Buchhalter, J.; Scheffer, I.E. Heart rate variability in epilepsy: A potential biomarker of sudden unexpected death in epilepsy risk. Epilepsia, 2018, 59(7), 1372-1380.
[http://dx.doi.org/10.1111/epi.14438] [PMID: 29873813]
Vanhoof-Villalba, S.L.; Gautier, N.M.; Mishra, V.; Glasscock, E. Pharmacogenetics of KCNQ channel activation in 2 potassium channelopathy mouse models of epilepsy. Epilepsia, 2018, 59(2), 358-368.
[http://dx.doi.org/10.1111/epi.13978] [PMID: 29265344]
Nolte, I.M.; Munoz, M.L.; Tragante, V.; Amare, A.T.; Jansen, R.; Vaez, A.; von der Heyde, B.; Avery, C.L.; Bis, J.C.; Dierckx, B.; van Dongen, J.; Gogarten, S.M.; Goyette, P.; Hernesniemi, J.; Huikari, V.; Hwang, S.J.; Jaju, D.; Kerr, K.F.; Kluttig, A.; Krijthe, B.P.; Kumar, J.; van der Laan, S.W.; Lyytikäinen, L.P.; Maihofer, A.X.; Minassian, A.; van der Most, P.J.; Müller-Nurasyid, M.; Nivard, M.; Salvi, E.; Stewart, J.D.; Thayer, J.F.; Verweij, N.; Wong, A.; Zabaneh, D.; Zafarmand, M.H.; Abdellaoui, A.; Albarwani, S.; Albert, C.; Alonso, A.; Ashar, F.; Auvinen, J.; Axelsson, T.; Baker, D.G.; de Bakker, P.I.W.; Barcella, M.; Bayoumi, R.; Bieringa, R.J.; Boomsma, D.; Boucher, G.; Britton, A.R.; Christophersen, I.; Dietrich, A.; Ehret, G.B.; Ellinor, P.T.; Eskola, M.; Felix, J.F.; Floras, J.S.; Franco, O.H.; Friberg, P.; Gademan, M.G.J.; Geyer, M.A.; Giedraitis, V.; Hartman, C.A.; Hemerich, D.; Hofman, A.; Hottenga, J.J.; Huikuri, H.; Hutri-Kähönen, N.; Jouven, X.; Junttila, J.; Juonala, M.; Kiviniemi, A.M.; Kors, J.A.; Kumari, M.; Kuznetsova, T.; Laurie, C.C.; Lefrandt, J.D.; Li, Y.; Li, Y.; Liao, D.; Limacher, M.C.; Lin, H.J.; Lindgren, C.M.; Lubitz, S.A.; Mahajan, A.; McKnight, B.; Zu Schwabedissen, H.M.; Milaneschi, Y.; Mononen, N.; Morris, A.P.; Nalls, M.A.; Navis, G.; Neijts, M.; Nikus, K.; North, K.E.; O’Connor, D.T.; Ormel, J.; Perz, S.; Peters, A.; Psaty, B.M.; Raitakari, O.T.; Risbrough, V.B.; Sinner, M.F.; Siscovick, D.; Smit, J.H.; Smith, N.L.; Soliman, E.Z.; Sotoodehnia, N.; Staessen, J.A.; Stein, P.K.; Stilp, A.M.; Stolarz-Skrzypek, K.; Strauch, K.; Sundström, J.; Swenne, C.A.; Syvänen, A.C.; Tardif, J.C.; Taylor, K.D.; Teumer, A.; Thornton, T.A.; Tinker, L.E.; Uitterlinden, A.G.; van Setten, J.; Voss, A.; Waldenberger, M.; Wilhelmsen, K.C.; Willemsen, G.; Wong, Q.; Zhang, Z.M.; Zonderman, A.B.; Cusi, D.; Evans, M.K.; Greiser, H.K.; van der Harst, P.; Hassan, M.; Ingelsson, E.; Järvelin, M.R.; Kääb, S.; Kähönen, M.; Kivimaki, M.; Kooperberg, C.; Kuh, D.; Lehtimäki, T.; Lind, L.; Nievergelt, C.M.; O’Donnell, C.J.; Oldehinkel, A.J.; Penninx, B.; Reiner, A.P.; Riese, H.; van Roon, A.M.; Rioux, J.D.; Rotter, J.I.; Sofer, T.; Stricker, B.H.; Tiemeier, H.; Vrijkotte, T.G.M.; Asselbergs, F.W.; Brundel, B.J.J.M.; Heckbert, S.R.; Whitsel, E.A.; den Hoed, M.; Snieder, H.; de Geus, E.J.C. Genetic loci associated with heart rate variability and their effects on cardiac disease risk. Nat. Commun., 2017, 8, 15805.
[http://dx.doi.org/10.1038/ncomms15805] [PMID: 28613276]
Riese, H.; Muñoz, L.M.; Hartman, C.A.; Ding, X.; Su, S.; Oldehinkel, A.J.; van Roon, A.M.; van der Most, P.J.; Lefrandt, J.; Gansevoort, R.T.; van der Harst, P.; Verweij, N.; Licht, C.M.; Boomsma, D.I.; Hottenga, J.J.; Willemsen, G.; Penninx, B.W.; Nolte, I.M.; de Geus, E.J.; Wang, X.; Snieder, H. Identifying genetic variants for heart rate variability in the acetylcholine pathway. PLoS One, 2014, 9(11)e112476
[http://dx.doi.org/10.1371/journal.pone.0112476] [PMID: 25384021]
Mitro, P.; Mudráková, K.; Micková, H.; Dudás, J.; Kirsch, P.; Valocik, G. Hemodynamic parameters and heart rate variability during a tilt test in relation to gene polymorphism of renin-angiotensin and serotonin system. Pacing Clin. Electrophysiol., 2008, 31(12), 1571-1580.
[http://dx.doi.org/10.1111/j.1540-8159.2008.01228.x] [PMID: 19067809]

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Page: [1640 - 1650]
Pages: 11
DOI: 10.2174/1568026620666200603105002
Price: $65

Article Metrics

PDF: 32