{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T05:58:39Z","timestamp":1726034319522},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1016\/j.bspc.2019.101842","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T12:34:00Z","timestamp":1579610040000},"page":"101842","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"special_numbering":"C","title":["Blood pressure prediction from speech recordings"],"prefix":"10.1016","volume":"58","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-9522-0609","authenticated-orcid":false,"given":"Haydar","family":"Ank\u0131\u015fhan","sequence":"first","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2019.101842_bib0005","series-title":"A Global Brief on Hypertension: Silent Killer, Global Public Health Crisis: World Health Day 2013","year":"2013"},{"issue":"10","key":"10.1016\/j.bspc.2019.101842_bib0010","first-page":"1","article-title":"Shock: a common pathway for life-threatening pediatric illnesses and injuries","volume":"2","author":"Silverman","year":"2005","journal-title":"Pediatr. Emerg. Med. Pract."},{"issue":"9859","key":"10.1016\/j.bspc.2019.101842_bib0015","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1016\/S0140-6736(12)61766-8","article-title":"\u201cA comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions\u201d, 1990\u20132010: a systematic analysis for the global burden of disease study 2010","volume":"380","author":"Lim","year":"2013","journal-title":"Lancet"},{"key":"10.1016\/j.bspc.2019.101842_bib0020","series-title":"World Health Organization","author":"World Health statistics","year":"2015"},{"issue":"4","key":"10.1016\/j.bspc.2019.101842_bib0025","article-title":"Guideline for the diagnosis and management of hypertension in adults mortality","volume":"3","author":"Gabb","year":"2016","journal-title":"Med. J. Aust."},{"issue":"5","key":"10.1016\/j.bspc.2019.101842_bib0030","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/BF02345755","article-title":"Continuous estimation of systolic blood pressure using the pulse arrival time and intermittent calibration","volume":"38","author":"Chen","year":"2000","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.bspc.2019.101842_bib0035","first-page":"398","article-title":"Photoplethysmogram intensity ratio: a potential indicator for improving the accuracy of Ptt-based cuffless blood pressure estimation","author":"Ding","year":"2014","journal-title":"Conference Proceedings, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Engineering in Medicine and Biology Society, Annual Conference"},{"issue":"6","key":"10.1016\/j.bspc.2019.101842_bib0040","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/BF02367308","article-title":"Characterization of the oscillometric method for measuring indirect blood pressure","volume":"10","author":"Geddes","year":"1982","journal-title":"Ann. Biomed. Eng."},{"issue":"2","key":"10.1016\/j.bspc.2019.101842_bib0045","first-page":"57","article-title":"Introduction of the auscultatory method of measuring blood pressure including a translation of korotko_\u2019s original paper","volume":"5","author":"Geddes","year":"1966","journal-title":"Cardiovasc. Res. Cent. Bull."},{"issue":"1","key":"10.1016\/j.bspc.2019.101842_bib0050","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1111\/j.1469-8986.1981.tb01545.x","article-title":"Pulse transit time as an indicator of arterial blood pressure","volume":"18","author":"Geddes","year":"1981","journal-title":"Psychophysiology"},{"key":"10.1016\/j.bspc.2019.101842_bib0055","first-page":"877","article-title":"Cuffless and noninvasive measurements of arterial blood pressure by pulse transit time","author":"Poon","year":"2006","journal-title":"Engineering in Medicine and Biology Society, 2005, IEEE-EMBS 2005, 27th Annual International Conference of the. IEEE"},{"key":"10.1016\/j.bspc.2019.101842_bib0060","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1109\/ICACCI.2014.6968642","article-title":"Cuffless bp measurement using a correlation study of pulse transient time and heart rate","author":"Kumar","year":"2014","journal-title":"Advances in Computing, Communications and Informatics (ICACCI 2014) International Conference on. IEEE"},{"issue":"1","key":"10.1016\/j.bspc.2019.101842_bib0065","doi-asserted-by":"crossref","first-page":"14","DOI":"10.2174\/157340312801215782","article-title":"On the analysis of fingertip photoplethysmogram signals","volume":"8","author":"Elgendi","year":"2012","journal-title":"Curr. Cardiol. Rev."},{"issue":"4","key":"10.1016\/j.bspc.2019.101842_bib0070","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/TBME.2016.2580904","article-title":"Cuffless blood pressure estimation algorithms for continuous health-care monitoring","volume":"64","author":"Kachuee","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2019.101842_bib0075","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.compind.2017.04.003","article-title":"Continuous blood pressure estimation based on multiple parameters from electrocardiogram and photoplethysmogram by back-propagation neural network","volume":"89","author":"Xu","year":"2017","journal-title":"Comput. Ind."},{"issue":"2","key":"10.1016\/j.bspc.2019.101842_bib0080","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1161\/01.HYP.32.2.365","article-title":"Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform","volume":"32","author":"Takazawa","year":"1998","journal-title":"Hypertension"},{"issue":"12","key":"10.1016\/j.bspc.2019.101842_bib0085","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1038\/jhh.2013.41","article-title":"An examination of calibration intervals required for accurately tracking blood pressure using pulse transit time algorithms","volume":"27","author":"McCarthy","year":"2013","journal-title":"J. Hum. Hypertens."},{"key":"10.1016\/j.bspc.2019.101842_bib0090","series-title":"McDonald\u2019s Blood Flow in Arteries: Theoretical, Experimental and Clinical Principles","author":"Nichols","year":"2011"},{"issue":"4","key":"10.1016\/j.bspc.2019.101842_bib0095","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1177\/016173467900100406","article-title":"Measurements of Young\u2019s modulus of elasticity of the canine aorta with ultrasound","volume":"1","author":"Hughes","year":"1979","journal-title":"Ultrason. Imaging"},{"issue":"6","key":"10.1016\/j.bspc.2019.101842_bib0100","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1161\/01.RES.5.6.594","article-title":"A method using induced waves to study pressure propagation in human arteries","volume":"5","author":"Lansdowne","year":"1957","journal-title":"Circ. Res."},{"issue":"2","key":"10.1016\/j.bspc.2019.101842_bib0105","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/72.363467","article-title":"Artificial neural networks for feature extraction and multivariate data projection","volume":"6","author":"Mao","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"issue":"6","key":"10.1016\/j.bspc.2019.101842_bib0110","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1080\/03091900701781317","article-title":"Assessment of heart rate variability derived from fingertip photoplethysmography as compared to electrocardiography","volume":"32","author":"Selvaraj","year":"2008","journal-title":"J. Med. Eng. Technol."},{"key":"10.1016\/j.bspc.2019.101842_bib0115","series-title":"Heart Physiology: From Cell to Circulation","author":"Opie","year":"2004"},{"issue":"8","key":"10.1016\/j.bspc.2019.101842_bib0120","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1364\/BOE.7.003007","article-title":"Optical blood pressure estimation with photoplethysmography and fft-based neural networks","volume":"7","author":"Xing","year":"2016","journal-title":"Biomed. Opt. Express"},{"issue":"2","key":"10.1016\/j.bspc.2019.101842_bib0125","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s13246-014-0269-6","article-title":"Characters available in photoplethysmogram for blood pressure estimation: beyond the pulse transit time","volume":"37","author":"Li","year":"2014","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"10.1016\/j.bspc.2019.101842_bib0130","series-title":"Predicting Blood Pressure With Deep Bidirectional Lstm Network","author":"Su","year":"2017"},{"issue":"11","key":"10.1016\/j.bspc.2019.101842_bib0135","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1161\/CIRCRESAHA.114.302524","article-title":"The autonomic nervous system and hypertension","volume":"114","author":"Mancia","year":"2014","journal-title":"Circ. Res."},{"key":"10.1016\/j.bspc.2019.101842_bib0140","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s10916-017-0886-1","article-title":"From pacemaker to wearable: techniques for ECG detection systems","volume":"42","author":"Ashish","year":"2018","journal-title":"J. Med. Syst."},{"issue":"March","key":"10.1016\/j.bspc.2019.101842_bib0145","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.bspc.2019.01.015","article-title":"Estimation of heartbeat rate from speech recording with hybrid feature vector (HFV)","volume":"49","author":"Ank\u0131\u015fhan","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"10.1016\/j.bspc.2019.101842_bib0150","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s11390-012-1300-6","article-title":"Heart rate extraction from vowel speech signals","volume":"27","author":"Mesleh","year":"2012","journal-title":"J. Comput. Sci. Technol."},{"issue":"14","key":"10.1016\/j.bspc.2019.101842_bib0155","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1016\/S0002-9149(99)80309-9","article-title":"The effects of emotions on short-term power spectrum analysis of heart rate variability","volume":"15","author":"McCraty","year":"1995","journal-title":"Am. J. Cardiol."},{"issue":"3","key":"10.1016\/j.bspc.2019.101842_bib0160","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/BF02344719","article-title":"Emotion recognition system using short-term monitoring of physiological signals","volume":"42","author":"Kim","year":"2004","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.bspc.2019.101842_bib0165","article-title":"Speech signal analysis for the estimation of heart rates under different emotional states","author":"Ryskaliyev","year":"2016","journal-title":"Proceedings of International Conference on Advances in Computing, Communications and Informations"},{"key":"10.1016\/j.bspc.2019.101842_bib0170","article-title":"Heartbeat feature extraction from vowel speech signal using 2D spectrum representation","author":"Skopin","year":"2009","journal-title":"Proceedings of IEEE International Conference of Information Technology"},{"key":"10.1016\/j.bspc.2019.101842_bib0175","series-title":"Analysis and Prediction of Heart Rate Using Speech Features From Natural Speech","author":"Smith","year":"2017"},{"issue":"3","key":"10.1016\/j.bspc.2019.101842_bib0180","doi-asserted-by":"crossref","DOI":"10.1109\/21.256541","article-title":"ANFIS: adaptive network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.bspc.2019.101842_bib0185","series-title":"Statistical Learning Theory","author":"Vapnik","year":"1998"},{"key":"10.1016\/j.bspc.2019.101842_bib0190","first-page":"781","article-title":"Multi-class support vector machines: a new approach","author":"Arenas-Garc\u00eda","year":"2003","journal-title":"Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing"},{"key":"10.1016\/j.bspc.2019.101842_bib0195","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2019.101842_bib0200","doi-asserted-by":"crossref","DOI":"10.1145\/1273496.1273600","article-title":"Supervised feature selection via dependence estimation","author":"Song","year":"2007","journal-title":"International Conference on Machine Learning"},{"key":"10.1016\/j.bspc.2019.101842_bib0205","first-page":"845","article-title":"Feature selection for unsupervised learning","volume":"5","author":"Dy","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.bspc.2019.101842_bib0210","series-title":"Data Classification: Algorithms and Applications","first-page":"37","article-title":"Feature selection for classification: a review","author":"Tang","year":"2014"},{"key":"10.1016\/j.bspc.2019.101842_bib0215","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical and empirical analysis of Relief and ReliefF","volume":"53","author":"Sikonja","year":"2003","journal-title":"Mach. Learn."},{"key":"10.1016\/j.bspc.2019.101842_bib0220","series-title":"Multivariate Analysis","author":"Mardia","year":"1979"},{"issue":"4","key":"10.1016\/j.bspc.2019.101842_bib0225","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"issue":"5","key":"10.1016\/j.bspc.2019.101842_bib0230","first-page":"453","article-title":"Blood pressure measurement anno 2016","volume":"30","author":"Jan Staessen","year":"2017","journal-title":"Am. J. Hypertens."},{"key":"10.1016\/j.bspc.2019.101842_bib0235","first-page":"1103","article-title":"Forest species recognition using deep convolutional neural networks","author":"Hafemann","year":"2014","journal-title":"International Conference on Pattern Recognition"},{"key":"10.1016\/j.bspc.2019.101842_bib0240","series-title":"Fundamentals of Speech Recognition","author":"Rabiner","year":"1993"},{"issue":"February","key":"10.1016\/j.bspc.2019.101842_bib0245","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.bspc.2018.08.037","article-title":"Classification of acoustic signals with new feature: fibonacci space (FS)","volume":"48","author":"Ank\u0131\u015fhan","year":"2019","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809419304239?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809419304239?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T15:31:10Z","timestamp":1612884670000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809419304239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4]]},"references-count":49,"alternative-id":["S1746809419304239"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2019.101842","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2020,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Blood pressure prediction from speech recordings","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2019.101842","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"101842"}}