{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T04:12:46Z","timestamp":1740111166823,"version":"3.37.3"},"reference-count":232,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1016\/j.compbiomed.2024.108207","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T01:41:41Z","timestamp":1709084501000},"page":"108207","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013\u20132023)"],"prefix":"10.1016","volume":"172","author":[{"given":"Anjan","family":"Gudigar","sequence":"first","affiliation":[]},{"given":"Nahrizul Adib","family":"Kadri","sequence":"additional","affiliation":[]},{"given":"U.","family":"Raghavendra","sequence":"additional","affiliation":[]},{"given":"Jyothi","family":"Samanth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4550-6836","authenticated-orcid":false,"given":"M.","family":"Maithri","sequence":"additional","affiliation":[]},{"given":"Mahesh Anil","family":"Inamdar","sequence":"additional","affiliation":[]},{"given":"Mukund A.","family":"Prabhu","sequence":"additional","affiliation":[]},{"given":"Ajay","family":"Hegde","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7225-7401","authenticated-orcid":false,"given":"Massimo","family":"Salvi","sequence":"additional","affiliation":[]},{"given":"Chai Hong","family":"Yeong","sequence":"additional","affiliation":[]},{"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1150-2244","authenticated-orcid":false,"given":"Filippo","family":"Molinari","sequence":"additional","affiliation":[]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compbiomed.2024.108207_bib1","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/S0140-6736(21)01330-1","article-title":"Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants","volume":"398","year":"2021","journal-title":"Lancet"},{"key":"10.1016\/j.compbiomed.2024.108207_bib2","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1097\/00004872-200306000-00001","article-title":"2003 European Society of Hypertension-European Society of Cardiology guidelines for the management of arterial hypertension","volume":"21","year":"2003","journal-title":"J. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib3","series-title":"Tech. Rep.","article-title":"A global brief on hypertension: Silent killer, global public health crisis: world health day 2013","author":"Organization","year":"2013"},{"key":"10.1016\/j.compbiomed.2024.108207_bib4","article-title":"Hypertension","volume":"4","author":"Oparil","year":"2018","journal-title":"Nat. Rev. Dis. Prim."},{"key":"10.1016\/j.compbiomed.2024.108207_bib5","series-title":"Encyclopedia of Molecular Pharmacology","first-page":"317","article-title":"Blood pressure control","author":"Kreutz","year":"2021"},{"key":"10.1016\/j.compbiomed.2024.108207_bib6","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."},{"issue":"12","key":"10.1016\/j.compbiomed.2024.108207_bib7","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1097\/HJH.0000000000003480","volume":"41","author":"Mancia","year":"2023","journal-title":"J. Hypertens."},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib8","first-page":"41","article-title":"Hypertension. Silent killer","volume":"96","author":"Rapport","year":"1999","journal-title":"N. J. Med."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib9","doi-asserted-by":"crossref","first-page":"e22","DOI":"10.2196\/jmir.9268","article-title":"Prediction of incident hypertension within the next year: prospective study using statewide electronic health records and machine learning","volume":"20","author":"Ye","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"10.1016\/j.compbiomed.2024.108207_bib10","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1093\/eurheartj\/eht565","article-title":"Central blood pressure: current evidence and clinical importance","volume":"35","author":"McEniery","year":"2014","journal-title":"Eur. Heart J."},{"key":"10.1016\/j.compbiomed.2024.108207_bib11","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1161\/HYPERTENSIONAHA.109.148999","article-title":"Amlodipine-valsartan combination decreases central systolic blood pressure more effectively than the amlodipine-atenolol combination: the EXPLOR study","volume":"55","author":"Boutouyrie","year":"2010","journal-title":"Hypertension"},{"key":"10.1016\/j.compbiomed.2024.108207_bib12","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1097\/HJH.0000000000002438","article-title":"Genetics, diagnosis, management and future directions of research of phaeochromocytoma and paraganglioma: a position statement and consensus of the Working Group on Endocrine Hypertension of the European Society of Hypertension","volume":"38","author":"Lenders","year":"2020","journal-title":"J. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib13","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1097\/HJH.0000000000002510","article-title":"Genetics, prevalence, screening and confirmation of primary aldosteronism: a position statement and consensus of the Working Group on Endocrine Hypertension of the European Society of Hypertension","volume":"38","author":"Mulatero","year":"2020","journal-title":"J. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib14","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1097\/HJH.0000000000003252","article-title":"Diagnosis and management of hypertension in patients with Cushing's syndrome: a position statement and consensus of the Working Group on Endocrine Hypertension of the European Society of Hypertension","volume":"40","author":"Fallo","year":"2022","journal-title":"J. Hypertens."},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib15","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0266334","article-title":"Prediction of hypertension using traditional regression and machine learning models: a systematic review and meta-analysis","volume":"17","author":"Chowdhury","year":"2022","journal-title":"PLoS One"},{"key":"10.1016\/j.compbiomed.2024.108207_bib16","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11906-022-01212-6","article-title":"Machine learning for hypertension prediction: a systematic review","volume":"24","author":"Silva","year":"2022","journal-title":"Curr. Hypertens. Rep."},{"issue":"11","key":"10.1016\/j.compbiomed.2024.108207_bib17","doi-asserted-by":"crossref","first-page":"5838","DOI":"10.3390\/ijerph18115838","article-title":"Automated detection of hypertension using physiological signals: a review","volume":"18","author":"Sharma","year":"2021","journal-title":"Int. J. Environ. Res. Publ. Health"},{"key":"10.1016\/j.compbiomed.2024.108207_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102813","article-title":"A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data","volume":"68","author":"Martinez-R\u00edos","year":"2021","journal-title":"Biomed. Signal Process Control"},{"issue":"9","key":"10.1016\/j.compbiomed.2024.108207_bib19","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.122.027896","article-title":"Survey and evaluation of hypertension machine learning research","volume":"12","author":"du Toit","year":"2023","journal-title":"J. Am. Heart Assoc."},{"issue":"19","key":"10.1016\/j.compbiomed.2024.108207_bib20","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1001\/jama.289.19.2560","article-title":"The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report","volume":"289","author":"Chobanian","year":"2003","journal-title":"JAMA"},{"issue":"9","key":"10.1016\/j.compbiomed.2024.108207_bib21","doi-asserted-by":"crossref","first-page":"663","DOI":"10.3949\/ccjm.75.9.663","article-title":"What is the proper workup of a patient with hypertension?","volume":"75","author":"Katakam","year":"2008","journal-title":"Cleve. Clin. J. Med."},{"year":"2010","series-title":"Hypertension Diagnosis and Treatment","key":"10.1016\/j.compbiomed.2024.108207_bib22"},{"unstructured":"Stiles S. Framingham Criteria Predict New Hypertension Better Than Prehypertension in Young Adults. Medscape Medical News. Available at: http:\/\/www.medscape.com\/viewarticle\/811416. Accessed: September 30, 2013.","key":"10.1016\/j.compbiomed.2024.108207_bib23"},{"year":"2013","author":"Carson","series-title":"Evaluating the Framingham Hypertension Risk Prediction Model in Young Adults: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Hypertension","key":"10.1016\/j.compbiomed.2024.108207_bib24"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib25","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1161\/HYPERTENSIONAHA.117.10368","article-title":"Pulse wave velocity predicts the progression of blood pressure and development of hypertension in young adults","volume":"71","author":"Koivistoinen","year":"2018","journal-title":"Hypertension"},{"key":"10.1016\/j.compbiomed.2024.108207_bib26","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1097\/HJH.0000000000001940","volume":"36","author":"Williams","year":"2018","journal-title":"J. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib27","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1097\/HJH.0000000000000584","article-title":"Effective risk stratification in patients with moderate cardiovascular risk using albuminuria and atherosclerotic plaques in the carotid arteries","volume":"33","author":"Greve","year":"2015","journal-title":"J. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib28","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1097\/HJH.0b013e328330b845","article-title":"Mean Absolute Error increases cardiovascular risk independently of in-office and out-of-office blood pressure values","volume":"27","author":"Bombelli","year":"2009","journal-title":"J. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib29","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1056\/NEJM199005313222203","article-title":"Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study","volume":"322","author":"Levy","year":"1990","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib30","doi-asserted-by":"crossref","first-page":"345","DOI":"10.7326\/0003-4819-114-5-345","article-title":"Relation of left ventricular mass and geometry to morbidity and mortality in uncomplicated essential hypertension","volume":"114","author":"Koren","year":"1991","journal-title":"Ann. Intern. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib31","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.kint.2021.04.032","article-title":"Basic principles and new advances in kidney imaging","volume":"100","author":"Caroli","year":"2021","journal-title":"Kidney Int."},{"key":"10.1016\/j.compbiomed.2024.108207_bib32","first-page":"11","article-title":"Gray-scale, color Doppler, spectral Doppler, and contrast-enhanced renal artery ultrasound: imaging techniques and features","author":"Park","year":"2022","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib70","first-page":"BBI","article-title":"Machine learning data imputation and classification in a multicohort hypertension clinical study","volume":"9","author":"Seffens","year":"2015","journal-title":"Bioinf. Biol. Insights"},{"key":"10.1016\/j.compbiomed.2024.108207_bib71","series-title":"2018 Second International Conference on Advances in Electronics, Computers and Communications","first-page":"1","article-title":"Predicting the occurrence of essential hypertension using annual health records","author":"Patnaik","year":"2018"},{"key":"10.1016\/j.compbiomed.2024.108207_bib72","first-page":"1","article-title":"Automatic classification of hypertension types based on personal features by machine learning algorithms","volume":"2020","author":"Nour","year":"2020","journal-title":"Math. Probl Eng."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib75","doi-asserted-by":"crossref","DOI":"10.2196\/19739","article-title":"An application of machine learning to etiological diagnosis of secondary hypertension: retrospective study using electronic medical records","volume":"9","author":"Diao","year":"2021","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.compbiomed.2024.108207_bib76","doi-asserted-by":"crossref","first-page":"400","DOI":"10.3390\/s19020400","article-title":"A mobile crowd sensing application for hypertensive patients","volume":"19","author":"Jovanovic","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.compbiomed.2024.108207_bib73","article-title":"A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations","volume":"25","author":"Kandil","year":"2020","journal-title":"Neuroimage: Clinical"},{"year":"2022","author":"Raghavendra","series-title":"Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images","key":"10.1016\/j.compbiomed.2024.108207_bib74"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib33","first-page":"264","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement Ann","volume":"151","author":"Moher","year":"2009","journal-title":"Intern. Med."},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib34","article-title":"Risk factors associated with hypertension in young adults: a systematic review","volume":"15","author":"Meher","year":"2023","journal-title":"Cureus"},{"key":"10.1016\/j.compbiomed.2024.108207_bib35","doi-asserted-by":"crossref","DOI":"10.1155\/2017\/5491838","article-title":"Prevalence and associated risk factors of hypertension: a cross-sectional study in urban varanasi","volume":"2017","author":"Singh","year":"2017","journal-title":"Int. J. Hypertens."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib36","first-page":"43","article-title":"Risk factors for hypertension in young adults of Bangladesh","volume":"29","author":"Paul","year":"2020","journal-title":"Mymensingh Med. J."},{"issue":"Issue 5","key":"10.1016\/j.compbiomed.2024.108207_bib37","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.amjmed.2008.10.034","article-title":"Prediction of incident hypertension risk in women with currently normal blood pressure","volume":"122","author":"Paynter","year":"2009","journal-title":"Am. J. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib38","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.cmpb.2015.12.008","article-title":"ECG-based heartbeat classification for arrhythmia detection: a survey","volume":"127","author":"da","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"issue":"17","key":"10.1016\/j.compbiomed.2024.108207_bib39","doi-asserted-by":"crossref","first-page":"9","DOI":"10.18180\/tecciencia.2014.17.1","article-title":"Relationship of blood pressure with the electrical signal of the heart using signal processing","volume":"9","author":"Estrada","year":"2014","journal-title":"Tecciencia"},{"key":"10.1016\/j.compbiomed.2024.108207_bib41","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0002-8703(49)90562-1","article-title":"The ventricular complex in left ventricular hypertrophy as obtained by unipolar precordial and limbs leads","volume":"37","author":"Sokolow","year":"1949","journal-title":"Am. Heart J."},{"year":"2017","author":"Liang","series-title":"PPG-BP Database. Figshare. Dataset","key":"10.1016\/j.compbiomed.2024.108207_bib40"},{"key":"10.1016\/j.compbiomed.2024.108207_bib42","article-title":"The use of photoplethysmography for assessing hypertension","volume":"2","author":"Elgendi","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib43","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3390\/jcm8010012","article-title":"Hypertension assessment using photoplethysmography: a risk stratification approach","volume":"8","author":"Liang","year":"2018","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106677","article-title":"Application of photoplethysmography signals for healthcare systems: an in-depth review","volume":"216","author":"Loh","year":"2022","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.compbiomed.2024.108207_bib45","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1161\/01.CIR.93.5.1043","article-title":"Heart rate variability: standards of measurement, physiological interpretation, and clinical use","volume":"93","year":"1996","journal-title":"Circulation"},{"key":"10.1016\/j.compbiomed.2024.108207_bib46","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/S0895-7061(96)00249-X","article-title":"Association of cardiac autonomic function and the development of hypertension: the ARIC Study","volume":"9","author":"Liao","year":"1996","journal-title":"Am. J. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib47","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1161\/01.HYP.32.2.293","article-title":"Reduced heart rate variability and new-onset hypertension: insights into pathogenesis of hypertension: the Framingham Heart Study","volume":"32","author":"Singh","year":"1998","journal-title":"Hypertension"},{"key":"10.1016\/j.compbiomed.2024.108207_bib48","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105407","article-title":"Rajendra Acharya, Heart rate variability for medical decision support systems: a review","volume":"145","author":"Faust","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib49","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.3390\/s19071489","article-title":"Unobtrusive mattress-based identification of hypertension by integrating classification and association Rule mining","volume":"19","author":"Liu","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.compbiomed.2024.108207_bib50","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/0002-8703(56)90021-7","article-title":"Physical basis of ballistocardiography. IV. The relative movement of subject and ballistocardiograph","volume":"52","author":"Burger","year":"1956","journal-title":"Am. Heart J."},{"key":"10.1016\/j.compbiomed.2024.108207_bib51","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/0002-9149(58)90271-6","article-title":"The relation of the ballistocardiogram to cardiac function","volume":"2","author":"Starr","year":"1959","journal-title":"Am. J. Cardiol."},{"key":"10.1016\/j.compbiomed.2024.108207_bib52","series-title":"Experimental Investigations on Ultra-low Frequency Displacement Ballistocardiography","first-page":"1","author":"Knoop","year":"1965"},{"key":"10.1016\/j.compbiomed.2024.108207_bib53","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1161\/hy0302.105777","article-title":"Blood pressure response to heart rate during exercise test and risk of future hypertension","volume":"39","author":"Miyai","year":"2002","journal-title":"Hypertension"},{"key":"10.1016\/j.compbiomed.2024.108207_bib54","doi-asserted-by":"crossref","first-page":"2836","DOI":"10.1161\/CIRCULATIONAHA.111.063933","article-title":"Relations of exercise blood pressure response to cardiovascular risk factors and vascular function in the framingham heart study","volume":"125","author":"Thanassoulis","year":"2012","journal-title":"Circulation"},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108207_bib77","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1097\/CCM.0b013e31820a92c6","article-title":"Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public-access ICU database","volume":"39","author":"Saeed","year":"2011","journal-title":"Crit. Care Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib78","series-title":"National Health and Nutrition Examination Survey","first-page":"1999","author":"Johnson","year":"2013"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib79","first-page":"799","article-title":"Data resource profile: the national health information database of the National Health Insurance Service in South Korea","volume":"46","author":"Cheol Seong","year":"2017","journal-title":"Int. J. Epidemiol."},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib80","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1093\/ije\/dyw248","article-title":"Data resource profile: national electronic medical record data from the canadian primary care sentinel surveillance network (cpcssn)","volume":"46","author":"Garies","year":"2017","journal-title":"Int. J. Epidemiol."},{"key":"10.1016\/j.compbiomed.2024.108207_bib81","series-title":"2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China","first-page":"2122","article-title":"The prediction of hypertension based on convolution neural network","author":"Luo","year":"2018"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-022-01900-5","article-title":"Machine learning in hypertension detection: a study on World Hypertension Day data","volume":"47","author":"Montagna","year":"2022","journal-title":"J. Med. Syst."},{"issue":"8","key":"10.1016\/j.compbiomed.2024.108207_bib83","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0289613","article-title":"Predicting the risk of hypertension using machine learning algorithms: a cross sectional study in Ethiopia","volume":"18","author":"Islam","year":"2023","journal-title":"PLoS One"},{"key":"10.1016\/j.compbiomed.2024.108207_bib84","series-title":"2016 IEEE Region 10 Conference (TENCON)","first-page":"2943","article-title":"Machine learning-based clinical decision support system for early diagnosis from real-time physiological data","author":"Baig","year":"2016"},{"key":"10.1016\/j.compbiomed.2024.108207_bib85","doi-asserted-by":"crossref","DOI":"10.1155\/2014\/637635","article-title":"Predicting increased blood pressure using machine learning","volume":"2014","author":"Golino","year":"2014","journal-title":"J. Obes."},{"key":"10.1016\/j.compbiomed.2024.108207_bib86","series-title":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","first-page":"1","article-title":"Using machine learning to predict hypertension from a clinical dataset","author":"LaFreniere","year":"2016"},{"key":"10.1016\/j.compbiomed.2024.108207_bib87","series-title":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","first-page":"1322","article-title":"ADASYN: adaptive synthetic sampling approach for imbalanced learning","author":"He","year":"2008"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib88","article-title":"Predicting hypertension using machine learning: a case study at petra university","volume":"14","author":"Sakka","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"issue":"20","key":"10.1016\/j.compbiomed.2024.108207_bib89","doi-asserted-by":"crossref","first-page":"14487","DOI":"10.1007\/s00521-021-06060-0","article-title":"A hybrid machine learning approach for hypertension risk prediction","volume":"35","author":"Fang","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.compbiomed.2024.108207_bib90","first-page":"131","article-title":"A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records","volume":"19","author":"Ren","year":"2019","journal-title":"BMC Med. Inf. Decis. Making"},{"key":"10.1016\/j.compbiomed.2024.108207_bib91","first-page":"5491","article-title":"Problems with shapley-value-based explanations as feature importance measures","volume":"119","author":"Kumar","year":"2020","journal-title":"Proceedings of the 37th International Conference on Machine Learning"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib92","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1093\/ehjdh\/ztac066","article-title":"Prediction of white-coat hypertension and white-coat uncontrolled hypertension using machine learning algorithm","volume":"3","author":"Shih","year":"2022","journal-title":"European Heart Journal-Digital Health"},{"unstructured":"https:\/\/archive.physionet.org\/mimic2\/(accessed on 15\/November\/2023).","key":"10.1016\/j.compbiomed.2024.108207_bib97"},{"unstructured":"https:\/\/physionet.org\/content\/mimiciii\/1.4\/(accessed on 15\/November\/2023).","key":"10.1016\/j.compbiomed.2024.108207_bib98"},{"unstructured":"https:\/\/physionet.org\/content\/mimiciv\/2.2\/(accessed on 15\/November\/2023).","key":"10.1016\/j.compbiomed.2024.108207_bib99"},{"unstructured":"https:\/\/eicu-crd.mit.edu\/(accessed on 15\/November\/2023).","key":"10.1016\/j.compbiomed.2024.108207_bib100"},{"unstructured":"https:\/\/physionet.org\/content\/mimic3wdb\/1.0\/(accessed on 15\/November\/2023).","key":"10.1016\/j.compbiomed.2024.108207_bib101"},{"unstructured":"https:\/\/physionet.org\/content\/shareedb\/1.0.0\/(accessed on 15\/November\/2023).","key":"10.1016\/j.compbiomed.2024.108207_bib102"},{"key":"10.1016\/j.compbiomed.2024.108207_bib103","series-title":"2022 Medical Technologies Congress (TIPTEKNO)","first-page":"1","article-title":"Hypertension classification using PPG signals","author":"Tanc","year":"2022"},{"key":"10.1016\/j.compbiomed.2024.108207_bib104","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.compbiomed.2024.108207_bib105","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.bspc.2018.08.022","article-title":"Blood pressure estimation from appropriate and inappropriate PPG signals using A whole-based method","volume":"47","author":"Mousavi","year":"2019","journal-title":"Biomed. Signal Process Control"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib106","first-page":"424","article-title":"Multi-domain feature-based expert diagnostic system for detection of hypertension using photoplethysmogram signal","volume":"10","author":"Khan","year":"2022","journal-title":"International Journal of Intelligent Systems and Applications in Engineering"},{"key":"10.1016\/j.compbiomed.2024.108207_bib107","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.01.012","article-title":"Intradialytic hypotension related episodes identification based on the most effective features of photoplethysmography signal","volume":"157","author":"Nafisi","year":"2018","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.compbiomed.2024.108207_bib108","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106222","article-title":"Classification of blood pressure in critically ill patients using photoplethysmography and machine learning","volume":"208","author":"Mej\u00eda-Mej\u00eda","year":"2021","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.compbiomed.2024.108207_bib109","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105479","article-title":"A machine learning approach for hypertension detection based on photoplethysmography and clinical data","volume":"145","author":"Martinez-R\u00edos","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib110","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.micpro.2016.06.001","article-title":"Methods for reliable estimation of pulse transit time and blood pressure variations using smartphone sensors","volume":"46","author":"Junior","year":"2016","journal-title":"Microprocess. Microsyst."},{"key":"10.1016\/j.compbiomed.2024.108207_bib111","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102772","article-title":"A multi-type features fusion neural network for blood pressure prediction based on photoplethysmography","volume":"68","author":"Rong","year":"2021","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib112","article-title":"Expert diagnostic system for detection of hypertension and diabetes mellitus using discrete wavelet decomposition of photoplethysmogram signal and machine learning technique","volume":"19","author":"Singh","year":"2023","journal-title":"Medicine in Novel Technology and Devices"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib113","doi-asserted-by":"crossref","first-page":"247","DOI":"10.3390\/s21010247","article-title":"Expert hypertension detection system featuring pulse plethysmograph signals and hybrid feature selection and reduction scheme","volume":"21","author":"Khan","year":"2021","journal-title":"Sensors"},{"issue":"16","key":"10.1016\/j.compbiomed.2024.108207_bib114","doi-asserted-by":"crossref","first-page":"8380","DOI":"10.3390\/app12168380","article-title":"Hypertension detection based on photoplethysmography signal morphology and machine learning techniques","volume":"12","author":"Evdochim","year":"2022","journal-title":"Appl. Sci."},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108207_bib115","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3390\/info11020093","article-title":"Noninvasive blood pressure classification based on photoplethysmography using k-nearest neighbors algorithm: a feasibility study","volume":"11","author":"Tjahjadi","year":"2020","journal-title":"Information"},{"issue":"6","key":"10.1016\/j.compbiomed.2024.108207_bib116","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/aba537","article-title":"Hypertension assessment based on feature extraction using a photoplethysmography signal and its derivatives","volume":"42","author":"Yao","year":"2021","journal-title":"Physiol. Meas."},{"issue":"22","key":"10.1016\/j.compbiomed.2024.108207_bib117","doi-asserted-by":"crossref","first-page":"22030","DOI":"10.1109\/JSEN.2022.3211993","article-title":"Higher order derivative-based integrated model for cuff-less blood pressure estimation and stratification using PPG signals","volume":"22","author":"Gupta","year":"2022","journal-title":"IEEE Sensor. J."},{"key":"10.1016\/j.compbiomed.2024.108207_bib118","series-title":"2022 IEEE Delhi Section Conference (DELCON)","first-page":"1","article-title":"Automated detection of blood pressure using CNN","author":"Ranjan","year":"2022"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib119","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3390\/jsan9030034","article-title":"Non-invasive risk stratification of hypertension: a systematic comparison of machine learning algorithms","volume":"9","author":"Sannino","year":"2020","journal-title":"J. Sens. Actuator Netw."},{"key":"10.1016\/j.compbiomed.2024.108207_bib120","article-title":"An efficient hybrid LSTM-ANN joint classification-regression model for PPG based blood pressure monitoring","volume":"84","author":"Ali","year":"2023","journal-title":"Biomed. Signal Process Control"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib121","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.redar.2022.01.011","article-title":"Photoplethysmography waveform analysis for classification of vascular tone and arterial blood pressure: study based on neural networks","volume":"70","author":"Echeverr\u00eda","year":"2023","journal-title":"Rev. Esp. Anestesiol. Reanim."},{"issue":"6","key":"10.1016\/j.compbiomed.2024.108207_bib135","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.3390\/s20061695","article-title":"Medical-grade ECG sensor for long-term monitoring","volume":"20","author":"Rashkovska","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.compbiomed.2024.108207_bib136","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103630","article-title":"A computational intelligence tool for the detection of hypertension using empirical mode decomposition","volume":"118","author":"Soh","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib144","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103629","article-title":"ECG signal based automated hypertension detection using fourier decomposition method and cosine modulated filter banks","volume":"76","author":"Parmar","year":"2022","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib138","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103924","article-title":"Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank","volume":"123","author":"Rajput","year":"2020","journal-title":"Comput. Biol. Med."},{"issue":"21","key":"10.1016\/j.compbiomed.2024.108207_bib139","doi-asserted-by":"crossref","first-page":"4068","DOI":"10.3390\/ijerph16214068","article-title":"Hypertension diagnosis index for discrimination of high-risk hypertension ECG signals using optimal orthogonal wavelet filter bank","volume":"16","author":"Rajput","year":"2019","journal-title":"Int. J. Environ. Res. Publ. Health"},{"key":"10.1016\/j.compbiomed.2024.108207_bib140","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rmed.2016.06.001","article-title":"Non-invasive screening for pulmonary hypertension in idiopathic pulmonary fibrosis","volume":"117","author":"Alkukhun","year":"2016","journal-title":"Respir. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib141","doi-asserted-by":"crossref","DOI":"10.1016\/j.bios.2023.115693","article-title":"AI-enabled epidermal electronic system to automatically monitor a prognostic parameter for hypertension with a smartphone","volume":"241","author":"Hesar","year":"2023","journal-title":"Biosens. Bioelectron."},{"key":"10.1016\/j.compbiomed.2024.108207_bib126","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103719","article-title":"A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals","volume":"120","author":"Esmaelpoor","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib142","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2020.100479","article-title":"A two-stage deep CNN architecture for the classification of low-risk and high-risk hypertension classes using multi-lead ECG signals","volume":"21","author":"Jain","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"10.1016\/j.compbiomed.2024.108207_bib143","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2021.100090","article-title":"Machine learning to promote health management through lifestyle changes for hypertension patients","volume":"12","author":"Islam","year":"2021","journal-title":"Array"},{"key":"10.1016\/j.compbiomed.2024.108207_bib145","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpbup.2023.100097","article-title":"Interpretable hybrid model for an automated patient-wise categorization of hypertensive and normotensive electrocardiogram signals","volume":"3","author":"Chen","year":"2023","journal-title":"Computer Methods and Programs in Biomedicine Update"},{"key":"10.1016\/j.compbiomed.2024.108207_bib146","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.jelectrocard.2023.09.011","article-title":"End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks","volume":"81","author":"Habineza","year":"2023","journal-title":"J. Electrocardiol."},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108207_bib147","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.cvdhj.2023.03.001","article-title":"Artificial intelligence\u2013enabled classification of hypertrophic heart diseases using electrocardiograms","volume":"4","author":"Haimovich","year":"2023","journal-title":"Cardiovascular Digital Health Journal"},{"key":"10.1016\/j.compbiomed.2024.108207_bib148","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijcha.2023.101211","article-title":"Identification of patients with dilated phase of hypertrophic cardiomyopathy using a convolutional neural network applied to multiple, dual, and single lead electrocardiograms","volume":"46","author":"Hirota","year":"2023","journal-title":"IJC Heart & Vasculature"},{"key":"10.1016\/j.compbiomed.2024.108207_bib149","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.jelectrocard.2023.08.011","article-title":"Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings","volume":"81","author":"Rooney","year":"2023","journal-title":"J. Electrocardiol."},{"issue":"7","key":"10.1016\/j.compbiomed.2024.108207_bib151","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.cardfail.2022.12.016","article-title":"Electrocardiogram detection of pulmonary hypertension using deep learning","volume":"29","author":"Aras","year":"2023","journal-title":"J. Card. Fail."},{"key":"10.1016\/j.compbiomed.2024.108207_bib152","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101919","article-title":"Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques","volume":"108","author":"Miao","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib153","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106191","article-title":"A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms","volume":"207","author":"Baker","year":"2021","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.compbiomed.2024.108207_bib157","doi-asserted-by":"crossref","first-page":"109","DOI":"10.7439\/ijbar.v5i2.659","article-title":"A study on analysis of Heart Rate Variability in hypertensive individuals","volume":"5","author":"Natarajan","year":"2014","journal-title":"Int. J. Biomed. Adv. Res."},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib158","doi-asserted-by":"crossref","first-page":"e25","DOI":"10.1016\/j.jelectrocard.2013.05.090","article-title":"Linear-nonlinear heart rate variability analysis and SVM based classification of normal and hypertensive subjects","volume":"46","author":"Poddar","year":"2013","journal-title":"J. Electrocardiol."},{"key":"10.1016\/j.compbiomed.2024.108207_bib159","series-title":"Machine Learning in Bio-Signal Analysis and Diagnostic Imaging","first-page":"99","article-title":"Automated classification of hypertension and coronary artery disease patients by PNN, KNN, and SVM classifiers using HRV analysis","author":"Poddar","year":"2019"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib160","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.ancard.2018.04.014","article-title":"Nonlinear analyses of heart rate variability in hypertension","volume":"67","author":"Koichubekov","year":"2018","journal-title":"Ann. Cardiol. Angeiol"},{"key":"10.1016\/j.compbiomed.2024.108207_bib161","doi-asserted-by":"crossref","first-page":"5985479","DOI":"10.1155\/2017\/5985479","article-title":"Comparison of machine learning methods for the arterial hypertension diagnostics","volume":"2017","author":"Kublanov","year":"2017","journal-title":"Appl. Bionics Biomechanics"},{"key":"10.1016\/j.compbiomed.2024.108207_bib162","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1155\/2019\/4936179","article-title":"Multiscale fine-grained heart rate variability analysis for recognizing the severity of hypertension","volume":"2019","author":"Ni","year":"2019","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib165","series-title":"2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and its Associated Workshops (UIC-ATC-ScalCom)","first-page":"1230","article-title":"Extracting features for cardiovascular disease classification based on ballistocardiography","author":"Song","year":"2015"},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108207_bib166","doi-asserted-by":"crossref","first-page":"182","DOI":"10.3390\/diagnostics13020182","article-title":"Automated hypertension detection using ConvMixer and Spectrogram techniques with ballistocardiograph signals","volume":"13","author":"Ozcelik","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.compbiomed.2024.108207_bib167","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119058","article-title":"A support system for automatic classification of hypertension using BCG signals","volume":"214","author":"Gupta","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compbiomed.2024.108207_bib168","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2021.100736","article-title":"Automated detection of hypertension using wavelet transform and nonlinear techniques with ballistocardiogram signals","volume":"26","author":"Rajput","year":"2021","journal-title":"Inform. Med. Unlocked"},{"issue":"7","key":"10.1016\/j.compbiomed.2024.108207_bib169","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.3390\/ijerph19074014","article-title":"Automated detection of hypertension using continuous wavelet transform and a deep neural network with Ballistocardiography signals","volume":"19","author":"Rajput","year":"2022","journal-title":"Int. J. Environ. Res. Publ. Health"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib170","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1016\/j.bbe.2022.06.001","article-title":"Hyp-Net: automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals","volume":"42","author":"Gupta","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"issue":"6","key":"10.1016\/j.compbiomed.2024.108207_bib189","first-page":"565","article-title":"Hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) diagnosis using echocardiography and electrocardiography","volume":"9","author":"Forghani","year":"2021","journal-title":"Comput. Methods Biomech. Biomed. Eng.: Imaging & Visualization"},{"key":"10.1016\/j.compbiomed.2024.108207_bib202","series-title":"2021 Computing in Cardiology (CinC)","first-page":"1","article-title":"Hypertension risk assessment from photoplethysmographic recordings using deep learning classifiers","author":"Cano","year":"2021"},{"key":"10.1016\/j.compbiomed.2024.108207_bib203","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104247","article-title":"NABNet: a nested attention-guided BiConvLSTM network for a robust prediction of blood pressure components from reconstructed arterial blood pressure waveforms using PPG and ECG signals","volume":"79","author":"Mahmud","year":"2023","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib204","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105287","article-title":"BPNet: a multi-modal fusion neural network for blood pressure estimation using ECG and PPG","volume":"86","author":"Long","year":"2023","journal-title":"Biomed. Signal Process Control"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib134","doi-asserted-by":"crossref","first-page":"101","DOI":"10.3390\/bios8040101","article-title":"Photoplethysmography and deep learning: enhancing hypertension risk stratification","volume":"8","author":"Liang","year":"2018","journal-title":"Biosensors"},{"issue":"7","key":"10.1016\/j.compbiomed.2024.108207_bib179","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0236378","article-title":"A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: a retrospective study","volume":"15","author":"Zou","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.compbiomed.2024.108207_bib137","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103001","article-title":"Cuffless blood pressure estimation based on composite neural network and graphics information","volume":"70","author":"Qiu","year":"2021","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib206","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-0942-5","article-title":"Toward hypertension prediction based on PPG-derived HRV signals: a feasibility study","volume":"42","author":"Lan","year":"2018","journal-title":"J. Med. Syst."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib201","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1111\/jch.12920","article-title":"Comprehensive first-line magnetic resonance imaging in hypertension: experience from a single-center tertiary referral clinic","volume":"19","author":"Burchell","year":"2017","journal-title":"J. Clin. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib55","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1161\/01.STR.27.12.2262","article-title":"Presence and severity of cerebral white matter lesions and hypertension, its treatment, and its control. The ARIC Study. Atherosclerosis Risk in Communities Study","volume":"27","author":"Liao","year":"1996","journal-title":"Stroke"},{"key":"10.1016\/j.compbiomed.2024.108207_bib56","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1161\/01.STR.27.8.1274","article-title":"Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study","volume":"27","author":"Longstreth","year":"1996","journal-title":"Stroke"},{"key":"10.1016\/j.compbiomed.2024.108207_bib57","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S1474-4422(03)00305-3","article-title":"Vascular cognitive impairment","volume":"2","author":"O'Brien","year":"2003","journal-title":"Lancet Neurol."},{"key":"10.1016\/j.compbiomed.2024.108207_bib58","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1161\/01.STR.0000068408.82115.D2","article-title":"Silent brain infarcts and white matter lesions increase stroke risk in the general population: the Rotterdam scan study","volume":"34","author":"Vermeer","year":"2003","journal-title":"Stroke"},{"key":"10.1016\/j.compbiomed.2024.108207_bib59","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1056\/NEJMoa022066","article-title":"Silent brain infarcts and the risk of dementia and cognitive decline","volume":"348","author":"Vermeer","year":"2003","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib60","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1056\/NEJM199005313222203","article-title":"Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study","volume":"322","author":"Levy","year":"1990","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib61","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1067\/mhj.2002.126113","article-title":"Change of left ventricular geometric pattern after 1 year of antihypertensive treatment: the Losartan Intervention for Endpoint reduction in hypertension (LIFE) study","volume":"144","author":"Wachtell","year":"2002","journal-title":"Am. Heart J."},{"key":"10.1016\/j.compbiomed.2024.108207_bib62","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12947-021-00261-2","article-title":"Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis","volume":"19","author":"Zhou","year":"2021","journal-title":"Cardiovasc. Ultrasound"},{"key":"10.1016\/j.compbiomed.2024.108207_bib63","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.echo.2011.11.010","article-title":"EAE\/ASE recommendations for image acquisition and display using three dimensional echocardiography","volume":"25","author":"Lang","year":"2012","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"10.1016\/j.compbiomed.2024.108207_bib64","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1093\/ehjci\/jew082","article-title":"Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American society of echocardiography and the European association of cardiovascular imaging","volume":"17","author":"Nagueh","year":"2016","journal-title":"Eur Heart J Cardiovasc Imaging"},{"key":"10.1016\/j.compbiomed.2024.108207_bib65","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1161\/01.HYP.0000121223.44837.de","article-title":"Left ventricular concentric geometry during treatment adversely affects cardiovascular prognosis in hypertensive patients","volume":"43","author":"Muiesan","year":"2004","journal-title":"Hypertension"},{"key":"10.1016\/j.compbiomed.2024.108207_bib66","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1016\/j.jcmg.2015.06.007","article-title":"Association of a 4-tiered classification of LV hypertrophy with adverse CV outcomes in the general population","volume":"8","author":"Garg","year":"2015","journal-title":"JACC Cardiovasc Imaging"},{"key":"10.1016\/j.compbiomed.2024.108207_bib67","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1001\/jama.1966.03100050043015","article-title":"Prognostic importance of ophthalmoscopic findings in essential hypertension","volume":"195","author":"Breslin","year":"1966","journal-title":"JAMA"},{"key":"10.1016\/j.compbiomed.2024.108207_bib68","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1161\/CIRCULATIONAHA.111.049965","article-title":"Mild retinopathy is a risk factor for cardiovascular mortality in Japanese with and without hypertension: the Ibaraki Prefectural Health Study","volume":"124","author":"Sairenchi","year":"2011","journal-title":"Circulation"},{"key":"10.1016\/j.compbiomed.2024.108207_bib69","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.procs.2017.10.066","article-title":"The classification of hypertensive retinopathy using convolutional neural network","volume":"116","author":"Triwijoyo","year":"2017","journal-title":"Procedia Comput. Sci."},{"issue":"12","key":"10.1016\/j.compbiomed.2024.108207_bib172","doi-asserted-by":"crossref","first-page":"e140","DOI":"10.1161\/CIRCRESAHA.116.309493","article-title":"Is high blood pressure self-protection for the brain?","volume":"119","author":"Warnert","year":"2016","journal-title":"Circ. Res."},{"issue":"9","key":"10.1016\/j.compbiomed.2024.108207_bib173","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.3390\/app12094291","article-title":"Studying the role of cerebrovascular changes in different compartments in human brains in hypertension prediction","volume":"12","author":"Kandil","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compbiomed.2024.108207_bib174","series-title":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","first-page":"1","article-title":"A CAD system for the early prediction of hypertension based on changes in cerebral vasculature","author":"Kandil","year":"2019"},{"key":"10.1016\/j.compbiomed.2024.108207_bib175","series-title":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","first-page":"1","article-title":"A cad system for the early prediction of hypertension based on changes in cerebral vasculature","author":"Kandil","year":"2019"},{"key":"10.1016\/j.compbiomed.2024.108207_bib176","series-title":"2020 IEEE International Conference on Image Processing (ICIP)","first-page":"394","article-title":"Precise cerebrovascular segmentation","author":"Taher","year":"2020"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib177","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-47368-1","article-title":"A novel framework for early detection of hypertension using magnetic resonance angiography","volume":"9","author":"Kandil","year":"2019","journal-title":"Sci. Rep."},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib178","doi-asserted-by":"crossref","first-page":"849","DOI":"10.3390\/biomedicines11030849","article-title":"Using mean arterial pressure in hypertension diagnosis versus using either systolic or diastolic blood pressure measurements","volume":"11","author":"Kandil","year":"2023","journal-title":"Biomedicines"},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108207_bib180","doi-asserted-by":"crossref","first-page":"684","DOI":"10.21037\/apm-21-3936","article-title":"Ultrasound cardiogram-based diagnosis of cardiac hypertrophy from hypertension and analysis of its relationship with expression of autophagy-related protein","volume":"11","author":"Li","year":"2022","journal-title":"Ann. Palliat. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib181","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.patrec.2019.03.027","article-title":"Global weighted LBP-based entropy features for the assessment of pulmonary hypertension","volume":"125","author":"Gudigar","year":"2019","journal-title":"Pattern Recogn. Lett."},{"issue":"10","key":"10.1016\/j.compbiomed.2024.108207_bib182","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0224453","article-title":"A machine learning approach for the prediction of pulmonary hypertension","volume":"14","author":"Leha","year":"2019","journal-title":"PLoS One"},{"issue":"11","key":"10.1016\/j.compbiomed.2024.108207_bib183","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1093\/ehjci\/jeac147","article-title":"A framework of deep learning networks provides expert-level accuracy for the detection and prognostication of pulmonary arterial hypertension","volume":"23","author":"Diller","year":"2022","journal-title":"European Heart Journal-Cardiovascular Imaging"},{"key":"10.1016\/j.compbiomed.2024.108207_bib184","series-title":"DAGM German Conference on Pattern Recognition","first-page":"529","article-title":"Interpretable prediction of pulmonary hypertension in newborns using echocardiograms","author":"Ragnarsdottir","year":"2022"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib185","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1186\/s12880-023-01035-0","article-title":"Weakly supervised video-based cardiac detection for hypertensive cardiomyopathy","volume":"23","author":"Chen","year":"2023","journal-title":"BMC Med. Imag."},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib186","doi-asserted-by":"crossref","DOI":"10.1002\/pul2.12272","article-title":"Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning","volume":"13","author":"Liao","year":"2023","journal-title":"Pulm. Circ."},{"key":"10.1016\/j.compbiomed.2024.108207_bib188","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2022.891703","article-title":"Deep learning for detection of exercise-induced pulmonary hypertension using chest X-ray images","volume":"9","author":"Kusunose","year":"2022","journal-title":"Frontiers in Cardiovascular Medicine"},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108207_bib190","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0233166","article-title":"Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: a cross-sectional study of chronic diseases in central China","volume":"15","author":"Zhang","year":"2020","journal-title":"PLoS One"},{"issue":"6","key":"10.1016\/j.compbiomed.2024.108207_bib191","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/JBHI.2014.2337960","article-title":"Computer-aided diagnosis software for hypertensive risk determination through fundus image processing","volume":"18","author":"Morales","year":"2014","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib192","doi-asserted-by":"crossref","DOI":"10.1136\/openhrt-2019-001124","article-title":"Northern Ireland Cohort of Longitudinal Ageing. Association between hypertension and retinal vascular features in ultra-widefield fundus imaging","volume":"7","author":"Robertson","year":"2020","journal-title":"Open Heart"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib193","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans. Med. Imag."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib194","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.4.1.014503","article-title":"DR HAGIS-a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients","volume":"4","author":"Holm","year":"2017","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2024.108207_bib195","series-title":"Mach. Vis. Pattern Recognit. Res. Group","first-page":"1","article-title":"DIARETDB0 : evaluation database and methodology for diabetic retinopathy algorithms","author":"Kauppi","year":"2006"},{"key":"10.1016\/j.compbiomed.2024.108207_bib196","series-title":"The 17th British Machine Vision Conference (BMVC)","first-page":"1","article-title":"The diaretdb1 diabetic retinopathy database and evaluation protocol","author":"Kauppi","year":"2007"},{"key":"10.1016\/j.compbiomed.2024.108207_bib197","doi-asserted-by":"crossref","first-page":"31595","DOI":"10.1007\/s11042-020-09630-x","article-title":"An automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning","volume":"79","author":"Abbas","year":"2020","journal-title":"Multimed. Tool. Appl."},{"key":"10.1016\/j.compbiomed.2024.108207_bib198","doi-asserted-by":"crossref","first-page":"30107","DOI":"10.1007\/s11042-023-15044-2","article-title":"Automatic detection of hypertensive retinopathy using improved fuzzy clustering and novel loss function","volume":"82","author":"Bhimavarapu","year":"2023","journal-title":"Multimed. Tool. Appl."},{"issue":"8","key":"10.1016\/j.compbiomed.2024.108207_bib199","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.3390\/diagnostics13081439","article-title":"Mobile-HR: an ophthalmologic-based classification system for diagnosis of hypertensive retinopathy using optimized MobileNet architecture","volume":"13","author":"Sajid","year":"2023","journal-title":"Diagnostics"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib200","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1111\/jch.12661","article-title":"Impact of small renal ischemia in hypertension development: renovascular hypertension caused by small branch artery stenosis","volume":"18","author":"Mishima","year":"2016","journal-title":"J. Clin. Hypertens."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib211","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4103\/0301-4738.37595","article-title":"Understanding and using sensitivity, specificity and predictive values","volume":"56","author":"Parikh","year":"2008","journal-title":"Indian J. Ophthalmol."},{"year":"2023","author":"Khare","series-title":"Emotion Recognition and Artificial Intelligence: A Systematic Review (2014\u20132023) and Research Recommendations","key":"10.1016\/j.compbiomed.2024.108207_bib212"},{"year":"2023","author":"Salvi","series-title":"Multi-Modality Approaches for Medical Support Systems: A Systematic Review of the Last Decade","key":"10.1016\/j.compbiomed.2024.108207_bib213"},{"year":"2022","author":"Jahmunah","series-title":"Endoscopy, Video Capsule Endoscopy, and Biopsy for Automated Celiac Disease Detection: A Review","key":"10.1016\/j.compbiomed.2024.108207_bib214"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib215","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.bbe.2022.07.001","article-title":"Assessment of CT for the categorization of hemorrhagic stroke (HS) and cerebral amyloid angiopathy hemorrhage (CAAH): a review","volume":"42","author":"Sudarshan","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"issue":"23","key":"10.1016\/j.compbiomed.2024.108207_bib216","doi-asserted-by":"crossref","first-page":"8045","DOI":"10.3390\/s21238045","article-title":"Role of artificial intelligence in COVID-19 detection","volume":"21","author":"Gudigar","year":"2021","journal-title":"Sensors"},{"year":"2023","author":"Khare","series-title":"Application of Data Fusion for Automated Detection of Children with Developmental and Mental Disorders: A Systematic Review of the Last Decade","key":"10.1016\/j.compbiomed.2024.108207_bib217"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib219","first-page":"1","article-title":"ROC graphs: notes and practical considerations for researchers","volume":"31","author":"Fawcett","year":"2004","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compbiomed.2024.108207_bib94","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2022.839379","article-title":"Machine learning approaches for predicting hypertension and its associated factors using population-level data from three south asian countries","volume":"9","author":"Islam","year":"2022","journal-title":"Frontiers in Cardiovascular Medicine"},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108207_bib96","first-page":"776","article-title":"Hypertension prediction using machine learning algorithm among Indonesian adults","volume":"12","author":"Kurniawan","year":"2023","journal-title":"IAES Int. J. Artif. Intell."},{"issue":"14","key":"10.1016\/j.compbiomed.2024.108207_bib95","doi-asserted-by":"crossref","first-page":"5272","DOI":"10.3390\/s22145272","article-title":"Hypertension diagnosis with backpropagation neural networks for sustainability in public health","volume":"22","author":"Orozco Torres","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.compbiomed.2024.108207_bib122","series-title":"2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)","first-page":"262","article-title":"Blood pressure detection using CNN-LSTM model","author":"Gupta","year":"2022"},{"key":"10.1016\/j.compbiomed.2024.108207_bib124","article-title":"ExHyptNet: an explainable diagnosis of hypertension using EfficientNet with PPG signals","author":"El-Dahshan","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compbiomed.2024.108207_bib133","first-page":"2021","article-title":"Improving the accuracy in classification of blood pressure from photoplethysmography using continuous wavelet transform and deep learning","author":"Wu","year":"2021","journal-title":"Int. J. Hypertens."},{"key":"10.1016\/j.compbiomed.2024.108207_bib154","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.cmpb.2018.04.021","article-title":"Shearlet and contourlet transforms for analysis of electrocardiogram signals","volume":"161","author":"Amorim","year":"2018","journal-title":"Comput. Methods Progr. Biomed."},{"issue":"7","key":"10.1016\/j.compbiomed.2024.108207_bib155","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1016\/j.jacc.2019.12.030","article-title":"Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram","volume":"75","author":"Ko","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.compbiomed.2024.108207_bib150","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104757","article-title":"Noninvasive continuous blood pressure estimation with fewer parameters based on RA-ReliefF feature selection and MPGA-BPN models","volume":"84","author":"Zhang","year":"2023","journal-title":"Biomed. Signal Process Control"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib163","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118504","article-title":"Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis","volume":"10","author":"Melillo","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.compbiomed.2024.108207_bib164","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1007\/s12652-017-0471-y","article-title":"Automated recognition of hypertension through overnight continuous HRV monitoring","volume":"9","author":"Ni","year":"2018","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"10.1016\/j.compbiomed.2024.108207_bib171","doi-asserted-by":"crossref","DOI":"10.1016\/j.nanoen.2023.108730","article-title":"Unconstrained blood pressure monitoring based on a neural network\u2013assisted multistage pressure textile sensor","volume":"115","author":"Si","year":"2023","journal-title":"Nano Energy"},{"key":"10.1016\/j.compbiomed.2024.108207_bib207","series-title":"2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","first-page":"8095","article-title":"Detection of essential hypertension with physiological signals from wearable devices","author":"Ghosh","year":"2015"},{"key":"10.1016\/j.compbiomed.2024.108207_bib208","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103404","article-title":"MLP-BP: a novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-Mixer neural networks","volume":"73","author":"Huang","year":"2022","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib209","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.artmed.2018.12.005","article-title":"A novel dynamical approach in continuous cuffless blood pressure estimation based on ECG and PPG signals","volume":"97","author":"Sharifi","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib210","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.101942","article-title":"Investigation on the effect of Womersley number, ECG and PPG features for cuff less blood pressure estimation using machine learning","volume":"60","author":"Thambiraj","year":"2020","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib231","doi-asserted-by":"crossref","first-page":"3659","DOI":"10.1109\/ACCESS.2023.3349090","article-title":"Directional-Guided motion sensitive descriptor for automated detection of hypertension using ultrasound images","volume":"12","author":"Gudigar","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2024.108207_bib218","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102134","article-title":"Rajendra Acharya, Multi-modality approaches for medical support systems: a systematic review of the last decade","volume":"103","author":"Salvi","year":"2024","journal-title":"Inf. Fusion"},{"year":"2020","author":"Dosovitskiy","series-title":"Transformers for Image Recognition at Scale","key":"10.1016\/j.compbiomed.2024.108207_bib220"},{"key":"10.1016\/j.compbiomed.2024.108207_bib221","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: techniques, applications and challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.compbiomed.2024.108207_bib222","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107441","article-title":"Application of uncertainty quantification to artificial intelligence in healthcare: a review of last decade (2013\u20132023)","volume":"165","author":"Seoni","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108207_bib223","article-title":"A systematic review of Explainable Artificial Intelligence models and applications: recent developments and future trends","author":"Saranya","year":"2023","journal-title":"Decision analytics journal"},{"key":"10.1016\/j.compbiomed.2024.108207_bib224","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107161","article-title":"Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011\u20132022)","author":"Loh","year":"2022","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.compbiomed.2024.108207_bib225","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"year":"2015","author":"Zhou","series-title":"Learning Deep Features for Discriminative Localization\u201e arXiv - Cs - CV","key":"10.1016\/j.compbiomed.2024.108207_bib226"},{"year":"2016","author":"Ribeiro","series-title":"Why Should I Trust You?: Explaining the Predictions of Any Classifier","key":"10.1016\/j.compbiomed.2024.108207_bib227"},{"year":"2019","author":"Nori","series-title":"Interpretml: A unified framework for machine learning interpretability","key":"10.1016\/j.compbiomed.2024.108207_bib228"},{"key":"10.1016\/j.compbiomed.2024.108207_bib229","doi-asserted-by":"crossref","first-page":"152900","DOI":"10.1109\/ACCESS.2019.2948430","article-title":"Explainable prediction of chronic renal disease in the colombian population using neural networks and case-based reasoning","volume":"7","author":"Vasquez-Morales","year":"2019","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108207_bib230","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.bbe.2022.05.008","article-title":"The internet of medical things and artificial intelligence: trends, challenges, and opportunities","volume":"42","author":"Kakhi","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib93","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-67640-z","article-title":"An artificial neural network approach for predicting hypertension using NHANES data","volume":"10","author":"L\u00f3pez-Mart\u00ednez","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2024.108207_bib123","series-title":"2022 10th RSI International Conference on Robotics and Mechatronics (ICRoM)","first-page":"472","article-title":"Stress Assessment with Convolutional Neural Network Using PPG Signals","author":"Hasanpoor","year":"2022"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108207_bib125","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.bja.2020.12.035","article-title":"Deep learning models for the prediction of intraoperative hypotension","volume":"126","author":"Lee","year":"2021","journal-title":"Br. J. Anaesth."},{"key":"10.1016\/j.compbiomed.2024.108207_bib127","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102198","article-title":"Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features","volume":"63","author":"Lin","year":"2021","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib128","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.neunet.2022.04.017","article-title":"A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation","volume":"152","author":"Chen","year":"2022","journal-title":"Neural Network."},{"key":"10.1016\/j.compbiomed.2024.108207_bib129","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103891","article-title":"PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning","volume":"78","author":"Hu","year":"2022","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib130","article-title":"Continuous blood pressure monitoring using photoplethysmography and electrocardiogram signals by random forest feature selection and GWO-GBRT prediction model","author":"Liu","year":"2023","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib131","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105067","article-title":"Nonlinear features of photoplethysmography signals for Non-invasive blood pressure estimation","volume":"85","author":"Shoeibi","year":"2023","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2024.108207_bib132","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1155\/2022\/1672677","article-title":"Detection of cardiovascular disease based on PPG signals using machine learning with cloud computing","volume":"2022","author":"Sadad","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.compbiomed.2024.108207_bib156","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.inffus.2019.12.008","article-title":"Multi-level information fusion for learning a blood pressure predictive model using sensor data","volume":"58","author":"Simjanoska","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.compbiomed.2024.108207_bib232","doi-asserted-by":"crossref","first-page":"192727","DOI":"10.1109\/ACCESS.2020.3033004","article-title":"Predicting hypertensive patients with higher risk of developing vascular events using heart rate variability and machine learning","volume":"8","author":"Alkhodari","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108207_bib205","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.20","article-title":"A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China","volume":"5","author":"Liang","year":"2018","journal-title":"Sci. Data"},{"issue":"7","key":"10.1016\/j.compbiomed.2024.108207_bib187","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0236378","article-title":"A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: a retrospective study","volume":"15","author":"Zou","year":"2020","journal-title":"PLoS One"}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482524002919?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482524002919?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T20:05:08Z","timestamp":1728331508000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482524002919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":232,"alternative-id":["S0010482524002919"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108207","relation":{},"ISSN":["0010-4825"],"issn-type":[{"type":"print","value":"0010-4825"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013\u20132023)","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108207","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"108207"}}