research-article

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders

Authors:

Nathan C. Hurley,

Erica S. Spatz,

Harlan M. Krumholz,

Roozbeh Jafari,

Bobak J. MortazaviAuthors Info & Claims

ACM Transactions on Computing for Healthcare , Volume 2, Issue 1

Article No.: 9, Pages 1 - 42

https://doi.org/10.1145/3417958

Published: 30 December 2020 Publication History

Abstract

Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.

References

[1]

Huami Technology. [n.d.]. Amazfit Verge. Retrieved from https://en.amazfit.com/verge.html.

[2]

Mohammadreza Abtahi, Joshua V. Gyllinsky, Brandon Paesang, Scott Barlow, Matthew Constant, Nicholas Gomes, Oliver Tully, Susan E. D’Andrea, and Kunal Mankodiya. 2018. MagicSox: An E-textile IoT system to quantify gait abnormalities. Smart Health 5 (2018), 4--14.

[3]

Roy Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, and Benjamin Marlin. 2016. Hierarchical span-based conditional random fields for labeling and segmenting events in wearable sensor data streams. In International Conference on Machine Learning. 334--343.

[4]

Tariq Ahmad, Michael J. Pencina, Phillip J. Schulte, Emily O’Brien, David J. Whellan, Ileana L. Piãa, Dalane W. Kitzman, Kerry L. Lee, Christopher M. O’Connor, and G. Michael Felker. 2014. Clinical implications of chronic heart failure phenotypes defined by cluster analysis. Journal of the American College of Cardiology 64, 17 (2014), 1765--1774.

[5]

Ali Akbari, Roger Solis Castilla, Roozbeh Jafari, and Bobak Jack Mortazavi. 2020. Using intelligent personal annotations to improve human activity recognition for movements in natural environments. IEEE Journal of Biomedical and Health Informatics 24 (2020), 2639--2650.

[6]

Ali Akbari and Roozbeh Jafari. 2020. Personalizing activity recognition models with quantifying different types of uncertainty using wearable sensors. IEEE Transactions on Biomedical Engineering 67 (2020), 2530--2541.

[7]

Zarko Alfirevic, Gillian M. L. Gyte, Anna Cuthbert, and Declan Devane. 2017. Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2 (2017).

[8]

AliveCor. [n.d.]. AliveCor. Retrieved from https://www.alivecor.com/kardiamobile6l/.

[9]

Robert Amelard, Richard L. Hughson, Danielle K. Greaves, Kaylen J. Pfisterer, Jason Leung, David A. Clausi, and Alexander Wong. 2017. Non-contact hemodynamic imaging reveals the jugular venous pulse waveform. Scientific Reports 7 (2017), 40150.

[10]

Samsung Electronics America. [n.d.]. Samsung Heart Rate Sensor. Retrieved from https://www.samsung.com/us/heartratesensor/.

[11]

Rasmus S. Andersen, Abdolrahman Peimankar, and Sadasivan Puthusserypady. 2019. A deep learning approach for real-time detection of atrial fibrillation. Expert Systems with Applications 115 (2019), 465--473.

[12]

Stefan D. Anker, Friedrich Koehler, and William T. Abraham. 2011. Telemedicine and remote management of patients with heart failure. The Lancet 378, 9792 (2011), 731--739.

[13]

Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, and Xiaoning Qian. 2019. Adaptive activity monitoring with uncertainty quantification in switching Gaussian process models. In The 22nd International Conference on Artificial Intelligence and Statistics. 266--275.

[14]

Sherif Assaad, Wolf B. Kratzert, Benjamin Shelley, Malcolm B. Friedman, and Albert Perrino Jr. 2018. Assessment of pulmonary edema: Principles and practice. Journal of Cardiothoracic and Vascular Anesthesia 32, 2 (2018), 901--914.

[15]

Akin Avci, Stephan Bosch, Mihai Marin-Perianu, Raluca Marin-Perianu, and Paul Havinga. 2010. Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. In 23th International Conference on Architecture of Computing Systems 2010. VDE, 1--10.

[16]

Ayca Aygun, Hassan Ghasemzadeh, and Roozbeh Jafari. 2019. Robust interbeat interval and heart rate variability estimation method from various morphological features using wearable sensors. IEEE Journal of Biomedical and Health Informatics 24 (2019), 2238--2250.

[17]

Charles F. Babbs. 2012. Oscillometric measurement of systolic and diastolic blood pressures validated in a physiologic mathematical model. Biomedical Engineering Online 11, 1 (2012), 56.

[18]

David W. Baker, Doug Einstadter, Scott S. Husak, and Randall D. Cebul. 2004. Trends in postdischarge mortality and readmissions: Has length of stay declined too far? Archives of Internal Medicine 164, 5 (2004), 538--544.

[19]

Ulas Baran Baloglu, Muhammed Talo, Ozal Yildirim, Ru San Tan, and U. Rajendra Acharya. 2019. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters 122 (2019), 23--30.

Digital Library

[20]

David Bannach, Oliver Amft, and Paul Lukowicz. 2009. Automatic event-based synchronization of multimodal data streams from wearable and ambient sensors. In European Conference on Smart Sensing and Context. Springer, 135--148.

[21]

Paddy M. Barrett, Ravi Komatireddy, Sharon Haaser, Sarah Topol, Judith Sheard, Jackie Encinas, Angela J. Fought, and Eric J. Topol. 2014. Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. American Journal of Medicine 127, 1 (2014), 95--e11.

[22]

Emelia J. Benjamin, Paul Muntner, and Márcio Sommer Bittencourt. 2019. Heart disease and stroke statistics-2019 update: A report from the American Heart Association. Circulation 139, 10 (2019), e56--e528.

[23]

Terrell R. Bennett, Claudio Savaglio, David Lu, Hunter Massey, Xianan Wang, Jian Wu, and Roozbeh Jafari. 2014. Motionsynthesis toolset (most): A toolset for human motion data synthesis and validation. In Proceedings of the 4th ACM MobiHoc Workshop on Pervasive Wireless Healthcare. ACM, 25--30.

[24]

Frank C. Bennis, Carola van Pul, Jarno J. L. van den Bogaart, Peter Andriessen, Boris W. Kramer, and Tammo Delhaas. 2019. Artifacts in pulse transit time measurements using standard patient monitoring equipment. PloS One 14, 6 (2019), e0218784.

[25]

Anshuman Bhuyan, Jung Woo Choe, Byung Chul Lee, Paul Cristman, Ömer Oralkan, and Butrus T. Khuri-Yakub. 2011. Miniaturized, wearable, ultrasound probe for on-demand ultrasound screening. In 2011 IEEE International Ultrasonics Symposium. IEEE, 1060--1063.

[26]

M. Bolanos, H. Nazeran, and E. Haltiwanger. 2006. Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 4289--4294.

[27]

Joseph M. Bumgarner, Cameron T. Lambert, Ayman A. Hussein, Daniel J. Cantillon, Bryan Baranowski, Kathy Wolski, Bruce D. Lindsay, Oussama M. Wazni, and Khaldoun G. Tarakji. 2018. Smartwatch algorithm for automated detection of atrial fibrillation. Journal of the American College of Cardiology 71, 21 (2018), 2381--2388.

[28]

Alan C. Burton and Samuel Yamada. 1951. Relation between blood pressure and flow in the human forearm. Journal of Applied Physiology 4, 5 (1951), 329--339.

[29]

Ayse S. Cakmak, Erik Reinertsen, Herman A. Taylor, Amit J. Shah, and Gari D. Clifford. 2018. Personalized heart failure severity estimates using passive smartphone data. In 2018 IEEE International Conference on Big Data (Big Data’18). IEEE, 1569--1574.

[30]

Caretaker Medical. [n.d.]. Medical Papers -- Caretaker Medical. Retrieved from https://www.caretakermedical.net/medical-papers/.

[31]

Sarwat I. Chaudhry, Jennifer A. Mattera, Jeptha P. Curtis, John A. Spertus, Jeph Herrin, Zhenqiu Lin, Christopher O. Phillips, Beth V. Hodshon, Lawton S. Cooper, and Harlan M. Krumholz. 2010. Telemonitoring in patients with heart failure. New England Journal of Medicine 363, 24 (2010), 2301--2309.

[32]

Chen Chen, Roozbeh Jafari, and Nasser Kehtarnavaz. 2017. A survey of depth and inertial sensor fusion for human action recognition. Multimedia Tools and Applications 76, 3 (2017), 4405--4425.

Digital Library

[33]

Jiaming Chen, Han Peng, and Abolfazl Razi. 2017. Remote ECG monitoring kit to predict patient-specific heart abnormalities. Journal of Systemics, Cybernetics and Informatics 15, 4 (2017), 82--89.

[34]

Yu Mike Chi, Tzyy-Ping Jung, and Gert Cauwenberghs. 2010. Dry-contact and noncontact biopotential electrodes: Methodological review. IEEE Reviews in Biomedical Engineering 3 (2010), 106--119.

[35]

Muhammad E. H. Chowdhury, Amith Khandakar, Khawla Alzoubi, Samar Mansoor, Anas M. Tahir, Mamun Bin Ibne Reaz, and Nasser Al-Emadi. 2019. Real-time smart-digital stethoscope system for heart diseases monitoring. Sensors 19, 12 (2019), 2781.

[36]

Gari D. Clifford, Chengyu Liu, Benjamin Moody, David Springer, Ikaro Silva, Qiao Li, and Roger G. Mark. 2016. Classification of normal/abnormal heart sound recordings: The PhysioNet/computing in cardiology challenge 2016. In 2016 Computing in Cardiology Conference (CinC’16). IEEE, 609--612.

[37]

Robert D. Conn and James H. O’Keefe. 2009. Cardiac physical diagnosis in the digital age: An important but increasingly neglected skill (from stethoscopes to microchips). American Journal of Cardiology 104, 4 (2009), 590--595.

[38]

Wen-Xuan Dai, Yuan-Ting Zhang, Jing Liu, Xiao-Rong Ding, and Ni Zhao. 2016. Dual-modality arterial pulse monitoring system for continuous blood pressure measurement. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16). IEEE, 5773--5776.

[39]

Shishir Dash, Kirk H. Shelley, David G. Silverman, and Ki H. Chon. 2010. Estimation of respiratory rate from ECG, photoplethysmogram, and piezoelectric pulse transducer signals: A comparative study of time--frequency methods. IEEE Transactions on Biomedical Engineering 57, 5 (2010), 1099--1107.

[40]

Simon de Denus, Eileen O’Meara, Akshay S. Desai, Brian Claggett, Eldrin F. Lewis, Grégoire Leclair, Martin Jutras, Joël Lavoie, Scott D. Solomon, and Bertram Pitt. 2017. Spironolactone metabolites in TOPCAT—New insights into regional variation. New England Journal of Medicine 376, 17 (2017), 1690--1692.

[41]

Eric de Groot, G. Kees Hovingh, Albert Wiegman, Patrick Duriez, Andries J. Smit, Jean-Charles Fruchart, and John J. P. Kastelein. 2004. Measurement of arterial wall thickness as a surrogate marker for atherosclerosis. Circulation 109, 23_Suppl_1 (2004), III--33.

[42]

Bruce H. Dobkin and Clarisa Martinez. 2018. Wearable sensors to monitor, enable feedback, and measure outcomes of activity and practice. Current Neurology and Neuroscience Reports 18, 12 (2018), 87.

[43]

Mohamed Elgendi, Prashant Bobhate, Shreepal Jain, Jennifer Rutledge, James Y. Coe, Roger Zemp, Dale Schuurmans, and Ian Adatia. 2014. Time-domain analysis of heart sound intensity in children with and without pulmonary artery hypertension: A pilot study using a digital stethoscope. Pulmonary Circulation 4, 4 (2014), 685--695.

[44]

Fernando Elijovich, Myron H. Weinberger, Cheryl A. M. Anderson, Lawrence J. Appel, Michael Bursztyn, Nancy R. Cook, Richard A. Dart, Christopher H. Newton-Cheh, Frank M. Sacks, and Cheryl L. Laffer. 2016. Salt sensitivity of blood pressure: A scientific statement from the American Heart Association. Hypertension 68, 3 (2016), e7--e46.

[45]

Empatica. [n.d.]. Real-time physiological signals | E4 EDA/GSR sensor. Retrieved from https://www.empatica.com/research/e4.

[46]

Bjoern Eskofier, Sunghoon Lee, Manuela Baron, André Simon, Christine Martindale, Heiko Gaßner, and Jochen Klucken. 2017. An overview of smart shoes in the internet of health things: Gait and mobility assessment in health promotion and disease monitoring. Applied Sciences 7, 10 (2017), 986.

[47]

Ramin Fallahzadeh, Samaneh Aminikhanghahi, Ashley Nichole Gibson, and Diane J. Cook. 2016. Toward personalized and context-aware prompting for smartphone-based intervention. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16). IEEE, 6010--6013.

[48]

Ramin Fallahzadeh, Mahdi Pedram, and Hassan Ghasemzadeh. 2016. Smartsock: A wearable platform for context-aware assessment of ankle edema. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16). IEEE, 6302--6306.

[49]

Ramin Fallahzadeh, Mahdi Pedram, Ramyar Saeedi, Bahman Sadeghi, Michael Ong, and Hassan Ghasemzadeh. 2015. Smart-cuff: A wearable bio-sensing platform with activity-sensitive information quality assessment for monitoring ankle edema. In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops’15). IEEE, 57--62.

[50]

Shaobo Fang, Zeman Shao, Deborah A. Kerr, Carol J. Boushey, and Fengqing Zhu. 2019. An end-to-end image-based automatic food energy estimation technique based on learned energy distribution images: Protocol and methodology. Nutrients 11, 4 (2019), 877.

[51]

Kyle D. Feuz and Diane J. Cook. 2013. Real-time annotation tool (RAT). In Workshops at the 27th AAAI Conference on Artificial Intelligence.

[52]

Finapres Medical Systems. [n.d.]. Finapres Medical Systems | Products - Finapres® NOVA. Retrieved from http://www.finapres.com/Products/Finapres-NOVA.

[53]

Centers for Disease Control and Prevention, et al. 2019. National Diabetes Statistics Report, 2017: Estimates of Diabetes and Its Burden in the United States. Atlanta, GA: Centers for Disease Control and Prevention.

[54]

Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, and Jenna Wiens. 2017. Contextual motifs: Increasing the utility of motifs using contextual data. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 155--164.

[55]

Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, and Jenna Wiens. 2018. Deep multi-output forecasting: Learning to accurately predict blood glucose trajectories. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining. ACM, 1387--1395.

[56]

Maurizio Galderisi, Michael S. Lauer, and Daniel Levy. 1992. Echocardiographic determinants of clinical outcome in subjects with coronary artery disease (the Framingham Heart Study). American Journal of Cardiology 70, 11 (1992), 971--976.

[57]

Garmin and Garmin Ltd subsidiaries. [n.d.]. Garmin | Heart Rate Monitors. Retrieved from https://buy.garmin.com/en-US/US/c14662-p1.html.

[58]

Garmin and Garmin Ltd subsidiaries. [n.d.]. Garmin fenix® 6 | Multisport Fitness Watch. Retrieved from https://buy.garmin.com/en-US/US/p/641449.

[59]

Alan H. Gee, Diego Garcia-Olano, Joydeep Ghosh, and David Paydarfar. 2019. Explaining deep classification of time-series data with learned prototypes. arXiv preprint arXiv:1904.08935 (2019).

[60]

Robert W. Gill. 1985. Measurement of blood flow by ultrasound: Accuracy and sources of error. Ultrasound in Medicine and Biology 11, 4 (1985), 625--641.

[61]

David C. Goff, Donald M. Lloyd-Jones, Glen Bennett, Sean Coady, Ralph B. D’Agostino, Raymond Gibbons, Philip Greenland, Daniel T. Lackland, Daniel Levy, and Christopher J. O’Donnell. 2014. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Journal of the American College of Cardiology 63, 25 Part B (2014), 2935--2959.

[62]

SPRINT Research Group. 2015. A randomized trial of intensive versus standard blood-pressure control. New England Journal of Medicine 373, 22 (2015), 2103--2116.

[63]

Naomi M. Hamburg and Mark A. Creager. 2017. Pathophysiology of intermittent claudication in peripheral artery disease. Circulation Journal 81 (2017), 281--289.

[64]

Fitbit Help. [n.d.]. How do I track my heart rate with my Fitbit device? Retrieved from https://help.fitbit.com/articles/en_US/Help_article/1565.

[65]

Jette Henderson, Huan He, Bradley A. Malin, Joshua C. Denny, Abel N. Kho, Joydeep Ghosh, and Joyce C. Ho. 2018. Phenotyping through semi-supervised tensor factorization (PSST). In AMIA Annual Symposium Proceedings, Vol. 2018. American Medical Informatics Association, 564.

[66]

Katharine E. Henry, David N. Hager, Peter J. Pronovost, and Suchi Saria. 2015. A targeted real-time early warning score (TREWScore) for septic shock. Science Translational Medicine 7, 299 (2015), 299ra122--299ra122.

[67]

Balraj S. Heran, Jenny M. H. Chen, Shah Ebrahim, Tiffany Moxham, Neil Oldridge, Karen Rees, David R. Thompson, and Rod S. Taylor. 2011. Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database of Systematic Reviews 7 (2011), CD001800.

[68]

Shurouq Hijazi, Alex Page, Burak Kantarci, and Tolga Soyata. 2016. Machine learning in cardiac health monitoring and decision support. Computer 49, 11 (2016), 38--48.

Digital Library

[69]

Julia Hippisley-Cox, Carol Coupland, and Peter Brindle. 2013. Derivation and validation of QStroke score for predicting risk of ischaemic stroke in primary care and comparison with other risk scores: A prospective open cohort study. Bmj 346 (2013), f2573.

[70]

J. Hodgkinson, J. Mant, U. Martin, B. Guo, F. D. R. Hobbs, J. J. Deeks, C. Heneghan, N. Roberts, and R. J. McManus. 2011. Relative effectiveness of clinic and home blood pressure monitoring compared with ambulatory blood pressure monitoring in diagnosis of hypertension: Systematic review. Bmj 342 (2011), d3621.

[71]

Enamul Hoque and John Stankovic. 2012. AALO: Activity recognition in smart homes using active learning in the presence of overlapped activities. In 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth’12) and Workshops. IEEE, 139--146.

[72]

Zepeng Huo, Bobak J. Mortazavi, Theodora Chaspari, Nicolaas Deutz, Laura Ruebush, and Ricardo Gutierrez-Osuna. 2019. Predicting the meal macronutrient composition from continuous glucose monitors. In 2019 IEEE EMBS International Conference on Biomedical 8 Health Informatics (BHI’19). IEEE, 1--4.

[73]

Zepeng Huo, Arash PakBin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, and Bobak Mortazavi. 2020. Uncertainty quantification for deep context-aware mobile activity recognition and unknown context discovery. arXiv preprint arXiv:2003.01753 (2020).

[74]

Toan Huynh, Roozbeh Jafari, and Wan-Young Chung. 2018. An accurate bioimpedance measurement system for blood pressure monitoring. Sensors 18, 7 (2018), 2095.

[75]

Bassem Ibrahim, Ali Akbari, and Roozbeh Jafari. 2017. A novel method for pulse transit time estimation using wrist bio-impedance sensing based on a regression model. In 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS’17). IEEE, 1--4.

[76]

Bassem Ibrahim and Roozbeh Jafari. 2019. Cuffless blood pressure monitoring from an array of wrist bio-impedance sensors using subject-specific regression models: Proof of concept. IEEE Transactions on Biomedical Circuits and Systems 13, 6 (2019), 1723--1735.

[77]

Omer T. Inan, Abdul Q. Javaid, Sean Dowling, Hazar Ashouri, Mozziyar Etemadi, James A. Heller, Shuvo Roy, and Liviu Klein. 2016. Using ballistocardiography to monitor left ventricular function in heart failure patients. Journal of Cardiac Failure 22, 8 (2016), S45.

[78]

Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. 2015. Spatial transformer networks. In Advances in Neural Information Processing Systems. 2017--2025.

[79]

Shweta H. Jambukia, Vipul K. Dabhi, and Harshadkumar B. Prajapati. 2015. Classification of ECG signals using machine learning techniques: A survey. In 2015 International Conference on Advances in Computer Engineering and Applications. IEEE, 714--721.

[80]

Muammar M. Kabir, Erick A. Perez-Alday, Jason Thomas, Golriz Sedaghat, and Larisa G. Tereshchenko. 2017. Optimal configuration of adhesive ECG patches suitable for long-term monitoring of a vectorcardiogram. Journal of Electrocardiology 50, 3 (2017), 342--348.

[81]

Haik Kalantarian, Nabil Alshurafa, Tuan Le, and Majid Sarrafzadeh. 2015. Monitoring eating habits using a piezoelectric sensor-based necklace. Computers in Biology and Medicine 58 (2015), 46--55.

Digital Library

[82]

Minhee Kang, Eunkyoung Park, Baek Hwan Cho, and Kyu-Sung Lee. 2018. Recent patient health monitoring platforms incorporating internet of things-enabled smart devices. International Neurourology Journal 22, Suppl 2 (2018), S76.

[83]

Kunal N. Karmali, Philippa Davies, Fiona Taylor, Andrew Beswick, Nicole Martin, and Shah Ebrahim. 2014. Promoting patient uptake and adherence in cardiac rehabilitation. Cochrane Database of Systematic Reviews 6 (2014), CD007131.

[84]

Jared L. Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. 2018. DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology 18, 1 (2018), 24.

[85]

Chang-Sei Kim, Andrew M. Carek, Omer T. Inan, Ramakrishna Mukkamala, and Jin-Oh Hahn. 2018. Ballistocardiogram-based approach to cuffless blood pressure monitoring: Proof of concept and potential challenges. IEEE Transactions on Biomedical Engineering 65, 11 (2018), 2384--2391.

[86]

Chang-Sei Kim, Stephanie L. Ober, M. Sean McMurtry, Barry A. Finegan, Omer T. Inan, Ramakrishna Mukkamala, and Jin-Oh Hahn. 2016. Ballistocardiogram: Mechanism and potential for unobtrusive cardiovascular health monitoring. Scientific Reports 6 (2016), 31297.

[87]

Serkan Kiranyaz, Turker Ince, and Moncef Gabbouj. 2017. Personalized monitoring and advance warning system for cardiac arrhythmias. Scientific Reports 7, 1 (2017), 1--8.

[88]

Arthur Knapp, Violetta Cetrullo, Brett A. Sillars, Nat Lenzo, Wendy A. Davis, and Timothy M. E. Davis. 2014. Carotid artery ultrasonographic assessment in patients from the Fremantle Diabetes Study Phase II with carotid bruits detected by electronic auscultation. Diabetes Technology 8 Therapeutics 16, 9 (2014), 604--610.

[89]

Kirill Kochetov, Evgeny Putin, Svyatoslav Azizov, Ilya Skorobogatov, and Andrey Filchenkov. 2017. Wheeze detection using convolutional neural networks. In EPIA Conference on Artificial Intelligence. Springer, 162--173.

[90]

Kirill Kochetov, Evgeny Putin, Maksim Balashov, Andrey Filchenkov, and Anatoly Shalyto. 2018. Noise masking recurrent neural network for respiratory sound classification. In International Conference on Artificial Neural Networks. Springer, 208--217.

[91]

Harlan M. Krumholz, Sarwat I. Chaudhry, John A. Spertus, Jennifer A. Mattera, Beth Hodshon, and Jeph Herrin. 2016. Do non-clinical factors improve prediction of readmission risk?: Results from the Tele-HF study. JACC: Heart Failure 4, 1 (2016), 12--20.

[92]

Harlan M. Krumholz, Jeph Herrin, Lauren E. Miller, Elizabeth E. Drye, Shari M. Ling, Lein F. Han, Michael T. Rapp, Elizabeth H. Bradley, Brahmajee K. Nallamothu, and Wato Nsa. 2011. Improvements in door-to-balloon time in the United States, 2005 to 2010. Circulation 124, 9 (2011), 1038--1045.

[93]

David N. Ku. 1997. Blood flow in arteries. Annual Review of Fluid Mechanics 29, 1 (1997), 399--434. arXiv:https://doi.org/10.1146/annurev.fluid.29.1.399

[94]

Roberto M. Lang, Luigi P. Badano, Victor Mor-Avi, Jonathan Afilalo, Anderson Armstrong, Laura Ernande, Frank A. Flachskampf, Elyse Foster, Steven A. Goldstein, and Tatiana Kuznetsova. 2015. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. European Heart Journal-Cardiovascular Imaging 16, 3 (2015), 233--271.

[95]

Oscar D. Lara and Miguel A. Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys 8 Tutorials 15, 3 (2012), 1192--1209.

[96]

Changhee Lee, Jinsung Yoon, and Mihaela Van Der Schaar. 2019. Dynamic-DeepHit: A deep learning approach for dynamic survival analysis with competing risks based on longitudinal data. IEEE Transactions on Biomedical Engineering 67 (2019), 122--133.

[97]

Changhee Lee, William R. Zame, Jinsung Yoon, and Mihaela van der Schaar. 2018. Deephit: A deep learning approach to survival analysis with competing risks. In 32nd AAAI Conference on Artificial Intelligence.

[98]

Douglas S. Lee, Philimon Gona, Ramachandran S. Vasan, Martin G. Larson, Emelia J. Benjamin, Thomas J. Wang, Jack V. Tu, and Daniel Levy. 2009. Relation of disease etiology and risk factors to heart failure with preserved or reduced ejection fraction: Insights from the National Heart, Lung, and Blood Institute’s Framingham Heart Study. Circulation 119, 24 (2009), 3070.

[99]

Joong Hoon Lee, Hannes Gamper, Ivan Tashev, Steven Dong, Siyuan Ma, Jacquelin Remaley, James D. Holbery, and Sang Ho Yoon. 2020. Stress monitoring using multimodal bio-sensing headset. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems Extended Abstracts. 1--7.

Digital Library

[100]

H. Ishwaran, D. Lee, and N. Chen. 2017. Boosted nonparametric hazards with time-dependent covariates. SSRN 2906586 (2017).

[101]

Shuang Leng, Ru San Tan, Kevin Tshun Chuan Chai, Chao Wang, Dhanjoo Ghista, and Liang Zhong. 2015. The electronic stethoscope. Biomedical Engineering Online 14, 1 (2015), 66.

[102]

R. Levin, M. Dolgin, C. Fox, and R. Gorlin. 1994. The criteria committee of the new york heart association: Nomenclature and criteria for diagnosis of diseases of the heart and great vessels. LWW Handbooks 9 (1994), 344.

[103]

Changzhi Li, Julie Cummings, Jeffrey Lam, Eric Graves, and Wenhsing Wu. 2009. Radar remote monitoring of vital signs. IEEE Microwave Magazine 10, 1 (2009), 47--56.

[104]

Qiao Li, Cadathur Rajagopalan, and Gari D. Clifford. 2014. A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine 117, 3 (2014), 435--447.

Digital Library

[105]

Gregory Y. H. Lip, Robby Nieuwlaat, Ron Pisters, Deirdre A. Lane, and Harry J. G. M. Crijns. 2010. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: The euro heart survey on atrial fibrillation. Chest 137, 2 (2010), 263--272.

[106]

Zachary C. Lipton, David C. Kale, Charles Elkan, and Randall Wetzel. 2015. Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677 (2015).

[107]

Zachary C. Lipton, David C. Kale, and Randall Wetzel. 2016. Modeling missing data in clinical time series with RNNs. arXiv preprint arXiv:1606.04130 (2016).

[108]

Zachary C. Lipton, David C. Kale, and Randall Wetzel. 2016. Modeling missing data in clinical time series with RNNs. arXiv preprint arXiv:1606.04130 (2016).

[109]

Chengyu Liu, David Springer, Qiao Li, Benjamin Moody, Ricardo Abad Juan, Francisco J. Chorro, Francisco Castells, José Millet Roig, Ikaro Silva, Alistair E. W. Johnson, et al. 2016. An open access database for the evaluation of heart sound algorithms. Physiological Measurement 37, 12 (2016), 2181.

[110]

Yang Liu, Zhenjiang Li, Zhidan Liu, and Kaishun Wu. 2019. Real-time arm skeleton tracking and gesture inference tolerant to missing wearable sensors. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 287--299.

Digital Library

[111]

Donald M. Lloyd-Jones, Martin G. Larson, Eric P. Leip, Alexa Beiser, Ralph B. D’Agostino, William B. Kannel, Joanne M. Murabito, Ramachandran S. Vasan, Emelia J. Benjamin, and Daniel Levy. 2002. Lifetime risk for developing congestive heart failure: The Framingham Heart Study. Circulation 106, 24 (2002), 3068--3072.

[112]

Suhas Lohit, Qiao Wang, and Pavan Turaga. 2019. Temporal transformer networks: Joint learning of invariant and discriminative time warping. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 12426--12435.

[113]

Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 4765--4774. Retrieved from http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf.

[114]

T. Ma and Yuan-Ting Zhang. 2006. A correlation study on the variabilities in pulse transit time, blood pressure, and heart rate recorded simultaneously from healthy subjects. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 996--999.

[115]

Ralph Maddison, Jonathan C. Rawstorn, Anna Rolleston, Robyn Whittaker, Ralph Stewart, Jocelyne Benatar, Ian Warren, Yannan Jiang, and Nicholas Gant. 2014. The remote exercise monitoring trial for exercise-based cardiac rehabilitation (REMOTE-CR): A randomised controlled trial protocol. BMC Public Health 14, 1 (2014), 1236.

[116]

Sumit Majumder, Leon Chen, Ognian Marinov, Chih-Hung Chen, Tapas Mondal, and M. Jamal Deen. 2018. Noncontact wearable wireless ECG systems for long-term monitoring. IEEE Reviews in Biomedical Engineering 11 (2018), 306--321.

[117]

Bruce Del Mar. 2005. The history of clinical Holter monitoring. Annals of Noninvasive Electrocardiology 10, 2 (2005), 226--230.

[118]

Neil A. Martin, Curtis Doberstein, Cynthia Zane, Michael J. Caron, Kathleen Thomas, and Donald P. Becker. 1992. Posttraumatic cerebral arterial spasm: Transcranial Doppler ultrasound, cerebral blood flow, and angiographic findings. Journal of Neurosurgery 77, 4 (1992), 575--583.

[119]

Sherin M. Mathews, Chandra Kambhamettu, and Kenneth E. Barner. 2018. A novel application of deep learning for single-lead ECG classification. Computers in Biology and Medicine 99 (2018), 53--62.

[120]

Mario J. McLoughlin and Santiago McLoughlin. 2013. Cardiac auscultation: Preliminary findings of a pilot study using continuous wave Doppler and comparison with classic auscultation. International Journal of Cardiology 167, 2 (2013), 590--591.

[121]

Robert L. McNamara, Yongfei Wang, Jeph Herrin, Jeptha P. Curtis, Elizabeth H. Bradley, David J. Magid, Eric D. Peterson, Martha Blaney, Paul D. Frederick, and Harlan M. Krumholz. 2006. Effect of door-to-balloon time on mortality in patients with ST-segment elevation myocardial infarction. Journal of the American College of Cardiology 47, 11 (2006), 2180--2186.

[122]

Authors/Task Force Members, A. John Camm, Gregory Y. H. Lip, Raffaele De Caterina, Irene Savelieva, Dan Atar, Stefan H. Hohnloser, Gerhard Hindricks, Paulus Kirchhof, and ESC Committee for Practice Guidelines. 2012. 2012 focused update of the ESC guidelines for the management of atrial fibrillation: An update of the 2010 ESC guidelines for the management of atrial fibrillation developed with the special contribution of the European Heart Rhythm Association. European Heart Journal 33, 21 (2012), 2719--2747.

[123]

Fen Miao, Zengding Liu, Jikui Liu, Bo Wen, and Ye Li. 2019. Multi-sensor fusion approach for cuff-less blood pressure measurement. IEEE Journal of Biomedical and Health Informatics 24 (2019), 79--91.

[124]

Sadegh Mirshekarian, Razvan Bunescu, Cindy Marling, and Frank Schwartz. 2017. Using LSTMs to learn physiological models of blood glucose behavior. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17). IEEE, 2887--2891.

[125]

Gary F. Mitchell, Shih-Jen Hwang, Ramachandran S. Vasan, Martin G. Larson, Michael J. Pencina, Naomi M. Hamburg, Joseph A. Vita, Daniel Levy, and Emelia J. Benjamin. 2010. Arterial stiffness and cardiovascular events: The framingham heart study. Circulation 121, 4 (2010), 505.

[126]

Reham Mohamed and Moustafa Youssef. 2017. Heartsense: Ubiquitous accurate multi-modal fusion-based heart rate estimation using smartphones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1--18.

Digital Library

[127]

George B. Moody and Roger G. Mark. 2001. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20, 3 (2001), 45--50. https://www.ncbi.nlm.nih.gov/ /11446209.

[128]

Christopher L. Moore and Joshua A. Copel. 2011. Point-of-care ultrasonography. New England Journal of Medicine 364, 8 (2011), 749--757.

[129]

Bobak Mortazavi, Suneil Nyamathi, Sunghoon Ivan Lee, Thomas Wilkerson, Hassan Ghasemzadeh, and Majid Sarrafzadeh. 2013. Near-realistic mobile exergames with wireless wearable sensors. IEEE Journal of Biomedical and Health Informatics 18, 2 (2013), 449--456.

[130]

Bobak Mortazavi, Mohammad Pourhomayoun, Hassan Ghasemzadeh, Roozbeh Jafari, Christian K. Roberts, and Majid Sarrafzadeh. 2014. Context-aware data processing to enhance quality of measurements in wireless health systems: An application to met calculation of exergaming actions. IEEE Internet of Things Journal 2, 1 (2014), 84--93.

[131]

Bobak J. Mortazavi, Nicholas S. Downing, Emily M. Bucholz, Kumar Dharmarajan, Ajay Manhapra, Shu-Xia Li, Sahand N. Negahban, and Harlan M. Krumholz. 2016. Analysis of machine learning techniques for heart failure readmissions. Circulation: Cardiovascular Quality and Outcomes 9, 6 (2016), 629--640.

[132]

Bobak Jack Mortazavi, Mohammad Pourhomayoun, Gabriel Alsheikh, Nabil Alshurafa, Sunghoon Ivan Lee, and Majid Sarrafzadeh. 2014. Determining the single best axis for exercise repetition recognition and counting on smartwatches. In 2014 11th International Conference on Wearable and Implantable Body Sensor Networks. IEEE, 33--38.

Digital Library

[133]

Brahmajee K. Nallamothu, Sharon-Lise T. Normand, Yongfei Wang, Timothy P. Hofer, John E. Brush Jr., John C. Messenger, Elizabeth H. Bradley, John S. Rumsfeld, and Harlan M. Krumholz. 2015. Relation between door-to-balloon times and mortality after primary percutaneous coronary intervention over time: A retrospective study. The Lancet 385, 9973 (2015), 1114--1122.

[134]

Viswam Nathan, Sudip Paul, Temiloluwa Prioleau, Li Niu, Bobak J. Mortazavi, Stephen A. Cambone, Ashok Veeraraghavan, Ashutosh Sabharwal, and Roozbeh Jafari. 2018. A survey on smart homes for aging in place: Toward solutions to the specific needs of the elderly. IEEE Signal Processing Magazine 35, 5 (2018), 111--119.

[135]

G. L. Nayler, D. N. Firmin, D. B. Longmore, et al. 1986. Blood flow imaging by cine magnetic resonance. Journal of Computer Assisted Tomography 10, 5 (1986), 715--722.

[136]

Lisa Nobel, Nancy E. Mayo, James Hanley, Lyne Nadeau, and Stella S. Daskalopoulou. 2014. MyRisk_Stroke calculator: A personalized stroke risk assessment tool for the general population. Journal of Clinical Neurology 10, 1 (2014), 1--9.

[137]

Eoin O’Brien, Bernard Waeber, Gianfranco Parati, Jan Staessen, and Martin G. Myers. 2001. Blood pressure measuring devices: Recommendations of the European society of hypertension. Bmj 322, 7285 (2001), 531--536.

[138]

Christopher J. O’Donnell and Roberto Elosua. 2008. Cardiovascular risk factors. Insights from Framingham Heart Study. Revista Española de Cardiología (English Edition) 61, 3 (2008), 299--310.

[139]

Jeeheh Oh, Jiaxuan Wang, and Jenna Wiens. 2018. Learning to exploit invariances in clinical time-series data using sequence transformer networks. arXiv preprint arXiv:1808.06725 (2018).

[140]

Shu Lih Oh, Eddie Y. K. Ng, Ru San Tan, and U. Rajendra Acharya. 2018. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Computers in Biology and Medicine 102 (2018), 278--287.

Digital Library

[141]

Michael H. Olsen, Sonia Y. Angell, Samira Asma, Pierre Boutouyrie, Dylan Burger, Julio A. Chirinos, Albertino Damasceno, Christian Delles, Anne-Paule Gimenez-Roqueplo, and Dagmara Hering. 2016. A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: The Lancet commission on hypertension. The Lancet 388, 10060 (2016), 2665--2712.

[142]

Michael K. Ong, Patrick S. Romano, Sarah Edgington, Harriet U. Aronow, Andrew D. Auerbach, Jeanne T. Black, Teresa De Marco, Jose J. Escarce, Lorraine S. Evangelista, and Barbara Hanna. 2016. Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: The better effectiveness after transition--heart failure (BEAT-HF) randomized clinical trial. JAMA Internal Medicine 176, 3 (2016), 310--318.

[143]

Edward Onusko. 2003. Diagnosing secondary hypertension. American Family Physician 67, 1 (2003), 67--74. https://www.ncbi.nlm.nih.gov/ /12537168.

[144]

Francisco Javier Ordóñez and Daniel Roggen. 2016. Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16, 1 (2016), 115.

[145]

Andreas Østvik, Erik Smistad, Svein Arne Aase, Bjørn Olav Haugen, and Lasse Lovstakken. 2019. Real-time standard view classification in transthoracic echocardiography using convolutional neural networks. Ultrasound in Medicine 8 Biology 45, 2 (2019), 374--384.

[146]

Kazushige Ouchi and Miwako Doi. 2013. Smartphone-based monitoring system for activities of daily living for elderly people and their relatives etc. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. ACM, 103--106.

Digital Library

[147]

Rajkumar Palaniappan, Kenneth Sundaraj, and Nizam Uddin Ahamed. 2013. Machine learning in lung sound analysis: A systematic review. Biocybernetics and Biomedical Engineering 33, 3 (2013), 129--135.

[148]

Hans Pasterkamp, Paul L. P. Brand, Mark Everard, Luis Garcia-Marcos, Hasse Melbye, and Kostas N. Priftis. 2016. Towards the standardisation of lung sound nomenclature. European Respiratory Journal 47, 3 (2016), 724--732.

[149]

R. A. Payne, C. N. Symeonides, D. J. Webb, and S. R. J. Maxwell. 2006. Pulse transit time measured from the ECG: An unreliable marker of beat-to-beat blood pressure. Journal of Applied Physiology 100, 1 (2006), 136--141.

[150]

Diego Perna. 2018. Convolutional neural networks learning from respiratory data. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM’18). IEEE, 2109--2113.

[151]

Diego Perna and Andrea Tagarelli. 2019. Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks. In 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS’19). IEEE, 50--55.

[152]

Lukáš Peter, Norbert Noury, and M. Cerny. 2014. A review of methods for non-invasive and continuous blood pressure monitoring: Pulse transit time method is promising? IRBM 35, 5 (2014), 271--282.

[153]

Marc A. Pfeffer, Brian Claggett, Susan F. Assmann, Robin Boineau, Inder S. Anand, Nadine Clausell, Akshay S. Desai, Rafael Diaz, Jerome L. Fleg, and Ivan Gordeev. 2015. Regional variation in patients and outcomes in the treatment of preserved cardiac function heart failure with an aldosterone antagonist (TOPCAT) trial. Circulation 131, 1 (2015), 34--42.

[154]

Ivan Pires, Nuno Garcia, Nuno Pombo, and Francisco Flórez-Revuelta. 2016. From data acquisition to data fusion: A comprehensive review and a roadmap for the identification of activities of daily living using mobile devices. Sensors 16, 2 (2016), 184.

[155]

Ron Pisters, Deirdre A. Lane, Robby Nieuwlaat, Cees B. De Vos, Harry J. G. M. Crijns, and Gregory Y. H. Lip. 2010. A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: The Euro heart survey. Chest 138, 5 (2010), 1093--1100.

[156]

Bertram Pitt, Marc A. Pfeffer, Susan F. Assmann, Robin Boineau, Inder S. Anand, Brian Claggett, Nadine Clausell, Akshay S. Desai, Rafael Diaz, Jerome L. Fleg, et al. 2014. Spironolactone for heart failure with preserved ejection fraction. New England Journal of Medicine 370, 15 (2014), 1383--1392.

[157]

Mihaela Porumb, Saverio Stranges, Antonio Pescapè, and Leandro Pecchia. 2020. Precision medicine and artificial intelligence: A pilot study on deep learning for hypoglycemic events detection based on ECG. Scientific Reports 10, 1 (2020), 1--16.

[158]

Mohammad Pourhomayoun, Nabil Alshurafa, Bobak Mortazavi, Hassan Ghasemzadeh, Konstantinos Sideris, Bahman Sadeghi, Michael Ong, Lorraine Evangelista, Patrick Romano, and Andrew Auerbach. 2014. Multiple model analytics for adverse event prediction in remote health monitoring systems. In 2014 IEEE Healthcare Innovation Conference (HIC’14). IEEE, 106--110.

[159]

Qardio. [n.d.]. Irregular heart beat detection. Retrieved from http://support.getqardio.com/hc/en-us/articles/203579482.

[160]

Qardio. [n.d.]. QardioCore. Retrieved from https://store.getqardio.com/products/qardiocore.

[161]

David M. Reboussin, Norrina B. Allen, Michael E. Griswold, Eliseo Guallar, Yuling Hong, Daniel T. Lackland, Edgar Pete R. Miller, Tamar Polonsky, Angela M. Thompson-Paul, and Suma Vupputuri. 2018. Systematic review for the 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Journal of the American College of Cardiology 71, 19 (2018), 2176--2198.

[162]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1135--1144.

Digital Library

[163]

Oura Ring. [n.d.]. Get to know Oura. Retrieved from https://ouraring.com/get-to-know-oura.

[164]

B. M. Rocha, D. Filos, L. Mendes, I. Vogiatzis, E. Perantoni, E. Kaimakamis, P. Natsiavas, A. Oliveira, C. Jácome, A. Marques, et al. 2017. A respiratory sound database for the development of automated classification. In International Conference on Biomedical and Health Informatics. Springer, 33--37.

[165]

Seyed Ali Rokni and Hassan Ghasemzadeh. 2016. Plug-n-learn: Automatic learning of computational algorithms in human-centered internet-of-things applications. In Proceedings of the 53rd Annual Design Automation Conference. ACM, 139.

[166]

Seyed Ali Rokni and Hassan Ghasemzadeh. 2018. Autonomous training of activity recognition algorithms in mobile sensors: A transfer learning approach in context-invariant views. IEEE Transactions on Mobile Computing 17, 8 (2018), 1764--1777.

[167]

Seyed Ali Rokni, Marjan Nourollahi, and Hassan Ghasemzadeh. 2018. Personalized human activity recognition using convolutional neural networks. In 32nd AAAI Conference on Artificial Intelligence.

[168]

José Rafael Romero, Sarah R. Preis, Alexa Beiser, Charles DeCarli, Anand Viswanathan, Sergi Martinez-Ramirez, Carlos S. Kase, Philip A. Wolf, and Sudha Seshadri. 2014. Risk factors, stroke prevention treatments, and prevalence of cerebral microbleeds in the Framingham Heart Study. Stroke 45, 5 (2014), 1492--1494.

[169]

Jennifer A. Rymer and Sunil V. Rao. 2019. Enhancement of risk prediction with machine learning: Rise of the machines. JAMA Network Open 2, 7 (2019), e196823--e196823.

[170]

Saeed Saadatnejad, Mohammadhosein Oveisi, and Matin Hashemi. 2019. LSTM-based ECG classification for continuous monitoring on personal wearable devices. IEEE Journal of Biomedical and Health Informatics 24 (2019), 515--523.

[171]

Rajani Shankar Sadasivam, Erin M. Borglund, Roy Adams, Benjamin M. Marlin, and Thomas K. Houston. 2016. Impact of a collective intelligence tailored messaging system on smoking cessation: The Perspect randomized experiment. Journal of Medical Internet Research 18, 11 (2016), e285.

[172]

Manuel Sánchez-de-la Torre, Abdelnaby Khalyfa, Alicia Sánchez-de-la Torre, Montserrat Martinez-Alonso, Miguel Ángel Martinez-García, Antonia Barceló, Patricia Lloberes, Francisco Campos-Rodriguez, Francisco Capote, and Maria José Diaz-de Atauri. 2015. Precision medicine in patients with resistant hypertension and obstructive sleep apnea: Blood pressure response to continuous positive airway pressure treatment. Journal of the American College of Cardiology 66, 9 (2015), 1023--1032.

[173]

Renate B. Schnabel, Xiaoyan Yin, Philimon Gona, Martin G. Larson, Alexa S. Beiser, David D. McManus, Christopher Newton-Cheh, Steven A. Lubitz, Jared W. Magnani, Patrick T. Ellinor, et al. 2015. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: A cohort study. The Lancet 386, 9989 (2015), 154--162.

[174]

Kaan Sel, Jialu Zhao, Bassem Ibrahim, and Roozbeh Jafari. 2019. Measurement of chest physiological signals using wirelessly coupled bio-impedance patches. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’19). IEEE, 376--381.

[175]

Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan, and Youngki Lee. 2015. The case for smartwatch-based diet monitoring. In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops’15). IEEE, 585--590.

[176]

Ellora Sen-Gupta, Donald E. Wright, James W. Caccese, John A. Wright Jr., Elise Jortberg, Viprali Bhatkar, Melissa Ceruolo, Roozbeh Ghaffari, Dennis L. Clason, James P. Maynard, et al. 2019. A pivotal study to validate the performance of a novel wearable sensor and system for biometric monitoring in clinical and remote environments. Digital Biomarkers 3, 1 (2019), 1--13.

[177]

Arijit Sinharay, Deb Ghosh, Parijat Deshpande, Shahnawaz Alam, Rohan Banerjee, and Arpan Pal. 2016. Smartphone based digital stethoscope for connected health--A direct acoustic coupling technique. In 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE’16). IEEE, 193--198.

[178]

Roger Solis Castilla, Arash Pakbin, Ali Akbari, Bobak J. Mortazavi, and Roozbeh Jafari. 2019. A human-centered wearable sensing platform with intelligent automated data annotation capabilities. In Proceedings of the International Conference on Internet of Things Design and Implementation. ACM, 255--260.

[179]

Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, and Andreas Spanias. 2018. Attend and diagnose: Clinical time series analysis using attention models. In 32nd AAAI Conference on Artificial Intelligence.

[180]

Janani C. Sriram, Minho Shin, Tanzeem Choudhury, and David Kotz. 2009. Activity-aware ECG-based patient authentication for remote health monitoring. In Proceedings of the 2009 International Conference on Multimodal Interfaces. ACM, 297--304.

[181]

Steven R. Steinhubl, Jill Waalen, Alison M. Edwards, Lauren M. Ariniello, Rajesh R. Mehta, Gail S. Ebner, Chureen Carter, Katie Baca-Motes, Elise Felicione, and Troy Sarich. 2018. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: The mSToPS randomized clinical trial. Jama 320, 2 (2018), 146--155.

[182]

Apple Support. [n.d.]. Your heart rate. What it means, and where on Apple Watch you’ll find it. Retrieved from https://support.apple.com/en-us/HT204666.

[183]

Harini Suresh, Jen J. Gong, and John V. Guttag. 2018. Learning tasks for multitask learning: Heterogenous patient populations in the ICU. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining. 802--810.

[184]

Harini Suresh, Jen J. Gong, and John V. Guttag. 2018. Learning tasks for multitask learning: Heterogenous patient populations in the ICU. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining. ACM, 802--810.

[185]

Stephen B. Thacker, Donna Stroup, Man-huei Chang, and Sonja L Henderson. 2001. Continuous electronic heart rate monitoring for fetal assessment during labor. Cochrane Database of Systematic Reviews 2 (2001).

[186]

Simi Susan Thomas, Viswam Nathan, Chengzhi Zong, Ebunoluwa Akinbola, Antoine Lourdes Praveen Aroul, Lijoy Philipose, Karthikeyan Soundarapandian, Xiangrong Shi, and Roozbeh Jafari. 2014. BioWatch-A wrist watch based signal acquisition system for physiological signals including blood pressure. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2286--2289.

[187]

Zachary Trujillo, Viswam Nathan, Gerard L. Coté, and Roozbeh Jafari. 2017. Design and parametric analysis of a wearable dual-photoplethysmograph based system for pulse wave velocity detection. In 2017 IEEE International Symposium on Circuits and Systems (ISCAS’17). IEEE, 1--4.

[188]

Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2019. Multimodal transformer for unaligned multimodal language sequences. arXiv preprint arXiv:1906.00295 (2019).

[189]

Polar USA. [n.d.]. Polar USA. Retrieved from https://www.polar.com/us-en/products/compare.

[190]

Valencell. [n.d.]. Blood pressure. Retrieved from https://valencell.com/bloodpressure/.

[191]

Valencell. [n.d.]. Valencell | Customers. Retrieved from https://valencell.com/customers/.

[192]

Nick van Boven, Linda C. Battes, K. Martijn Akkerhuis, Dimitris Rizopoulos, Kadir Caliskan, Sharda S. Anroedh, Wisam Yassi, Olivier C. Manintveld, Jan-Hein Cornel, Alina A. Constantinescu, et al. 2018. Toward personalized risk assessment in patients with chronic heart failure: Detailed temporal patterns of NT-proBNP, troponin T, and CRP in the Bio-SHiFT study. American Heart Journal 196 (2018), 36--48.

[193]

Kristof Van Laerhoven, Marko Borazio, and Jan Hendrik Burdinski. 2015. Wear is your mobile? Investigating phone carrying and use habits with a wearable device. Frontiers in ICT 2 (2015), 10.

[194]

Gabor Vinci, Stefan Lindner, Francesco Barbon, Sebastian Mann, Maximilian Hofmann, Alexander Duda, Robert Weigel, and Alexander Koelpin. 2013. Six-port radar sensor for remote respiration rate and heartbeat vital-sign monitoring. IEEE Transactions on Microwave Theory and Techniques 61, 5 (2013), 2093--2100.

[195]

Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu. 2019. Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters 119 (2019), 3--11.

Digital Library

[196]

Jeremiah Wasserlauf, Cindy You, Ruchi Patel, Alexander Valys, David Albert, and Rod Passman. 2019. Smartwatch performance for the detection and quantification of atrial fibrillation. Circulation: Arrhythmia and Electrophysiology 12, 6 (2019), e006834.

[197]

Sören Weyer, Tobias Menden, Lennart Leicht, Steffen Leonhardt, and Tobias Wartzek. 2015. Development of a wearable multi-frequency impedance cardiography device. Journal of Medical Engineering 8 Technology 39, 2 (2015), 131--137.

[198]

Withings. [n.d.]. Fitness trackers and hybrid smartwatches by Withings. Retrieved from https://www.withings.com/us/en/watches.

[199]

S. P. Wright, D. Verouhis, G. Gamble, K. Swedberg, N. Sharpe, and R. N. Doughty. 2003. Factors influencing the length of hospital stay of patients with heart failure. European Journal of Heart Failure 5, 2 (2003), 201--209.

[200]

Michael Xie, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon. 2016. Transfer learning from deep features for remote sensing and poverty mapping. In 30th AAAI Conference on Artificial Intelligence.

[201]

Jianchu Yao, Elizabeth M. B. Weaver, Brandon D. Langley, Stephanie M. George, and Sonya R. Hardin. 2017. Monitoring peripheral edema of heart failure patients at home: Device, algorithm, and clinic study. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17). IEEE, 4074--4077.

[202]

Özal Yildirim. 2018. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Computers in Biology and Medicine 96 (2018), 189--202.

[203]

Cheuk-Man Yu, L. I. Wang, Elaine Chau, Raymond Hon-Wah Chan, Shun-Ling Kong, Man-Oi Tang, Jill Christensen, Robert W. Stadler, and Chu-Pak Lau. 2005. Intrathoracic impedance monitoring in patients with heart failure: Correlation with fluid status and feasibility of early warning preceding hospitalization. Circulation 112, 6 (2005), 841--848.

[204]

Ruoxi Yu, Yali Zheng, Ruikai Zhang, Yuqi Jiang, and Carmen C. Y. Poon. 2019. Using a multi-task recurrent neural network with attention mechanisms to predict hospital mortality of patients. IEEE Journal of Biomedical and Health Informatics 24 (2019), 486--492.

[205]

Özgür Yürür, Chi Harold Liu, and Wilfrido Moreno. 2015. Light-weight online unsupervised posture detection by smartphone accelerometer. IEEE Internet of Things Journal 2, 4 (2015), 329--339.

[206]

David Zeevi, Tal Korem, Niv Zmora, David Israeli, Daphna Rothschild, Adina Weinberger, Orly Ben-Yacov, Dar Lador, Tali Avnit-Sagi, Maya Lotan-Pompan, et al. 2015. Personalized nutrition by prediction of glycemic responses. Cell 163, 5 (2015), 1079--1094.

[207]

Yuezhou Zhang, Zhicheng Yang, Ke Lan, Xiaoli Liu, Zhengbo Zhang, Peiyao Li, Desen Cao, Jiewen Zheng, and Jianli Pan. 2019. Sleep stage classification using bidirectional lstm in wearable multi-sensor systems. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS’19). IEEE, 443--448.

[208]

Yuan-ting Zhang, Carmen C. Y. Poon, Chun-hung Chan, Martin W. W. Tsang, and Kin-fai Wu. 2006. A health-shirt using e-textile materials for the continuous and cuffless monitoring of arterial blood pressure. In 2006 3rd IEEE/EMBS International Summer School on Medical Devices and Biosensors. IEEE, 86--89.

[209]

Zhilin Zhang. 2015. Photoplethysmography-based heart rate monitoring in physical activities via joint sparse spectrum reconstruction. IEEE Transactions on Biomedical Engineering 62, 8 (2015), 1902--1910.

[210]

Boback Ziaeian and Gregg C. Fonarow. 2016. Epidemiology and aetiology of heart failure. Nature Reviews Cardiology 13, 6 (2016), 368.

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Xu TJost EMesser LCook PForlenza GSankaranarayanan SFiesler CVoida S(2024)“Obviously, Nothing's Gonna Happen in Five Minutes”: How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes ManagementProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642612(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642612
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A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders

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Published In

cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 2, Issue 1

Special Issue on Wearable Technologies for Smart Health: Part 2 and Regular Papers

January 2021

204 pages

EISSN:2637-8051

DOI:10.1145/3446563

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

Issue’s Table of Contents

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 December 2020

Accepted: 01 August 2020

Revised: 01 July 2020

Received: 01 August 2019

Published in HEALTH Volume 2, Issue 1

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Cited By

Allemailem KAlmousa SAlissa MAlrumaihi FAlharbi HAlmansour NAldaiji LAbouzied AAlsugoor MAlasmari OAlbakawi MStride J(2025)Innovations in quantitative rapid testing: Early prediction of health risksCurrent Problems in Cardiology10.1016/j.cpcardiol.2025.10300050:4(103000)Online publication date: Apr-2025
https://doi.org/10.1016/j.cpcardiol.2025.103000
Razek A(2024)Interaction of electromagnetic fields with body-onboard devicesExploration of Digital Health Technologies10.37349/edht.2024.00015(124-134)Online publication date: 17-Jun-2024
https://doi.org/10.37349/edht.2024.00015
Xu TJost EMesser LCook PForlenza GSankaranarayanan SFiesler CVoida S(2024)“Obviously, Nothing's Gonna Happen in Five Minutes”: How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes ManagementProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642612(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642612
Razek A(2023)Assessment of a Functional Electromagnetic Compatibility Analysis of Near-Body Medical Devices Subject to Electromagnetic Field PerturbationElectronics10.3390/electronics1223478012:23(4780)Online publication date: 25-Nov-2023
https://doi.org/10.3390/electronics12234780
Kimball JGazi AOzmen GJung HShandhi MMabrouk SGharehbaghi SGanti VInan O(2023)Noninvasive Multimodal Physiological Sensing SystemsEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00094-7(236-252)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-822548-6.00094-7
Kumar PRajak DAbubakar MAli SHussain M(2021)3D Printing Technology for Biomedical Practice: A ReviewJournal of Materials Engineering and Performance10.1007/s11665-021-05792-330:7(5342-5355)Online publication date: 26-Apr-2021
https://doi.org/10.1007/s11665-021-05792-3

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