Browse > Article
http://dx.doi.org/10.14400/JDC.2020.18.8.231

Heart rate monitoring and predictability of diabetes using ballistocardiogram(pilot study)  

Choi, Sang-Ki (Dept. of Integrated Medicine, Sunmoon University)
Lee, Geo-Lyong (Dept. of Integrated Medicine, Sunmoon University)
Publication Information
Journal of Digital Convergence / v.18, no.8, 2020 , pp. 231-242 More about this Journal
Abstract
The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.
Keywords
ballistocardiogram; diabetes prediction; artificial neural network; smart healthcare; heart rate; heart rate variability;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 B. G. Wallin, E. C. Hart, E. A. Wehrwein, N. Charkoudian and M. J. Joyner. (2010). Relationship between breathing and cardiovascular function at rest:sex-related differences. Acta Physiol (Oxf). 2010 October ; 193-200. doi:10.1111/j.1748-1716.2010.02126.x.
2 Araz Rawshani. (2017). Pocket guide to ECG interpretation. University of Gothengurg.
3 Pollock P. (1957). Ballistocardiography: a clinical review. Canadian Medical Association journal, 76(9), 778-783.
4 Gordon J. W. (1877). Certain Molar Movements of the Human Body produced by the Circulation of the Blood. Journal of anatomy and physiology, 11(Pt3), 533-536.
5 O. T. Inan et al. (2015). Ballistocardiography and Seismocardiography: A Review of Recent Advances. IEEE Journal of Biomedical and Health Informatics, vol. 19(4), 1414-1427. doi: 10.1109/JBHI.2014.2361732.   DOI
6 Eblen-Zajjur A. (2003). A simple ballistocardiographic system for a medical cardiovascular physiology course. Advances in physiology education, 27(1-4), 224-229. DOI : 10.1152/advan.00025.2002   DOI
7 William B. Thompson, Maurice B. Rappaport & Howard B. Sprague. (1953). Ballistocardiography: II. The Normal Ballistocardiogram. Circulation, Vol.7(3), 321-328. DOI:10.1161/01.CIR.7.3.321   DOI
8 Richard S. Gubner, Manuel Rodstein & Harry E. Ungerleider. (1953). Ballistocardiography An Appraisal of Technic. Physiologic Principles, and Clinical Value, Circulation, Vol.7(3), 268-286. DOI:10.1161/01.CIR.7.2.268   DOI
9 Herbert R. Brown, JR., Marvin J. Hoffman & Vincent De Lalla, JR. (1950). Ballistocardiographic Findings in Patients with Symptoms of Angina Pectoris. Circulation, Vol.1(1), 132-140. DOI:10.1161/01.CIR.1.1.132   DOI
10 Hossein A. et al. (2019). Accurate Detection of Dobutamine-induced Haemodynamic Changes by Kino-Cardiography: A Randomised Double-Blind Placebo-Controlled Validation Study. Sci Rep 9, 10479. DOI:10.1038/s41598-019-46823-3   DOI
11 T. Kirjavainen, O. Polo, S. McNamara, K. Vaahtoranta & C.E. Sullivan. (1996). Respiratory challenge induces high frequency spiking on the static charge sensitive bed (SCSB). European Respiratory Journal, 1996, 9, 1810-1815. DOI:10.1183/09031936.96.09091810.   DOI
12 Health insurance review & Assessment Service. (2019.04.11). The prevention of hypertension and diabetes complications is best achieved by constant medication and regular management. Evaluation Management Office, Chronic Disease Assessment Department. http://www.hira.or.kr
13 B. J. Park. (2016.04.05). Diabetes with more fearful complications, rapidly increasing from the 40s. National Health Insurance Service, Ilsan Hospital. http://www.mohw.go.kr/react/al/sal0301vw.jsp?PAR_MENU_ID=04&MENU_ID=0403&page=1&CONT_SEQ=330894
14 T. Willemen et al. (2014). Characterization of the respiratory and heart beat signal from an air pressure-based ballistocardiographic setup. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, (pp.6298-6301), DOI: 10.1109/EMBC.2014.6945069.
15 D. C. Mack et al. (2006). A Passive and Portable System for Monitoring Heart Rate and Detecting Sleep Apnea and Arousals: Preliminary Validation. 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2., Arlington, VA, 2006, (pp. 51-54). DOI:10.1109/DDHH.2006.1624795.
16 Matthias Daniel Zink et al. (2015). Heartbeat Cycle Length Detection by a Ballistocardiographic Sensor in Atrial Fibrillation and Sinus Rhythm. BioMed Research International. Vol. 2015(840356), DOI: 10.1155/2015/840356
17 Murtuza Ahmed, Nirav P. Patel & Ilene Rosen. (2007). Portable monitors in the diagnosis of obstructive sleep apnea. Chest, vol. 132, no. 5, 1672-1677. DOI:10.1378/chest.06-2793   DOI
18 D. C. Mack et al. (2009). Sleep assessment using a passive ballistocardiography-based system: Preliminary validation. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, 2009, (pp. 4319-4322), DOI: 10.1109/IEMBS.2009.5333805.
19 M. M. Lee. (2010). Diabetes medication treatment. J. Kor. Soc. Health-Syst. Pharm., Vol.27, No.1, 72-85.
20 B. C. Choi. (2000). ISSUE & TREND:Type II Diabetes Mellitus. Korea Pharmaceutical Information Center. http://www.health.kr/
21 J. Kim. (2019). Death cause statistics for 2018. National Statistical Office. Social Statistics Bureau, Population Trends Department. http://kostat.go.kr
22 B. O. Kwon. (2019.10.10). promising smart healthcare market trends and entry strategies. KOTRA Release-19-015. https://news.kotra.or.kr/user/reports/kotranews/20/us rReportsView.do?reportsIdx=10985
23 S. J. Park. (2018.10.17). Diabetes surpassed 5 million, an increase of 0.7% in 2 years. MEDICAL Observer. http://www.monews.co.kr/news/articleView.html?idxno=120246
24 Yoshiyuki Shigetoh et al. (2009). Higher Heart Rate May Predispose to Obesity and Diabetes Mellitus:20-Year Prospective Study in a General Population. American Journal of Hypertension, Volume 22, Issue 2, 151-155. DOI:10.1038/ajh.2008.331
25 D. I. Kim et al. (2016). The association between resting heart rate and type 2 diabetes and hypertension in Korean adults. Heart, 2016,102(21), 1757-1762. DOI:10.1136/heartjnl-2015-309119   DOI
26 Huge C. Hemmings & Talmage D. Egan. (2013). Pharmacology and Physiology for Anesthesia: Foundations and Clinical Application.. china : ELSEVIER Saunders.
27 Walter B. Cannon. (1963). The Wisdom of the Body. United States: The Norton library.
28 Aubert, A.E., Seps, B. & Beckers, F. (2003). Heart Rate Variability in Athletes. Sports Med 33, 889-919. DOI:10.2165/00007256-200333120-00003   DOI
29 Makkonen, J. (2014). Blood pressure measurement utilizing MEMS pressure sensors. Master's thesis, School of Electrical Engineering, Espoo, Finland.
30 Koeppen, Bruce M. (2018). Berne and Levy Physiology. 7th edition. Philadelpia, PA : Elsevier.
31 David E. Mohrman & Lois Jane Heller. (2018). Cardiovascular Physiology. 9th edition, New york: McGraw-Hill Education.
32 Ryan Splittgerber. (2019). Snell's clinical neuroanatomy. 8th edition, China : Wolters Kluwer Health/Lippincott Williams &Wilkins.
33 Zhang, X., Shu, X. O., Xiang, Y. B., Yang, G., Li, H., Cai, H., Gao, Y. T., & Zheng, W. (2010). Resting heart rate and risk of type 2 diabetes in women. International journal of epidemiology, 39(3), 900-906. DOI:10.1093/ije/dyq068   DOI
34 K. S. Park. (2018). Diabetes Fact Sheet in Korea 2018. Korean Diabetes Association. Seoul. http://www.diabetes.or.kr
35 H. A. Park, J. A. Lee, J. Y. Kim, D. I. Kim & Justin Y. Jeon. (2015). The Relationship between Resting Heart Rate and Prevalence of Metabolic Syndrome and Type 2 Diabetes Mellitus in Korean Adults: The Fifth Korea National Health and Nutrition Examination Survey(2012). Korean J Obes 2015, 24(3), 166-174. DOI:10.7570/kjo.2015.24.3.166   DOI
36 H. I. Yang, H. C. Kim & Justin Y. Jeon. (2016). The association of resting heart rate with diabetes, hypertension, and metabolic syndrome in the Korean adult population: The fifth Korea National Health and Nutrition Examination Survey. Clinica Chimica Acta.Vol.455, 195-200. DOI:10.1016/j.cca.2016.01.006   DOI
37 H. J. Jeon, S. S. Kim, J.D. Seong & D. M. Baek. (2001). Determinants of Heart Rate Variation in the General Population. Korean Circulation J 2001,31(1), 107-113   DOI
38 Ribeiro IJS, Pereira R, Valenca Neto PF, Freire IV, Casotti CA & Reis MGD. (2017). Relationship between diabetes mellitus and heart rate variability in community-dwelling elders. Medicina (Kaunas). 2017,53(6), 375-379. DOI:10.1016/j.medici.2017.12.001   DOI
39 Jojo Moolayil. (2019). Learn Keras for Deep Neural Networks. Vancouver:Apress. DOI:10.1007/978-1-4842-4240-7
40 Mahdavi, Hadis & Ramos-Castro, Juan & Giovinazzo, Giuseppe & García-González, Miguel & Rosell, Xavier. (2012). A Wireless Under-Mattress Sensor System for Sleep Monitoring in People with Major Depression. Proceedings of the 9th IASTED International Conference on Biomedical Engineering. BioMed 2012. DOI:10.2316/P.2012.764-119.
41 Chris Albon. (2018). Machine Learning with Python Cookbook. First Edition. United States of America : O'Reilly Media.
42 Murata Electronics. (2020). Acceleration Sensor Modules SCA11H-A01-036 Data Sheet. https://www.murata.com/products/sensor/accel/sca10h_11h/sca11h
43 Murata Electronics. (2015.11.12). Ballistocardiographic sensors provide contact-less approach to measuring patient vital signs. https://www.murata.com/en-eu/products/info/sensor/accel/2015/1112
44 Ewing DJ, Neilson JM, Shapiro CM, Stewart JA & Reid W. (1991). Twenty four hour heart rate variability: effects of posture, sleep, and time of day in healthy controls and comparison with bedside tests of autonomic function in diabetic patients. Br Heart J 1991, 65, 239-244.   DOI
45 K. J. Park, H. J. Jeong. (2014). Assessing Methods of Heart Rate Variability. Annals of Clinical Neurophysiology, vol.16(2), 49-54. DOI:10.14253/kjcn.2014.16.2.4   DOI
46 Nitesh Pradhan, Geeta Rani, Vijaypal Singh Dhaka & Ramesh Chandra Poonia. (2020). Diabetes prediction using artificial neural network. DOI:10.1016/B978-0-12-819061-6.00014-8.
47 Emily B. Schroeder et al. (2005). Diabetes, Glucose, Insulin, and Heart Rate Variability. Diabetes Care 2005 Mar; 28(3): 668-674. . DOI:10.2337/diacare.28.3.668   DOI
48 Srivastava et al. (2019). Prediction of Diabetes Using Artificial Neural Network Approach: ICoEVCI 2018, India. DOI:10.1007/978-981-13-1642-5_59.
49 El Jerjawi, Nesreen & Abu-Naser, Samy. (2018). Diabetes Prediction Using Artificial Neural Network. Journal of Advanced Science, 124, 1-10.
50 C. W. Ahn. (2014). Clinical study for diagnostic efficacy of diabetic angiopathy using hemorheological measurement system (RheoScan). MOHW. Health Technology R&D Project. Yonsei University Industry-Academic Innovation Team
51 W. Kim, J.M. Woo & J. H. Chae. (2004). Use of Heart Rate Variability in Psychiatry. J Korean Neuropsychiatr Assoc Vol.44(2). 176-184.
52 S. K. Park & J. H. Kang. (2012). Comparison of the Electrocardiographic Characteristics of Junior Athletes and Untrained Subjects. Korean J Clin Lab Sci.2012,44(3), 136-141.
53 Vanderlei, Luiz & Pastre, Carlos & Hoshi, Rosangela & T D, Carvalho & Godoy, Moacir. (2009). Basic notions of heart rate variability and its clinical applicability. Brazilian Journal of Cardiovascular Surgery 24(2), 205-17. DOI: 10.1590/S0102-76382009000200018   DOI
54 K. J. Park & H. J. Jeong. (2014). Assessing Methods of Heart Rate Variability. Annals of Clinical Neurophysiology, vol.16(2), 49-54. DOI:10.14253/kjcn.2014.16.2.4   DOI
55 D.S. Han, N.R. Jung, D.W. Kim, Y.E Kim & C.H. Lee. (2007). Analysis of Korean Stress Conditions by Measuring Heart Rate Variability. Stress Research: .15(3).163-169.
56 J. W. Kang. (2002). Respiratory Physiology for Inhalation Calming. Korean Journal of Dental Anesthesiology. 2002(2). 7-14.   DOI
57 Anna M. Bianchi & Martin O. Mendez. (2013). Methods for heart rate variability analysis during sleep. Conf Proc IEEE Eng Med Biol Soc, 6579-6582. DOI: 10.1109/EMBC.2013.6611063
58 B. M. Choi & G.J. No. (2004). Heart Rate Variability (HRV). Intravenous Anesthesia 200, 8, 45-86.
59 Paalasmaa, J. (2014). Monitoring sleep with force sensor measurement. Doctoral dissertation. University of Helsinki, Finland
60 Maria Skytioti, Signe Sovik & Maja Elstad. (2018). Respiratory pump maintains cardiac strokevolume during hypovolemia in young. healthy volunteers. J Appl Physiol 12, 1319-1325. doi:10.1152/japplphysiol.01009.2017.