Abstract
Cardiac health issues are severe and cause maximum death according to the survey done by “World Health Organization” (WHO). Cardiac diseases are caused due to family history, living style, diabetes, etc. Diagnosis of cardiac health prior to pathological conditions is highly important. Heart Rate Variability (HRV) is the technique used to study the cardiac abnormalities, which are related to fluctuation in the sympathetic and parasympathetic activities. In this paper, we compare the time domain, frequency domain and nonlinear parameters of heart rate variability for 73 subjects. Our results show that HRV parameters are high for normal subjects compared to diabetic subjects and lowest for cardiac subjects. The results are validated by diagnosis done through clinical processes. Thus non-invasive ECG and HRV techniques help to diagnose the subject before it causes the cardiac arrest.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ernst, G.: History of heart rate variability. Heart Rate Variability, pp. 3–8. Springer, London (2014). https://doi.org/10.1007/978-1-4471-4309-3_1
Acharya, R., Faust, O., Kadri, N., Suri, J., Yu, W.: Automated identification of normal and diabetes heart rate signals using non-linear measures. Int. J. Comput. Biol. Med. (2013)
Golińska, A.: Poincare plots in analysis of selected biomedical signals. Stud. Logic Grammar Rhetoric 35(1), 117–127. https://doi.org/10.2478/slgr-2013-0031
Joshi, M., Desai, K., Mennon, M.: Poincare plot used as confirmative tool in diagnosis of lv diastolic dysfunction for diabetic patients, with and without hypertension. Int. J. Sci. Eng. Res. 4(10) (2013). ISSN 2229–5518
Seyd, P., Ahamed, V., Jacob, J., Joseph, P.: Time and frequency domain analysis of heart rate variability and their correlations in diabetes mellitus, world academy of science, engineering and technology international. J. Med. Health Biomed. Bioeng. Pharm. Eng. 2(3) (2008)
https://www.mathworks.com/matlabcentral/fileexchange/26546-approximate-entropy
Fausta, O., Acharya, R., Molinarib, F., Chattopadhyayc, S., Toshiyo, T.: Linear and non-linear analysis of cardiac health in diabetic subjects. Biomed. Signal Process. Control 7, 295–302 (2012)
Corrales, M., Torres, B., Esquivel, A., Salazar, M., Orellana, J.: Normal valueitionsheart rate variability at rest in a young, healthy and active Mexican population. Health 4, 377–385 (2012)
Tarvainen, M., Niskanen, J., Lipponen, J., Ranta-aho, P., Karjalainen, P.: Kubios HRV - heart rate variability analysis software. Comput. Methods Programs Biomed. (2013). https://doi.org/10.1016/j.cmpb.2013.07.024
Stein, P., Domitrovich, P., Kleiger, R.: Including patients with diabetes mellitus or coronary artery bypass grafting decreases the association between heart rate variability and mortality after myocardial infarction. Am. Heart J. 147(2) (2004)
Shirole, U., Joshi, M., Desai, K., Bagul, P.: Cardiac autonomous function assessment in congestive heart failure using HRV analysis. Int. J. Scientic Eng. Res. 8(11) (2017). ISSN 2229-5518
Acharya, R., Ghista, D., Subbhuraam, V.: Sudden cardiac death prediction based on nonlinear heart rate variability features and SCD index. Appl. Soft Comput. (2016)
Soydana, N., Bretzel, R., Fischer, B., Wagenlehnerb, F., Pilatz, A., Linna, T.: Reduced capacity of heart rate regulation in response to mild hypoglycemia induced by glibenclamide and physical exercise in type 2 diabetes. Metab. Clin. Exp. 62, 717–724 (2013)
Joshi, M., Desai, K., Menon, M.: ECG signal analysis used as confirmative tool in quick diagnosis of Myocardial Infarction. Int. J. Sci. Eng. Res. 3(3) (2012). ISSN 2229–5518
Garcıaa, C., Oterob, A., Vilac, X., Marqueza, D.: A new algorithm for wavelet-based heart rate variability analysis. Biomed. Signal Process. Control 8(6), 542–550 (2013)
Joshi, M., Desai, K., Menon, M.: Correlation between Heart Rate Variability and Left Ventricular Ejection Fraction (LVEF) for diabetics and diabetics with hypertension. J. Bioeng. Biomed. Sci. ISSN, 2155–9538 (2015)
Montano, N.: Heart rate variability explored in the frequency domain: a tool to investigate the link between heart and behaviour. Neurosci. Biobehav. Rev. 33, 71–80 (2009)
Cornforth, D., Jelinek, H.: Detection of congestive heart failure using renyi entropy. Comput. Cardiol. 43, 667–669 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shirole, U., Joshi, M., Bagul, P. (2019). Linear and Nonlinear Analysis of Cardiac and Diabetic Subjects. In: Akoglu, L., Ferrara, E., Deivamani, M., Baeza-Yates, R., Yogesh, P. (eds) Advances in Data Science. ICIIT 2018. Communications in Computer and Information Science, vol 941. Springer, Singapore. https://doi.org/10.1007/978-981-13-3582-2_10
Download citation
DOI: https://doi.org/10.1007/978-981-13-3582-2_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3581-5
Online ISBN: 978-981-13-3582-2
eBook Packages: Computer ScienceComputer Science (R0)