Abstract
Heart Rate Variability (HRV) designates the progressive variation in the intervals between successive heartbeats in the sinus rhythm. The HRV analysis is a non-invasive/effective tool to demonstrate the influence of the autonomic nervous system over the heart rhythm regulation. Recently, researches are interested with developing advanced software systems for HRV analysis. For cardiac disease patients’ investigation, nonlinear modeling and analysis can keep track in real time and foresee possible changes in circadian heart rate. The current work presents a novel created software system for HRV analysis based on 24-h Holter ECG signals of group of healthy and unhealthy subjects. The nonlinear analysis of heart intervals was performed with the implementation of original high performance algorithms and software to quantify the heart rate irregularity. The proposed software system achieved the short time ability for parametric estimation of patients’ cardiac status. It is based on long-term (24-h) Holter ECG signals with implementation of mathematical nonlinear methods. The experimental results established that the designed software system for analysis of 24-h Holter recordings is appropriate for diagnostic, forecast and prevention of the pathological cardiac statuses. The developed and implemented graphical representation and visualization approaches for the results can be stored in specialized data base.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task force of the European society of cardiology and the North American Society of pacing and electrophysiology. Eur. Heart J. 17, 354–381 (1996)
Dimitrova, M., Lahtchev, L., Lozanova, S., Roumenin, C.: Cloud computing approach to novel medical interface design. In: Handbook of Medical and Healthcare Technologies, pp. 245–265. Springer, New York (2013)
Ernst, G.: Heart Rate Variability. Springer, London (2014)
Acharya, U.-R., Suri, J.-S., Spaan, J.-E., Krishnan, S.-M.: Advances in Cardiac Signal Processing. Springer, Heidelberg (2007)
Gospodinova, E., Gospodinov, M., Dey, N., Domuschiev, I., Ashour, A., Sifaki-Pistolla, D.: Analysis of heart rate variability by applying nonlinear methods with different approaches for graphical representation of results. Int. J. Adv. Comput. Sci. App. 6(8), 39–45 (2015)
Peng, C.-K., Havlin, S., Stanley, H.-E., Goldberger, A.-L.: Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: an interdisciplinary. J. Nonlinear Sci. 5(1), 82–87 (1995)
Gospodinova, E.: Graphical methods for nonlinear analysis of ECG signals. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(12), 40–44 (2014)
Smith, R.L., Wathen, E.R., Abaci, P.C., Bergen, N.H.V., Law, I.H., Dick II, M.D., Connor, C., Dove, E.L.: Analyzing heart rate variability in infant using non-linear Poincarè techniques. Comput. Cardiol. 36, 673–876 (2009)
Dey, N., Das, A., Chaudhuri, S.S.: Wavelet based normal and abnormal heart sound identification using spectrogram analysis. Int. J. Comput. Sci. Eng. Technol. (IJCSET) 3(6), 186–192 (2012). ISSN 2229-3345
Dey, N., Mishra, G., Nandi, B., Pal, M., Das, A., Chaudhuri, S.-S.: Wavelet based watermarked normal and abnormal heart sound identification using spectrogram analysis. In: 2012 IEEE International Conference on Computational Intelligence & Computing Research (ICCIC), pp. 1–7 (2012)
Araki, T., Ikeda, N., Dey, N., Acharjee, S., Molinari, F., Saba, L., Suri, J.-S.: Shape-based approach for coronary calcium lesion volume measurement on intravascular ultrasound imaging and its association with carotid intima-media thickness. J. Ultrasound Med. 34(3), 469–482 (2015)
Aarthishree, S., Jayashree, M., Fathima, J.R.: A quick approach to detect epilepsy and seizure in brain. Int. J. Adv. Intell. Paradig. 8(4), 412–424 (2016)
Arokiaraj, S.P., Robert, L.: RDNAS: a simple DNA sequence squeezer using enhanced run length encoding. Int. J. Adv. Intell. Paradig. 8(4), 443–450 (2016)
Khan, S., Rahman, S.M., Tanim, M.F., Ahmed, F.: Factors influencing K means algorithm. Int. J. Comput. Syst. Eng. 1(4), 217–228 (2013)
Aguiar, Y.P., Maria de Fátima, Q.V., Galy, E., Santoni, C.: Accounting for individual and situation characteristics to understand the user behaviour when interacting with systems during critical situations. Int. J. Comput. Syst. Eng. (IJACI) 6(2), 29–55 (2014)
Bersch, S., Azzi, D., Khusainov, R., Achumba, I.E.: Artificial immune systems for anomaly detection in ambient assisted living applications. Int. J. Ambient Comput. Intell. (IJACI) 5(3), 1–15 (2013)
Kumar, S.U., Inbarani, H.H., Azar, A.T., Hassanien, A.E.: Identification of heart valve disease using bijective soft sets theory. Int. J. Rough Sets Data Anal. (IJRSDA) 1(2), 1–14 (2014)
Kanungo, D.P., Nayak, J., Naik, B., Behera, H.S.: Hybrid clustering using elitist teaching learning-based optimization: an improved hybrid approach of TLBO. Int. J. Rough Sets Data Anal. (IJRSDA) 3(1), 1–19 (2016)
Hudlicka, E.: Guidelines for designing computational models of emotions. Int. J. Synth. Emot. (IJSE) 2(1), 26–79 (2011)
Shivakumar, G., Vijaya, P.A.: Analysis of human emotions using galvanic skin response and finger tip temperature. Int. J. Synth. Emot. (IJSE) 2(1), 15–25 (2011)
Hore, S., Chakroborty, S., Ashour, A.S., Dey, N., Ashour, A.S., Sifaki-Pistolla, D., Bhattacharya, T., Chaudhuri, S.R.: Finding contours of hippocampus brain cell using microscopic image analysis. J. Adv. Micros. Res. 10(2), 93–103 (2015)
Dey, N., Das, A., Chaudhuri, S.S.: Wavelet based normal and abnormal heart sound identification using spectrogram analysis. arXiv preprint arXiv:1209.1224 (2012)
Roy, P., Goswami, S., Chakraborty, S., Azar, A.T., Dey, N.: Image segmentation using rough set theory: a review. Int. J. Rough Sets Data Anal. (IJRSDA) 1(2), 62–74 (2014)
Dey, A., Bhattacha, D.K., Tibarewala, D.N., Dey, N., Ashour, A.S., Le, D.N., Gospodinova, E., Gospodinov, M.: Chinese-chi and Kundalini yoga meditations effects on the autonomic nervous system: comparative study. Int. J. Interact. Multimedia Artif. Intell. 3(7), 87–95 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Gospodinova, E. et al. (2018). Specialized Software System for Heart Rate Variability Analysis: An Implementation of Nonlinear Graphical Methods. In: Balas, V., Jain, L., Balas, M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-62521-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-62521-8_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62520-1
Online ISBN: 978-3-319-62521-8
eBook Packages: EngineeringEngineering (R0)