Application of Dynamic Thermogram for Diagnosis of Hypertension

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)

Abstract

Hypertension (high blood pressure) is blood pressure above than 140 over 90 mmHg (millimeters of mercury). Diagnosis of hypertension is made when one of the above readings is high. By the year 2025, the number of people living with hypertension is about 1.56 billion all over the world. This paper aims at developing a technique to diagnose hypertension noninvasively without using the cuff. In this approach, the dynamic infrared (IR) thermogram of selected body regions like hand (left and right) and neck (left and right) is obtained for about 60 s using IR thermal camera from 50 subjects (normal = 25 and age and sex-matched hypertensive = 25). The average temperature for every millisecond in these selected body regions is measured using ResearchIR software. Correlation is performed between the features extracted from dynamic thermogram and flow rate obtained from carotid Doppler ultrasound scan. The statistical analysis shows that highest correlation is obtained between the rate of temperature change (°C/min) in the neck left side with systolic pressure and hand left side with diastolic pressure (mmHg) (Pearson correlation r = −0.637 and 0.668 with p < 0.01, respectively). There also exists a linear correlation coefficient between neck right rate of change in temperature (°C/min) and right carotid artery flow rate (r = −0.358 with p < 0.05). An automated classifier using SVM network was designed with features extracted from dynamic thermogram for diagnosis of hypertension. The accuracy of the classifier was about 80% with sensitivity and specificity values 76.9 and 83.3%, respectively. The accuracy of the classifier when all the correlated features (n = 17) were included was 93% (sensitivity 90% and specificity 94%), whereas when highly correlated features (n = 15) were alone included, the sensitivity improved to 94% (accuracy 90% and specificity 85%).

Keywords

Dynamic thermal imaging SVM Hypertension Mean Energy Kurtosis Statistical parameters 

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Chekmenev SY, Farag AA, Essock EA (2007) Thermal imaging of the superficial temporal artery: an arterial pulse recovery model. In: 2007 IEEE conference on computer vision and pattern recognition. IEEEGoogle Scholar
  5. 5.
    Garbey M (2007) Contact-free measurement of cardiac pulse based on the analysis of thermal imagery. IEEE Trans Biomed Eng 54(8):1418–1426CrossRefGoogle Scholar
  6. 6.
    Vollmer M, Klaus-Peter M (2010) Infrared thermal imaging: fundamentals, research and applications. WileyGoogle Scholar
  7. 7.
    Ring EFJ, Ammer K (2012) Infrared thermal imaging in medicine. Physiol Meas 33(3):R33CrossRefGoogle Scholar
  8. 8.
    Hildebrandt C, Raschner C, Ammer K (2010) An overview of the recent application of medical infrared thermography in sports medicine in Austria. Sensors 10(5):4700–4715CrossRefGoogle Scholar
  9. 9.
    Qi H, Diakides NA (2003) Thermal infrared imaging in early breast cancer detection—a survey of recent research. In: Proceedings of the 25th annual international conference of the IEEE on engineering in medicine and biology society, 2003, vol 2. IEEEGoogle Scholar
  10. 10.
    Ring EFJ, Ammer K (2015) The technique of infrared imaging in medicine. Infrared Imaging. IOP PublishingGoogle Scholar
  11. 11.
  12. 12.
    Benetos A (1985) Pulsed Doppler: an evaluation of diameter, blood velocity and blood flow of the common carotid artery in patients with isolated unilateral stenosis of the internal carotid artery. Stroke 16(6):969–972CrossRefGoogle Scholar
  13. 13.
    Pytel K (2015) Anthropometric predictors and artificial neural networks in the diagnosis of hypertension. In: 2015 federated conference on computer science and information systems (FedCSIS). IEEEGoogle Scholar
  14. 14.
    Mead J, Whittenberger JL (1953) Physical properties of human lungs measured during spontaneous respiration. J Appl Physiol 5(12):779–796CrossRefGoogle Scholar
  15. 15.
    Mead J, Whittenberger JL (1954) Evaluation of airway interruption technique as a method for measuring pulmonary air-flow resistance. J Appl Physiol 6(7):408–416CrossRefGoogle Scholar
  16. 16.
    Hirsch JA, Bishop B (1981) Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. Am J Physiol Heart Circulatory Physiol 241(4):H620–H629CrossRefGoogle Scholar
  17. 17.
    Issa Z, Miller JM, Zipes DP (2012) Clinical arrhythmology and electrophysiology: a companion to Braunwald’s heart disease. Expert Consult: Online and Print. Elsevier Health SciencesGoogle Scholar
  18. 18.
    Braunwald E, Frahm CJ (1961) Studies on Starling’s law of the heart IV. Observations on the hemodynamic functions of the left atrium in man. Circulation 24(3):633–642CrossRefGoogle Scholar
  19. 19.
    Kitzman DW (1991) Exercise intolerance in patients with heart failure and preserved left ventricular systolic function: failure of the Frank-Starling mechanism. J Am Coll Cardiol 17(5):1065–1072CrossRefGoogle Scholar
  20. 20.
    Shiels HA, White E (2008) The Frank–Starling mechanism in vertebrate cardiac myocytes. J Exp Biol 211.13:2005–2013Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of BiomedicalSRM UniversityChennaiIndia

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