Article 6118

Title of the article

ANALYSIS OF PATIENTS ECG SIGNALS WITH BORDER MENTAL DISORDERS BASED ON HHT 

Authors

Tychkov Aleksandr Yur'evich, Candidate of engineering sciences, deputy director of scientific research institute of basic and applied research, Penza State University (40 Krasnaya street, Penza, Russia), tychkov-a@mail.ru

Index UDK

004.9

DOI

10.21685/2072-3059-2018-1-6

Abstract

Background. Diagnosis of mental disorders is carried out within the framework of medical standards and norms. Assessment of the patient's condition with borderline mental disorders is based on the results of the psychometric test (test methods). The purpose is of the presented work is introduction into clinical practice of methods of a physician for assessment of a person's mental health state through the analysis of ECG signals.
Materials and methods. For the analysis of ECG signals, the Hilbert-Huang transform and data processing in the energy-frequency-time coordinate system are used. To study signals and determining of new parameters of a person's mentalhealth state, a patented verified ECG signals database of patients with borderline mental disorders is used.
Results. An algorithm for determining borderline mental disorders on the ECG signal is developed, which allows to determine the period of occurrence of psychotraumatic situation on the amplitude-time and energy components of the signal obtained as a result of the Hilbert-Huang transform.
Conclusions. A new original algorithm for determining borderline mental disorders on the ECG signal is developed and investigated, which allows to assess person's mental health state according to the results of ECG processing with high accuracy.

Key words

electrocardiogram, Hilbert-Huang transform, markers, borderline mental disorders

Download PDF
References

1. Manea M., Comsa M. et al. Med Life. 2015, no. 8, pp. 66–71.
2. Purushothaman S., Salmani D. et al. SciBiol Med. 2014, no. 5, pp. 434–436.
3. Tychkov A. Yu., Ageykin A. V., Alimuradov A. K., Kalistratov V. B., Mitroshina S. Yu. Psikhicheskoe zdorov'e [Mental health]. 2017, vol. 15, no. 5, pp. 69–75.
4. Tychkov A. Yu. Izvestiya vysshikh uchebnykh zavedeniy. Povolzhskiy region. Tekhnicheskie nauki [University proceedings. Volga region. Engineering sciences]. 2015, no. 1 (33), pp. 92–98.
5. Takeuchi S., Nagatani K. et al. ClinNeurosci. 2015, no. 22, pp. 1959–1962.
6. Kaplan A. Ya. Zhurnal vysshey nervnoy deyatel'nosti [Journal of higher nervous activity]. 1999, no. 48, pp. 345–350.
7. Agrafioti F. Ph.D. Dissertation. Toronto: University of Toronto, 2011, 172 p.
8. Sornmo L. Medical and Biological Engineering and Computing. 1993, pp. 503–508.
9. Konareva I. N. Biology of chemistry. 2011, no. 24, pp. 161–168.
10. Rangayyan R. M. Analiz biomeditsinskikh signalov. Prakticheskiy podkhod: per. s angl. [Analysis of biomedical signals]. Moscow: Fizmatlit, 2007, 440 p.
11. Huang N. The Hilbert-Huang transform and its applications. Singapore: World scientific publishing, 2005, 526 p.
12. Huang N. An Introduction to Hilbert-Huang transform: a plea for adaptive data analysis. Research center for adaptive data analysis. 2007, 257 p.
13. Kuzmin A. V., Tychkov A. Y., Alimuradov A. K. International journal of applied engineering research. 2015, no. 4, pp. 8527–8531.
14. Tychkov A. Y. Biomedical Engineering. 2015, no. 49, pp. 37–41.
15. Bodin O. N., Churakov P. P. et al. Measurement Techniques. 2011, no. 4, pp. 41–44.
16. Alimuradov A. K., Frantsuzov M. V. et al. Measurement Techniques. 2015, no. 58, pp. 965–969.
17. Benitez D. Comput. Biol. 2002, no. 3, pp. 399–406.
18. Boudraa A. O., Cexus J. C. World academy of science, engineering and technology. 2002, 394 p.

 

Дата создания: 13.06.2018 14:03
Дата обновления: 03.07.2018 16:18