PRINCIPLES OF FORMING A DATABASE OF ECG SIGNALS AND THEIR FRAGMENTS FOR EVALUATING THE CHARACTERISTICS OF WEARABLE DIGITAL ON-LINE MONITORS
Keywords:
Electrocardiography, wavelet transform, discrete wavelet transform, continuous wavelet transform, MIT Physionet
Abstract
Electrocardiographic (ECG) signals have several properties that can greatly complement
the existing, and more established biometric modalities. Some of the most prominent properties
are the fact that the signals can be continuously acquired using minimally intrusive setups, are
not prone to produce latent patterns, and provide intrinsic liveliness detection, opening new
opportunities within the area of biometric systems development. The paper proposes methods
for forming a database of ECG signals and their fragments for assessing the characteristics of
portable digital on-line monitors. In the method of discrete wavelet transform (DWT) it allows
to determine with high accuracy the presence of RR-intervals and their segments. This makes it
possible to use this method for classifying ECG signals, forming a database of signal data records
and generating test signals designed to assess the characteristics of wearable digital
ONLINE monitors. This article presents an improved and more efficient algorithm by Discrete
Wavelet Transform for generating electrocardiogram (ECG) signals from the PhysioBank archive
to test the performance of an ECG machine.
References
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poverkhnosti tela u sobak [Detection of delayed ventricular activation on the body surface in
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11. Simson M.B. Ispol'zovanie signala terminal'nogo kompleksa QRS dlya identifikatsii patsientov s
zheludochkovoy takhikardiey posle infarkta miokarda [Using the signal of the terminal complex QRS
to identify patients with ventricular tachycardia after myocardial infarction], 1981, pp. 230-244.
12. Ebert Kh. Legkaya EKG. Interpretatsiya differentsial'nykh diagnozov [Light ECG. Interpretation
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vozdeystviya fil'tra vysokoy chastoty i fil'tra nizkoy chastoty na kachestvo registratsii
mikropotentsialov na elektrokardiogramme [Results of modeling the effect of a high-frequency
filter and a low-frequency filter on the quality of recording micro-potentials on an electrocardiogram],
2013, pp. 318.
14. Rangayyan R.M. Analiz biomeditsinskikh signalov. Prakticheskiy podkhod [Analysis of biomedical
signals. Practical approach], 2007, pp. 438-444.
15. Smolentsev N.K. Osnovy teorii veyvletov. Veyvlety v MatLab [Fundamentals of wavelet theory.
Wavelets in MatLab], 2005, pp. 304-310.
16. Drozdov D.V. Vliyanie fil'tratsii na diagnosticheskie svoystva biosignalov [Influence of filtering
on diagnostic properties of biosignals], Funktsional'naya diagnostika [Functional diagnostics],
2011, pp. 75-78.
17. Dubrovin V.I., Tverdokhleb Yu.V., Kharchenko V.V. Avtomatizirovannaya sistema analiza i
interpretatsii EKG [Automated system of analysis and interpretation of ECG], Radioelektronika,
informatika, upravlenie [Radioelectronics, informatics, managemen], 2014, pp. 148-160.
18. Cayt «PhysioBank Archive Index» [The Website "PhysioBank Archive Index"]. Available at:
http://www.physionet.org/physiobank/database/.
19. Gol'denberg L.M. Spravochnik. TSifrovaya obrabotka signalov [Guide. Digital signal
processing]. Moscow: Radio i svyaz', 1985, pp. 310-314.
20. Gaydyshev I. Analiz i obrabotka [Analysis and processing]. Saint Petersburg, 2006, pp. 750-754.
21. Monakhova O.A. Registratsiya karrdiointervalogrammy po veyvletnomu spektru elektrokardiogrammy
[Check cardiointervalogram in the wavelet spectrum of the electrocardiogram].
Saratov, 2009, pp. 10-17.
22. Monakhova O.A. Issledovanie tonkoy struktury elektrokardiograficheskogo signala metodami
veyvletnogo analiza [Investigation of the fine structure of the electrocardiographic signal by
wavelet analysis methods]. Saratov, 2009, pp. 119-132.
2. Tu Dzh., Gonsales R. Printsipy raspoznavaniya obrazov [Principles of image recognition]:
translated by I.P. Gurevicha, ed. by Yu.I. Zhuravleva. Moscow: Mir, 1978, pp. 410-415.
3. Beyts R.A., Khilton M.F., Godfri K.R. i Chappell M.Dzh. Sravnenie metodov garmonicheskogo
veyvlet-analiza variabel'nosti serdechnogo ritma [Comparison of methods of harmonic wavelet
analysis of heart rate variability], 1998, pp. 278-299.
4. Erchelebi E. Udalenie shuma signalov elektrokardiogrammy s ispol'zovaniem diskretnogo
veyvlet-preobrazovaniya na osnove pod"ema [Noise removal of electrocardiogram signals using
discrete lift-based wavelet transform], 2004, pp. 479-493.
5. Pin'omark A., Limsakul K., Pkhukpattaranont P. Optimal'nye veyvlet-funktsii v veyvletshumopodavlenii
dlya mnogofunktsional'nogo mioelektricheskogo upravleniya [Optimal
wavelet functions in wavelet noise reduction for multifunctional myoelectric control], ECTI
Transactions po elektrotekhnike, elektronike i kommunikatsiyam [ECTI Transactions in Electrical
engineering, Electronics and Communications]. ECTI, 2010, pp. 43-52.
6. Chuakri S. Veyvlet-shumopodavlenie signala elektrokardiogrammy na osnove otsenki
iskazhennogo shuma [Wavelet noise reduction of the electrocardiogram signal based on the assessment
of distorted noise], Komp'yutery v kardiologii [Computers in Cardiology], 2005, IEEE.
7. Khaberl R., Dzhige G., Pulter R., Steynbek G. Spektral'noe kartirovanie elektrokardiogrammy
s preobrazovaniem Fur'e dlya identifikatsii patsientov s ustoychivoy zheludochkovoy
takhikardiey i ishemicheskoy bolezn'yu serdtsa [Spectral mapping of a fourier transform electrocardiogram
for the identification of patients with sustained ventricular tachycardia and ischemic
heart disease], pp. 310-326.
8. Meste O., Riks Kh., Kaminal P., Takor N. Kharakteristika pozdnikh potentsialov zheludochkov v
chastotno-vremennoy oblasti s pomoshch'yu veyvlet-preobrazovaniya [Characterization of late ventricular
potentials in the time-frequency domain using a wavelet transform], Tranzaktsiya IEEE po
biomeditsinskoy inzhenerii [IEEE transaction on biomedical engineering], 1994, pp. 625-633.
9. Morlet D., Kuderk Dzh. P., Tubul P., Rubel' P. Veyvlet-analiz EKG vysokogo razresheniya u
patsientov posle infarkta: rol' osnovnogo veyvleta i analiziruemogo otvedeniya [Highresolution
ECG wavelet analysis in patients after a heart attack: the role of the main wavelet
and the analyzed lead], 1995, pp. 308-330.
10. Simson M.B., Eyler D., Maykel'son E. Obnaruzhenie zaderzhki aktivatsii zheludochkov na
poverkhnosti tela u sobak [Detection of delayed ventricular activation on the body surface in
dogs], 1981, pp. 362-372.
11. Simson M.B. Ispol'zovanie signala terminal'nogo kompleksa QRS dlya identifikatsii patsientov s
zheludochkovoy takhikardiey posle infarkta miokarda [Using the signal of the terminal complex QRS
to identify patients with ventricular tachycardia after myocardial infarction], 1981, pp. 230-244.
12. Ebert Kh. Legkaya EKG. Interpretatsiya differentsial'nykh diagnozov [Light ECG. Interpretation
of differential diagnoses]. Shtutgart; N'yu-York: Time, 2005, pp. 138-140.
13. Avdeeva D.K., Kazakov V.Yu., Natalinova N.M., Ivanov M.L. Rezul'taty modelirovaniya
vozdeystviya fil'tra vysokoy chastoty i fil'tra nizkoy chastoty na kachestvo registratsii
mikropotentsialov na elektrokardiogramme [Results of modeling the effect of a high-frequency
filter and a low-frequency filter on the quality of recording micro-potentials on an electrocardiogram],
2013, pp. 318.
14. Rangayyan R.M. Analiz biomeditsinskikh signalov. Prakticheskiy podkhod [Analysis of biomedical
signals. Practical approach], 2007, pp. 438-444.
15. Smolentsev N.K. Osnovy teorii veyvletov. Veyvlety v MatLab [Fundamentals of wavelet theory.
Wavelets in MatLab], 2005, pp. 304-310.
16. Drozdov D.V. Vliyanie fil'tratsii na diagnosticheskie svoystva biosignalov [Influence of filtering
on diagnostic properties of biosignals], Funktsional'naya diagnostika [Functional diagnostics],
2011, pp. 75-78.
17. Dubrovin V.I., Tverdokhleb Yu.V., Kharchenko V.V. Avtomatizirovannaya sistema analiza i
interpretatsii EKG [Automated system of analysis and interpretation of ECG], Radioelektronika,
informatika, upravlenie [Radioelectronics, informatics, managemen], 2014, pp. 148-160.
18. Cayt «PhysioBank Archive Index» [The Website "PhysioBank Archive Index"]. Available at:
http://www.physionet.org/physiobank/database/.
19. Gol'denberg L.M. Spravochnik. TSifrovaya obrabotka signalov [Guide. Digital signal
processing]. Moscow: Radio i svyaz', 1985, pp. 310-314.
20. Gaydyshev I. Analiz i obrabotka [Analysis and processing]. Saint Petersburg, 2006, pp. 750-754.
21. Monakhova O.A. Registratsiya karrdiointervalogrammy po veyvletnomu spektru elektrokardiogrammy
[Check cardiointervalogram in the wavelet spectrum of the electrocardiogram].
Saratov, 2009, pp. 10-17.
22. Monakhova O.A. Issledovanie tonkoy struktury elektrokardiograficheskogo signala metodami
veyvletnogo analiza [Investigation of the fine structure of the electrocardiographic signal by
wavelet analysis methods]. Saratov, 2009, pp. 119-132.
Published
2021-01-19
Issue
Section
SECTION I. INFORMATION PROCESSING ALGORITHMS
