Description

The aim of the study: to create and evaluate the diagnostic efficiency of a method for screening valvular heart disease based on data obtained from the analysis of a single-channel electrocardiogram.

It is a prospective, controlled, single-center, observational, non-randomized study. The study is planned to include at least 1000 patients over 18 years old in the training sample and 200 patients over 18 years old in the test sample (the total number of patients is at least 1200 people).

All subjects will undergo echocardiography with a comprehensive analysis of the function of the valves and other structures of the heart according to current recommendations by two independent experts.

Immediately after echocardiography, ECG registration will be performed in lead I for 1 minute with subsequent spectral analysis of the obtained data, which will be stored in the remote monitoring center of Sechenov University without reference to the personal data of the patients.

Single-channel ECG will be recorded using the portable single-lead ECG monitor CardioQvark. It is designed as an iPhone cover. It is registered with the Federal Service for Health Supervision on February 15, 2019. RZN No. 2019/8124.

If pathology is detected during echocardiography or ECG, the patient will be given a recommendation on the need to consult a cardiologist.

The patient's personal data (last name, first name, patronymic, date of birth, contact information) will not be transferred or taken into account. Each patient is assigned an individual number that is not associated with his/her personal data.

Then a spectral analysis of the electrocardiogram will be performed using a continuous wavelet transform, the principles of which are based on the Fourier transform.

The analysis involves the evaluation of the following parameters (the parameters listed below will be calculated as the median of the tact-cycle):

• TpTe - time from peak to end of the T-wave
• VAT - time from the beginning of the QRS to the R-peak
• QTc - corrected QT interval.
• QT / TQ - the ratio of QT length to TQ length (from the end of T to the beginning of the QRS of the next complex).
• QRS_E - the total energy of the QRS wave based on the wavelet transform
• T_E - T-wave total energy based on wavelet transform
• TP_E- energy of the main tooth of the T-wave based on the wavelet transform
• BETA, BETA_S - T-wave asymmetry coefficients (simple and smooth versions)
• BAD_T - flag of T-wave quality (whether expressed in the current lead
• QRS_D1_ons - energy of the leading edge of the R-wave (based on the "first derivative" wavelet transform)
• QRS_D1_offs - energy of the trailing edge of the R-wave (based on the "first derivative" wavelet transform)
• QRS_D2 - peak energy of the R-wave (based on the "second derivative" wavelet transform)
• QRS_Ei (i = 1,2,3,4) - QRS-wave energy in 4 frequency ranges (2-4-8-16-32 Hz) based on wavelet transform
• T_Ei (i = 1,2,3,4) - T-wave energy in 4 frequency ranges (2-4-6-8-10 Hz) based on wavelet transform
• HFQRS - the amplitude of the RF components of the QRS wave

Additionally used parameters:

• TpTe, VAT, QTc - are duplicated to control the correctness of the record processing (the value of the UCC should be approximately equal to the median of the tick-by-bar).
• QRSw - QRS width.
• RA, SA, TA - the amplitudes of the R, S, T-waves, respectively, are used to normalize the parameters listed above.

Statistical analysis and modeling will be performed using Python V3.8.8 and R V.4.0 programming languages, as well as SPSS v.17 software. The correlation between various combinations of time, amplitude and frequency parameters of ECG and the presence and degree of valvular heart defects will be analyzed. Certain parameters will be included in various multivariate analysis models: Lasso regression, Random Forest, Multilayer Perceptron, Support Vector Machine and Decision Tree. The model with the highest diagnostic accuracy will be selected, on which the algorithm will be tested.

The result of this study will be the development and testing of an algorithm for identifying valvular heart disease based on the analysis of single-channel ECG parameters. With the subsequent possibility of determining the degree of valvular heart disease.

Study endpoints:

• parameters of single-channel ECG that have a reliable correlation with the presence of valvular heart defects;
• sensitivity, specificity and diagnostic accuracy of multivariate models for analyzing single-channel electrocardiogram data;
• diagnostic accuracy of the algorithm when tested on a test sample of patients.