Screening for Cardiac and Cardiac-associated Pathology Using Single-channel Electrocardiogram

Overview

It is a prospective, controlled, single-center, observational, non-randomized study. The study is planned to include at least 4000 patients 18 years old and older in the training sample and 1000 patients over 18 years old in the test sample (the total number of patients is at least 5000 people). Patients will be included in the study if they have undergone a full examination (laboratory, clinical and instrumental), allowing for the verification or exclusion of cardiac and cardiac-associated pathology in accordance with current recommendations. During the course of the study, the authors of the work do not interfere with the above-mentioned scope of the examination, which is carried out on patients in accordance with clinical guidelines. All patients included in the study will undergo ECG recording in standard lead I for 1 minute twice, followed by spectral analysis of the obtained data, which will be stored at the remote monitoring center of Sechenov University without being linked to the personal data of patients. A spectral analysis of the electrocardiogram will be performed using a continuous wavelet transform. The result of this study will be the identification of ECG parameters that will correlate with cardiac and cardiac-associated pathology

The aim of the study: to create, evaluate the diagnostic efficiency and pilot application of a method for screening cardiac and cardiac-associated pathology based on the analysis of a single-channel electrocardiogram using elements of artificial intelligence. It is a prospective, controlled, single-center, observational, non-randomized study. The study is planned to include at least 4000 patients over 18 years old in the training sample and 1000 patients 18 years old and older in the test sample (the total number of patients is at least 5000 people). Patients will be included in the study if they have undergone a full examination (laboratory, clinical and instrumental), allowing for the verification or exclusion of cardiac and cardiac-associated pathology in accordance with current recommendations. To detect heart defects and heart failure: an expert echocardiography protocol, blood tests for cardiac-specific markers, and stress tests if indicated. To detect coronary artery disease: detection of significant coronary stenosis during coronary artery imaging: myocardial perfusion or determination of fractional coronary flow reserve, or stress echocardiography. For hypertension: repeated office blood pressure measurements, 24-hour blood pressure monitoring. For coronary heart disease: resting ECG and long-term Holter monitoring. For cardiac-associated pathology: blood tests: hemoglobin and red blood cell levels, glucose, glycated hemoglobin, oral glucose tolerance test, creatinine, uric acid levels, total cholesterol, low- and high-density lipoproteins, and triglycerides. Additional examination data will be taken into account if performed outside the protocol of this study. During the course of the study, the authors of the work do not interfere with the above-mentioned scope of the examination, which is carried out on patients in accordance with clinical guidelines. All patients included in the study will undergo ECG recording in standard lead I for 1 minute twice, followed by spectral analysis of the obtained data, which will be stored at the remote monitoring center of Sechenov University without being linked to the personal data of 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.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, as well as SPSS v.17.The correlation between various combinations of time, amplitude and frequency parameters of ECG and the presence of cardiac and cardiac-associated pathology 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 outcome of this study will be the development and validation of an algorithm for identifying various cardiac and cardiac-associated pathologies based on the analysis of single-channel ECG parameters. The development of a medical-use program will also be undertaken.Study endpoints: • Single-channel ECG parameters that significantly correlate with the presence of various cardiac and cardiac-related pathologies; • Sensitivity, specificity, and diagnostic accuracy of multivariate models for analyzing single-channel ECG data; • Diagnostic accuracy of the algorithm when tested on a test sample of patients.