Arterial HypertensionNCT07396792
Screening for Cardiac and Cardiac-associated Pathology Using Single-channel Electrocardiogram
An observational study of Arterial Hypertension and Heart Rhythm Disorders, run by I.M. Sechenov First Moscow State Medical University.
Trial summary
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). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Arterial Hypertension, Heart Rhythm Disorders, Valvular Heart Diseases
- How many people
- 4,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2026to 13 May 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: All patients with or without cardiac and cardiac-associated pathologies over 18 years old
Who may be able to join
- The presence of written informed consent of the patient to participate in the study
- Availability of examination data allowing for the verification or exclusion of cardiac and cardiac-associated pathology
- Age 18 years old and older
- Non-inclusion criteria:
- Patients with an implanted permanent pacemaker;
- ECG changes that prevent spectral analysis;
Show all 9Show fewer
- Conditions that may impair the quality of the ECG recording (Parkinson's disease, essential tremor, etc.);
- Conditions that make ECG recording in lead I impossible (congenital anomalies of the upper limbs, traumatic amputation of the upper limbs).
- Lack of written informed consent from the patient to participate in the study.
Who cannot join
- Poor quality of the ECG recording on a single-channel ECG monitor
- Insufficient examination data to verify or exclude cardiac or cardiac-associated pathology;
- Patient's unwillingness to continue participating in the study for any reason.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Screening for Cardiac and Cardiac-associated Pathology Using Single-channel Electrocardiogram Analyzed With Machine Learning Models
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
Read the detailed description
The aim of the study is to develop, evaluate the diagnostic efficacy, and pilot a screening method for cardiac and cardiac-related pathologies based on single-channel electrocardiogram analysis using artificial intelligence. To achieve this goal, a prospective, controlled, single-center, observational, non-randomized study will be conducted involving at least 4,000 patients meeting the inclusion criteria. All patients will undergo an expert echocardiography protocol with a comprehensive assessment of heart valve hemodynamics, systolic and diastolic myocardial function, along with standard measurements of the heart chambers and great vessels. To detect or exclude coronary artery disease: detection of significant coronary stenosis during coronary imaging: myocardial perfusion or determination of fractional coronary flow reserve, or stress echocardiography will be performed. For arterial hypertension: repeated blood pressure measurements in the office, 24-hour blood pressure monitoring will be performed. For cardiac arrhythmias: resting ECG and long-term Holter monitoring will be performed. For any cardiovascular pathology: blood tests: hemoglobin and red blood cell levels, glucose, glycated hemoglobin, oral glucose tolerance test, creatinine, uric acid, total cholesterol, low-density and high-density lipoproteins, and triglycerides will be performed. Additional testing data will be taken into account if conducted outside the protocol of this study. During the study, the authors will not interfere with the aforementioned scope of testing, which is performed on patients in accordance with clinical guidelines. All patients included in the study will undergo two ECG recordings in standard lead I for 1 minute, followed by spectral analysis of the obtained data, which will be stored at the remote monitoring center of Sechenov University without linking to the patients' personal data. A single-channel ECG will be recorded using a portable single-lead CardioQVARK ECG recorder. It was registered with the Federal Service for Surveillance in Healthcare on February 15, 2019. Number RU MI No. RZN 2024/22 855. 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.
- Conditions
- Arterial Hypertension, Heart Rhythm Disorders, Valvular Heart Diseases, Heart Failure, Diabetes Mellitus, Chronic Kidney Disease (Stage 3-4)
- Keywords
- Screening, Electrocardiogramm, single-channel electrocardiogram, machine learning models, Coronary artery disease, diabetes mellitus, metabolic syndrome, anemia, hypercholesterolemia, arterial hypertension
- Medical subject headings
- Hypertension, Arrhythmias, Cardiac, Heart Valve Diseases, Heart Failure, Diabetes Mellitus, Renal Insufficiency, Chronic, Metabolic Syndrome, Coronary Artery Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Training sample | 2500 of patients 18 years old and older with and without cardiac and cardiac-associated pathology confirmed by the results of full examination (laboratory, clinical and instrumental) and by results of the spectral analysis of electrocardiogram (the parameters listed below will be calculated as the median of the tact-cycle: TpTe, VAT, QTc, QT / TQ, QRS_E, T_E, TP_E, BETA, BETA_S, BAD_T, QRS_D1_ons, QRS_D1_offs, QRS_D2, QRS_Ei (i = 1,2,3,4), T_Ei (i= 1,2,3,4), HFQRS, QRSw, RA, SA, TA and others). | |
| Test sample | 1500 of patients 18 years old and older with and without cardiac and cardiac-associated pathology confirmed by the results of full examination (laboratory, clinical and instrumental) and by results of the spectral analysis of electrocardiogram (the parameters listed below will be calculated as the median of the tact-cycle: TpTe, VAT, QTc, QT / TQ, QRS_E, T_E, TP_E, BETA, BETA_S, BAD_T, QRS_D1_ons, QRS_D1_offs, QRS_D2, QRS_Ei (i = 1,2,3,4), T_Ei (i= 1,2,3,4), HFQRS, QRSw, RA, SA, TA and others). |
What is being measured
The primary outcome is the main question the study is designed to answer.
Parameters of single-channel ECG that significantly correlate with the presence of various cardiac and cardiac-associated pathologies
comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor
Measured: through study completion, an average of 2 years
Determination of sensitivity of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data
comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor
Measured: through study completion, an average of 2 years
Determination of specificity of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data
comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor
Measured: through study completion, an average of 2 years
Determination of diagnostic accuracy of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data
comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor
Measured: through study completion, an average of 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Feb 2026
- Study start1 Apr 2026 (estimated)
- Primary completion1 Mar 2028 (estimated)
- Study completion13 May 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Arterial HypertensionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- I.M. Sechenov First Moscow State Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07396792
- Sponsor's protocol ID
- 7747-211021
- Status last verified
- Apr 2026
- First submitted
- 2 Feb 2026
- Last updated
- 16 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: It is not possible to provide documentation due to the prohibition received from the local ethics committee
Publications
A screening method for predicting left ventricular dysfunction based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms / N. Kuznetsova, Zh. Sagirova, A. Suvorov [et al.] // Biomedical Signal Processing and Control. - 2023. - Vol. 86. - P. 105219. - DOI 10.1016/j.bspc.2023.105219. - EDN APQSQF.
Complex automated remote system for assessing hemodynamic parameters when analyzing the native signal of a single-channel ECG and pulse wave using machine learning techniques / N. O. Kuznetsova, Zh. N. Sagirova, E. A. Sultygova [et al.] // Russian Journal of Cardiology. - 2023. - T. 28, No. S7. - pp. 41-42. - EDN LZGDKG.
Abdulraheem LW, Al-Dwa B, Shchekochikhin D, Gognieva D, Chomakhidze P, Kuznetsova N, Kopylov P, Bestavashvilli AA. A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation. Curr Cardiol Rev. 2025;21(1):e310724232529. doi: 10.2174/011573403X293703240715104503.
PubMed 39092649Mesitskaya DF, Fashafsha ZZA, Poltavskaya MG, Andreev DA, Levshina AR, Sultygova EA, Gognieva D, Chomakhidze P, Kuznetsova N, Suvorov A, Marina I S, Poddubskaya E, Novikova A, Bykova A, Kopylov P. A single-lead ECG based cardiotoxicity detection in patients on polychemotherapy. Int J Cardiol Heart Vasc. 2024 Jan 20;50:101336. doi: 10.1016/j.ijcha.2024.101336. eCollection 2024 Feb.
PubMed 38304727Kuznetsova N.O., Alekseeva A.M., Mamedzade F.E., Sedov V.P., Kopylov F.Yu., Syrkin A.L., Chomakhidze P.Sh. Screening for heart defects when analyzing an electrocardiogram using machine learning methods (literature review) // Bulletin of new medical technologies. Electronic edition. 2025. No. 1. Publication 1-6. DOI: 10.24412/2075-4094-2025-1-1-6. EDN KNFSNS
Kuznetsova N.O., Nartova A.A., Kurbanalieva N.K., Adueva D.Sh., Chursina E.Yu., Zhvania R.E., Ustinova D.I., Kostikova A.S., Kazakova M.V., Tarnaeva L.A., Chomakhidze P.Sh., Kopylov F.Yu. Results of screening for heart rhythm disturbances using a single-channel electrocardiogram without the participation of medical personnel // Bulletin of new medical technologies. 2025. No. 1. P. 56-60. DOI: 10.24412/1609-2163-2025-1-56-60. EDN YWJLFH.
Study at a glance
From the registry record, last updated by the sponsor on 16 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Screening for Cardiac and Cardiac-associated Pathology Using Single-channel Electrocardiogram Analyzed With Machine Learning Models
- ClinicalTrials.gov ID
- NCT07396792
- Acronym / sponsor ID
- 7747-211021
- Conditions
- Arterial Hypertension, Heart Rhythm Disorders, Valvular Heart Diseases, Heart Failure, Diabetes Mellitus, Chronic Kidney Disease (Stage 3-4)
- Keywords
- Screening, Electrocardiogramm, single-channel electrocardiogram, machine learning models, Coronary artery disease, diabetes mellitus, metabolic syndrome, anemia
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- I.M. Sechenov First Moscow State Medical UniversityUniversity, hospital or other
- Study start
- 1 Apr 2026 (estimated)
- Primary completion
- 1 Mar 2028 (estimated)
- Study completion
- 13 May 2028 (estimated)
- First posted
- 9 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Detailed description
The aim of the study is to develop, evaluate the diagnostic efficacy, and pilot a screening method for cardiac and cardiac-related pathologies based on single-channel electrocardiogram analysis using artificial intelligence. To achieve this goal, a prospective, controlled, single-center, observational, non-randomized study will be conducted involving at least 4,000 patients meeting the inclusion criteria. All patients will undergo an expert echocardiography protocol with a comprehensive assessment of heart valve hemodynamics, systolic and diastolic myocardial function, along with standard measurements of the heart chambers and great vessels. To detect or exclude coronary artery disease: detection of significant coronary stenosis during coronary imaging: myocardial perfusion or determination of fractional coronary flow reserve, or stress echocardiography will be performed. For arterial hypertension: repeated blood pressure measurements in the office, 24-hour blood pressure monitoring will be performed. For cardiac arrhythmias: resting ECG and long-term Holter monitoring will be performed. For any cardiovascular pathology: blood tests: hemoglobin and red blood cell levels, glucose, glycated hemoglobin, oral glucose tolerance test, creatinine, uric acid, total cholesterol, low-density and high-density lipoproteins, and triglycerides will be performed. Additional testing data will be taken into account if conducted outside the protocol of this study. During the study, the authors will not interfere with the aforementioned scope of testing, which is performed on patients in accordance with clinical guidelines. All patients included in the study will undergo two ECG recordings in standard lead I for 1 minute, followed by spectral analysis of the obtained data, which will be stored at the remote monitoring center of Sechenov University without linking to the patients' personal data. A single-channel ECG will be recorded using a portable single-lead CardioQVARK ECG recorder. It was registered with the Federal Service for Surveillance in Healthcare on February 15, 2019. Number RU MI No. RZN 2024/22 855. 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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Training sample | – | –2500 of patients 18 years old and older with and without cardiac and cardiac-associated pathology confirmed by the results of full examination (laboratory, clinical and instrumental) and by results of the spectral analysis of electrocardiogram (the parameters listed below will be calculated as the median of the tact-cycle: TpTe, VAT, QTc, QT / TQ, QRS_E, T_E, TP_E, BETA, BETA_S, BAD_T, QRS_D1_ons, QRS_D1_offs, QRS_D2, QRS_Ei (i = 1,2,3,4), T_Ei (i= 1,2,3,4), HFQRS, QRSw, RA, SA, TA and others). |
| Test sample | – | –1500 of patients 18 years old and older with and without cardiac and cardiac-associated pathology confirmed by the results of full examination (laboratory, clinical and instrumental) and by results of the spectral analysis of electrocardiogram (the parameters listed below will be calculated as the median of the tact-cycle: TpTe, VAT, QTc, QT / TQ, QRS_E, T_E, TP_E, BETA, BETA_S, BAD_T, QRS_D1_ons, QRS_D1_offs, QRS_D2, QRS_Ei (i = 1,2,3,4), T_Ei (i= 1,2,3,4), HFQRS, QRSw, RA, SA, TA and others). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- All patients with or without cardiac and cardiac-associated pathologies over 18 years old
Key inclusion criteria (9 listed)
- The presence of written informed consent of the patient to participate in the study
- Availability of examination data allowing for the verification or exclusion of cardiac and cardiac-associated pathology
- Age 18 years old and older
- Patients with an implanted permanent pacemaker;
- ECG changes that prevent spectral analysis;
Key exclusion criteria (3 listed)
- Poor quality of the ECG recording on a single-channel ECG monitor
- Insufficient examination data to verify or exclude cardiac or cardiac-associated pathology;
- Patient's unwillingness to continue participating in the study for any reason.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Parameters of single-channel ECG that significantly correlate with the presence of various cardiac and cardiac-associated pathologies
Time frame: through study completion, an average of 2 years
comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor
Determination of sensitivity of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data
Time frame: through study completion, an average of 2 years
comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor
Determination of specificity of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data
Time frame: through study completion, an average of 2 years
comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor
Determination of diagnostic accuracy of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data
Time frame: through study completion, an average of 2 years
comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Apr 2026.
- BackgroundA screening method for predicting left ventricular dysfunction based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms / N. Kuznetsova, Zh. Sagirova, A. Suvorov [et al.] // Biomedical Signal Processing and Control. - 2023. - Vol. 86. - P. 105219. - DOI 10.1016/j.bspc.2023.105219. - EDN APQSQF.
- BackgroundComplex automated remote system for assessing hemodynamic parameters when analyzing the native signal of a single-channel ECG and pulse wave using machine learning techniques / N. O. Kuznetsova, Zh. N. Sagirova, E. A. Sultygova [et al.] // Russian Journal of Cardiology. - 2023. - T. 28, No. S7. - pp. 41-42. - EDN LZGDKG.
- Background: PubMed 39092649Abdulraheem LW, Al-Dwa B, Shchekochikhin D, Gognieva D, Chomakhidze P, Kuznetsova N, Kopylov P, Bestavashvilli AA. A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation. Curr Cardiol Rev. 2025;21(1):e310724232529. doi: 10.2174/011573403X293703240715104503.
- Background: PubMed 38304727Mesitskaya DF, Fashafsha ZZA, Poltavskaya MG, Andreev DA, Levshina AR, Sultygova EA, Gognieva D, Chomakhidze P, Kuznetsova N, Suvorov A, Marina I S, Poddubskaya E, Novikova A, Bykova A, Kopylov P. A single-lead ECG based cardiotoxicity detection in patients on polychemotherapy. Int J Cardiol Heart Vasc. 2024 Jan 20;50:101336. doi: 10.1016/j.ijcha.2024.101336. eCollection 2024 Feb.
- BackgroundKuznetsova N.O., Alekseeva A.M., Mamedzade F.E., Sedov V.P., Kopylov F.Yu., Syrkin A.L., Chomakhidze P.Sh. Screening for heart defects when analyzing an electrocardiogram using machine learning methods (literature review) // Bulletin of new medical technologies. Electronic edition. 2025. No. 1. Publication 1-6. DOI: 10.24412/2075-4094-2025-1-1-6. EDN KNFSNS
- BackgroundKuznetsova N.O., Nartova A.A., Kurbanalieva N.K., Adueva D.Sh., Chursina E.Yu., Zhvania R.E., Ustinova D.I., Kostikova A.S., Kazakova M.V., Tarnaeva L.A., Chomakhidze P.Sh., Kopylov F.Yu. Results of screening for heart rhythm disturbances using a single-channel electrocardiogram without the participation of medical personnel // Bulletin of new medical technologies. 2025. No. 1. P. 56-60. DOI: 10.24412/1609-2163-2025-1-56-60. EDN YWJLFH.
- A single-channel ECG using machine learning techniques presentationLink provided by the sponsor.
- Individual participant dataSharing planned: No. It is not possible to provide documentation due to the prohibition received from the local ethics committee
Related trials for Arterial Hypertension
RecruitingPhase 2/3
The Effect of Additive On-Demand Sildenafil vs. Dextrose Candy on Maximum Exercise Capacity in Pulmonary Arterial Hypertension
Pulmonary Arterial Hypertension (PAH) (WHO Group 1 PH)
Not yet recruiting
Multimodal AI-Driven Out-of-Hospital Management for Hypertension and Cardiometabolic Risk
Hypertension, Hyperglycemia
Active, not recruiting
Optimizing Prescribing Decisions for Hospitalized Older Adults With Chronic Conditions
Prescribing Decisions, Diabetes
Enrolling by invitationObservational study
WSACS 2026 Delphi Consensus on the Practical Management of Intra-abdominal Hypertension and Abdominal Compartment Syndrome
Intra-Abdominal Hypertension, Abdominal Compartment Syndrome
Not yet recruitingPhase 4
Sacubitril/Valsartan vs Standard Treatment for Preventing Atrial Fibrillation Recurrence After Catheter Ablation in Persistent Atrial Fibrillation Patients With Without Hypertension
Atrial Fibrillation (Persistent), Catheter Ablation
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Arterial Hypertension and Heart Rhythm Disorders, run by I.M. Sechenov First Moscow State Medical University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 16 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Natalia Kuznetsova, Dr. (+79164778724, kuznetsova_n_o@staff.sechenov.ru), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Parameters of single-channel ECG that significantly correlate with the presence of various cardiac and cardiac-associated pathologies (time frame: through study completion, an average of 2 years). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07396792, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Apr 2026.