Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters

Table 3 Study participants selection criteria

Inclusion criteria	Non-inclusion criteria1	Exclusion criteria2
Participants age ≥ 40 years	Pregnancy and breastfeeding	Failure of the stress test for reasons unrelated to heart disease
Participants with intact mental and physical activity	Diabetes mellitus	Reluctance to continue participating in the study
Written consent to participate in the study, take blood samples, and anonymously publish the results of the study	Presence of signs of acute coronary syndrome (myocardial infarction in the prior 2 days), history of myocardial infarction
Participants in the experimental group are individuals with coronary artery disease, confirmed by stress-induced myocardial perfusion defect on the adenosine triphosphate stress myocardial perfusion computed tomography	Active infectious and non-infectious inflammatory diseases in the exacerbation phase
	Respiratory diseases (bronchial asthma, chronic bronchitis, cystic fibrosis)
	Acute thromboembolism of pulmonary artery branches
	Aortic dissection
	Critical anatomical heart defects
	Active oncopathology
	Decompensation phase of acute heart failure
	Neurological pathology (Parkinson’s disease, multiple sclerosis, acute psychosis, Guillain-Barré syndrome)
	Cardiac arrhythmias that do not allow exercise ECG testing (Wolff-Parkinson-White syndrome, Sick sinus syndrome, AV block of II-III-degree, persistent ventricular tachycardia)
	Diseases of the musculoskeletal system that prevent passing a stress test (bicycle ergometry)
	Allergic reaction to iodine and/or adenosine triphosphate

¹From the beginning of the study we did not include such patients in the study.

²After the end of the study we excluded these patients because of some reasons that related to these criteria we stated.

ECG: Electrocardiography; AV: Atrioventicular.