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BACKGROUND: Electrocardiography is one of the simplest, most widely used, inexpensive, and informative methods in functional diagnostics; yet, if performed poorly, its diagnostic value is sharply reduced. Several attempts were made to systematize errors and deviations in electrode application, but all concerned the most common options (rearrangement of red and yellow electrodes, yellow and green electrodes, and chest electrodes above or below the standard scheme). AIM: To create an electrocardiogram dataset with different options for transpositions and dispositions of electrodes during electrocardiogram recording. MATERIALS AND METHODS: The study included patients aged 1875 years (27 males and 22 females). All patients provided informed consent for electrocardiogram registration. During one visit, the cardiogram was recorded on the device Modular system for recording and remote transmission of electrocardiograms (EASY ECG) for each patient. RESULTS: In all, 488 electrocardiograms were recorded in 49 patients. The results obtained indicate a significant variability of the electrocardiogram pattern. Visual analysis of the electrocardiograms revealed no difficulties in determining the transposition associated with rearranging the leads on the arms (RY) in the thoracic C1C2. The placement of thoracic electrodes in contact cheek-to-cheek dispositions with the transfer of thoracic leads above or below two intercostals was reliably determined compared with the Wilson scheme. The transpositions of the yellow and green limb electrodes and the change in the position of the thoracic ones when they are lined up in a straight line, bullied between the ribs (curved), and confused in places C5 and C6 are difficult to determine even when comparing two cardiograms next to each other, with the correct and transpositional superposition of the electrodes. The initial changes on the electrocardiograms, physique type, breast size, or the presence of an implant most likely determine it. CONCLUSION: An electrocardiography dataset was obtained using various electrode dislocation variants. The dataset consists of a series of electrocardiograms obtained for each patient with several electrode placement options and contains both normal and pathological electrocardiograms.

Clinical physiology involves a complete, comprehensive, multilateral study of the functions of both affected and healthy organs, which allows us to assess the compensatory capabilities of the body. Artificial intelligence is increasingly being used in medicine, including in clinical physiology. This is facilitated by the increase in computing processing power, development of cloud services and datasets, and numerous scientific articles demonstrating the effectiveness and viability of such intelligent solutions. Although the approach to medical dataset development is generally similar, there are a number of key features and significant differences in clinical physiology. Artificial intelligence systems in clinical physiology may be effectively trained and applied in practice by following the recommendations in this study. The national standard of the Russian Federation GOST R 59921.9-2022, which has entered into force, is included in the set of standards Artificial Intelligence systems in clinical medicine and establishes additional requirements for data analysis algorithms and test methods of artificial intelligence systems used in the field of clinical physiology. A crucial feature of the created standard is its qualimetric type (i.e., it has a mandatory set of demonstration data). Russia is one of the first countries to start developing quasi-metric standards worldwide, and 15 industry standards in the field of artificial intelligence (2 of them in medicine) will come into force this year.