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Introduction. The most common method for diagnosing cardiovascular diseases is the method of ECG monitoring. In order to facilitate the analysis of the obtained monitorograms, special software solutions for automated ECG processing are required. One possible approach is the use of algorithms for automated ECG processing. Such algorithms perform  clustering of cardiac signals by dividing the ECG into complexes of similar cardiac signals. The most representative complexes obtained by statistical averaging are subject to further analysis.Aim. Development of an algorithm for automated ECG processing,  which performs clustering of cardiac signals by dividing the ECG into complexes of similar cardiac signals.Materials and methods. Experimental testing of the developed software was carried out using patient records provided by the Pavlov First State Medical University of St  Petersburg. The software module was implemented in the MatLab environment.Results. An algorithm for clustering cardiac signals with post-correction for the tasks of long-term ECG monitoring and a software module on its basis were proposed.Conclusion.  The presence of a small number of reference cardiac signal complexes, obtained through ECG processing using the proposed algorithm, allows physicians to optimize the process of ECG analysis. The as- obtained information serves as a basis for assessing dynamic changes in the shape and other parameters of cardiac signals for both a particular patient and groups of patients. The paper considers the effect of synchronization errors of clustered cardiac signals on the shape of the averaged cardiac complex. The classical solution to the deconvolution problem leads to significant errors in finding an estimate of the true form of a cardiac signal complex. On the basis of analytical calculations, expressions were obtained for the correction of clustered cardiac signals. Such correction was shown to reduce clusterization errors associated with desynchronization, which creates a basis for investigating the fine structure of ECG signals.

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Introduction. Cardiopulmonary stress test provides significant diagnostic and prognostic information of the condition of patients with cardiovascular and pulmonary diseases. There is a serious problem, that final phase of stress testing is a physically difficult exercise for a person. There is a significant risk of occurrence and development of pathological conditions of the patient's cardiovascular system. One of the solutions is the development of methods for assessing the biological parameters of the patients at the end of a load protocol based on data from the initial stages of the test.Aim. Development of a method for finding an estimate of the maximum heart rate (HR) and of the peak oxygen consumption (OC) for the patients with chronic heart failure at the end of a cardiorespiratory exercise stress test, based on the results of the study obtained at the first initial stages of the test.Materials and methods. For the study, 149 anonymized records of rhythmograms and data of changes in the oxygen consumption of the patients with chronic heart failure were used. The patients underwent a cardiopulmonary stress test by a bicycle ergometer using step-by-step load protocol (the load power increase at each stage was 10 W, the duration of the load stage was 1 min)Results. Based on the analysis of the data obtained, a method for assessing the peak values of HR and of PC of the patients with chronic heart failure was developed.Conclusion. The relative error of the proposed estimate of the HR peak in most cases was no more than 10 %, which allows it to be used for practical purposes. It was established that when performing 70 % of the stress protocol, the error of the proposed estimate of the OC peak in most cases did not exceed 20 %. More research is needed to improve the accuracy of the assessment for using in medical applications aimed to the modernization of methods and equipment for stress testing of the patients.