Objective: Both the central nervous system and the autonomic nervous system are complex physiological networks which modulate the heart rate. They are spatially extended, have built-in delays and work on many time scales simultaneously—nonhomogeneous networks with multifractal dynamics. The object of our research was the analysis of human heart rate variability (HRV) using the nonlinear multiscale multifractal analysis (MMA) method for several cardiovascular diseases. The analysis of HRV (night-time recordings) involved six groups of patients: 61 healthy persons, 104 cases with aortic valve stenosis, 42 with hypertrophic cardiomyopathy, 36 with atrial fibrillation, 70 patients with coronary artery disease and 19 with congestive heart failure. 85% of patients formed a training data set (282 subjects) and 15% formed a test data set (50 subjects). Approach: Multiscale multifractal analysis allows one to analyze the complexity of HRV and find the scaling properties of its fluctuations. The main result of MMA is the Hurst surface, the shape of which changes depending on the medical case analyzed. We prepared six criteria to distinguish a multifractal pattern for healthy subjects. We also prepared additional criteria, enabling one to recognize atrial fibrillation. Main results: For the training data set, we obtained the following accuracy statistics in distinguishing the patients from the healthy: 68% for coronary artery disease, 67% for hypertrophic cardiomyopathy, 88% for atrial fibrillation, 74% for aortic valve stenosis and 83% for congestive heart failure. For the complete training data set we obtained an accuracy of 73%, and 80% for the test data set (mean for ten random selections of the test data set). Significance: The results of MMA presented here provide an additional input into the diagnostic process and may help to create a paradigm for future studies on medical screening methods, especially in that MMA focuses on very low frequencies of HRV not easily accessible by standard medical techniques. Satisfactory statistics for screening using both MMA and the unfiltered version of LF/HF indicate that the nature of the complete network moderating heart rhythm needs to be studied and that sinus rhythm in clinical patients may not always be separated from arrhythmia when its incidence is large.

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Objective. The physiological activity of the heart is controlled and modulated mostly by the parasympathetic and sympathetic nervous systems. Heart rate variability (HRV) analysis is therefore used to observe fluctuations that reflect changes in the activity in these two branches. Knowing that acceleration and deceleration patterns in heart rate fluctuations are asymmetrically distributed, the ability to analyze HRV asymmetry was introduced into MMA. Approach. The new method is called asymmetric multiscale multifractal analysis (AMMA) and the analysis involved six groups: 36 healthy persons, 103 cases with aortic valve stenosis, 36 with hypertrophic cardiomyopathy, 32 with atrial fibrillation, 59 patients with coronary artery disease (CAD) and 13 with congestive heart failure. Main results. Analyzing the results obtained for the 6 groups of patients based on the AMMA method, i.e. comparing the Hurst surfaces for heart rate decelerations and accelerations, it was noticed that these surfaces differ significantly. And the differences occur in most groups for large fluctuations (multifractal parameter q > 0). In addition, a similarity was found for all groups for the AMMA Hurst surface for decelerations to the MMA Hurst surface—heart rate decelerations (lengthening of the RR intervals) appears to be the main factor determining the shape of the complete Hurst surface and so the multifractal properties of HRV. The differences between the groups, especially for CAD, hypertrophic cardiomyopathy and aortic valve stenosis, are more visible if the Hurst surfaces are analyzed separately for accelerations and decelerations. Significance. The AMMA results presented here may provide additional input for HRV analysis and create a new paradigm for future medical screening. Note that the HRV analysis using MMA (without distinguishing accelerations from decelerations) gave satisfactory screening statistics in our previous studies.

The growing demand for diagnosing of cardiovascular diseases leads to the development of new solutions for automatic classification of recorded ECG signals. Creating a robust and fast algorithm for automatic classification of ECG signal is crucial to improve the quality of healthcare, especially in countries where a lack of experienced specialists is an issue or the healthcare system is overloaded. The aim of the PhysioNet/Computing in Cardiology Challenge 2020 is to create an algorithm for classification of 12-lead ECGs based on ECG signals from multiple databases across the world. The shared training set consisted of 43,101 ECG recordings lasting from 5 to 1800 seconds. We (BioS Team) proposed the machine learning algorithm based on convolutional neural networks. The ECG signals were pre-processed using moving median filters to remove high-frequency noise and baseline wandering. We developed simply convolutional neural network consisting of four main convolutional blocks and one fully connected layer. We achieved a challenge validation score of 0.349, and full test score of 0.279, placing us 14 out of 41 in the official ranking.