## 1 Introduction
Biomedical signals are often used for the design and development of human-machine interfaces, which is emerging branch biomedical engineering. [PERSON] [3] proposes such a system dedicated to persons with disabilities, which that is a hands-free head-gesture-controlled interface. It can help, for example, paralyzed people to send messages or the visually impaired to handle travel aids. The system contains a small stereovision rig with a built-in inertial measurement unit (IMU). To recognize head movements, two methods are considered. In the first approach, for various time window sizes of the signals recorded from a three-axis accelerometer and a three-axis gyroscope, selected statistical parameters were calculated. In the second technique, the direct analysis of signal samples recorded from the IMU was performed. Next, the accuracies of 16 different data classifiers for distinguishing the head movements: pitch, roll, yaw, and immobility were evaluated. The highest accuracies were obtained for the direct classification of unprocessed samples of IMU signals and with the use of SVM classifier (95% correct recognitions), while the random forests classifier reached 93%. Such results indicate that a person with physical or sensory disability can efficiently communicate with other people or manage applications using simple head gesture sequences. MRI is one of most common imaging modalities used in diagnostics and treatment planning. [PERSON] [4] shows an application of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for visualizing and quantifying kidney perfusion, which is one of the most important indicators of an organ's state. In clinical practice, kidney function is assessed by measuring the glomerular filtration rate (GFR). Estimating the GFR based on DCE-MRI data requires the application of an organ-specific pharmacokinetic (PK) model, but the determination of the model parameters is sensitive to the determination of the arterial input function (AIF). Thus, a multi-layer perceptron network was proposed for PK model parameter determination. As a reference method, the trust-region reflective algorithm was used. The efficiency of the proposed approach was tested for 20 data sets, collected for 10 healthy volunteers whose image-derived GFR scores were compared with ground-truth blood test values. The achieved mean difference between the image-derived and ground-truth GFR values was 2.35 mL/min/1.73 m\({}^{2}\), which is comparable to the result obtained for the reference estimation method (\(-\)5.80 mL/min/1.73 m\({}^{2}\)). It was demonstrated that the implemented neural networks ensure agreement with ground-truth measurements at a comparable level. The advantages of using a neural network are twofold. First, it can estimate a GFR value without the need to determine the AIF for each individual patient. Second, a reliable estimate can be obtained, without the need to manually set up either the initial parameter values or the constraints thereof. After further validation and more exhaustive patient testing, the proposed approach can be implemented in clinical practice. Another important application of ML to fundus blood vessel image segmentation is presented in [5]. Such analysis is important in the diagnosis and treatment of several diseases, such as hypertension, coronary heart disease, and diabetes. The analysis of such images is rather complicated, and classic algorithms suffer from relatively low segmentation accuracy. Thus, an improved U-shaped neural network (MRU-NET) segmentation method for retinal vessels was proposed. After the application of the image enhancement algorithm, the image contrast is improved. Applied random segmentation divides the image into smaller image blocks that help to reduce the complexity of the U-shaped neural network model. Next, the residual learning is introduced into the encoder and decoder to improve the efficiency of feature analysis.