The application of modern and perspective bioradiophotonics methods on the basis of optical and acousto-optic devices for the processing of bioelectric signals (BES) have been considered. The basic application difficulties of these methods are connected with the fact that the studied signals are of low frequencies, and development of special actions are required for the processing devices adapting. It has been proposed to introduce into acousto-optic processing system with time integration the bioelectric signals using method of high frequency carrier with linear frequency modulation which is modulated by low frequency signal. The system configuration has to provide the realization of convolution procedure; hence, the used Bragg cells must be oriented oppositely to each other. The performed analysis has shown that it is possible to realize both signal power spectrum calculation and its wavelet transform; the presence of carrier is obligatory for both kinds of processing. Also, the method of the preliminary BES compression has been proposed for its transmission into the high frequency area. In this case, the possibility occurs to introduce the signal into the acoustooptic processing system with spatial integration. In the simple acousto-optic correlator with the reference transparency the envelope of the correlation function is formed depending on time. Using the set of the reference transparencies in the multichannel correlator, it is possible to realize the prolonged BES wavelet analysis using the mother wavelet. The optical preliminary BES processing can be also performed using liquid crystal arrays. The analysis of the processing of electrocardiac signals obtained from the experimental animals (rats) has been listed using the liquid crystal array for the signal introduction into optical processing system. It has been shown that both spectral and wavelet processing can be realized in this case without using of the high frequency carrier by the low frequency signal. The use of the obtained results will make it possible to create a new family of devices for wavelet processing of bioelectrical signals implemented in real time which will make an important contribution to improving the diagnosis of diseases of the cardiovascular system, the cortex, and the central nervous system.
The sensitivity of exercise ECG is marginally sufficient for detection of mild reduction of coronary blood flow in patients with early coronary atherosclerosis. Here we describe the application of new technique of ECG registration/analysis – ultrahigh resolution ECG (UHR ECG) – for early detection of myocardial ischemia (MIS). The utility of UHR ECG vs. conventional ECG (C ECG) was tested in anesthetized rats and pigs. Transmural MIS was induced in rats by the ligation of the left coronary artery (CA). In pigs, subendocardial ischemia of variable extent was produced by stepwise inflation of balloon within the right CA causing 25-100% reduction of its lumen. In rats, a reduction in power spectral density (PSD) in high-frequency (HF) channel of UHR ECG was registered at 60 s after ischemia (power 0.81±0.14 vs. 1.25±0.12 mW at baseline, p<0.01). This was not accompanied by any ST segment elevation on C ECG. In pigs, PSD in HF channel of UHR ECG was significantly decreased at 25% reduction of CA lumen, while ST segment on C ECG remained unchanged. In conclusion, UHR ECG enabled earlier detection of transmural MIS compared to C ECG. PSD in HF-channel of UHR ECG demonstrated greater sensitivity in the settings of subendocardial ischemia.
Opportunity research of using neural networks and computer vision to analyze images of skin lesion and identify features of various pathologies, including oncological neoplasms. A methodology has been developed that makes it possible to evaluate the significance of combinations of color components and spaces in feature extraction using local binary patterns (LBP) and histogram of oriented gradients (HOG) computer vision technologies to extract features of skin changes binary classification of human skin lesions. Optimization of extracted feature makes it possible to more effectively solve the problem of data separability in classification. Research reveals an accessible way to classify skin lesions on a small dataset (less than 1000 images). Research is supposed to be applied to data sequences obtained using a new unique method of multispectral processing of skin lesions. In the course of the work, data from the ISIC-19 and ISIC-20 datasets were used. Samples were formed with a limit of 1000 images for training and validating the models. Additionally, a test sample of 250 images was formed. All images were reduced to 128 × 128 pixels and converted to YCrCb, BGR, Grayscale, HSV color spaces. Features were extracted for each color channel using the HOG and LBP methods. Mathematical models, including neural networks have been used for data classification. The effectiveness of features combinations by color channels and feature extraction methods was evaluated. The preprocessed images were divided into training and validation subsets in a 70/30 ratio. The accuracy, recall, precision and f1-score metrics were used to evaluate the models. The models were evaluated using stratified cross-validation and a test dataset. Optimization of model parameters was carried out based on the loss function represented by the average of cross-validation and evaluation on the validation set. In the process of research, more than 15 000 different optimizations of model parameters were executed. The most stable results on the validation dataset were achieved using ensemble of models, which were trained on a combination of features using local binary patterns (LBP) and histogram of oriented gradients (HOG) technologies. Models which used only local binary patterns technology had the best metrics values, but these models are not recommended to be used in practice without ensemble with stronger models. The results gained can be applied for usage with an ensemble of state-of-the-art convolutional and recurrent neural networks. The proposed approach is universal and applicable both for the analysis of individual images of skin neoplasms and for the analysis of their sequences obtained by the method of multispectral image processing. The technique can be applied to datasets with a limited amount of data. The results obtained will be of interest to specialists in the fields of computer vision and medical images analysis.
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