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We present information about Ohtahara II syndrome which is a rare epileptic encephalopathy, and our own clinical case: a 4-year old child with this disorder.

It should be mentioned that any particular traffic accident is most often a concurrence of a number of causes and factors associated with any of the subsystems “Driver – Car – Road – Environment”. As evidenced by judicial practice, crimes under Article 264 of the Criminal Code of the Russian Federation are traditionally qualified by courts as committed in a careless form of guilt in the form of criminal flippancy. However, there may exist doubts about the correct assessment of the mental element in these crimes. The main question is if these crimes were intentional. The relevance of the study is conditional upon the need to develop a system of prevention of road traffic injuries and improve the effectiveness of measures to prevent them. The purpose of the study is to analyze theoretical problems and the results of empirical observation for road safety. This article is devoted to the relevant problem of accurate determination of the form of guilt in violation by the person driving the vehicle of road traffic regulations on the example of the analysis of the crime under Article 264 of the Criminal Code of the Russian Federation “Violation of road traffic regulations and the rules of operation of vehicles.” In the preparation of the article, general scientific methods (dialectical, logical, socio-scientific method, method of system analysis) and interdisciplinary methods (sociological, statistical, method of the research of specific facts) were used. The novelty of the study is in an attempt to deeply analyze and interpret the practical application of the provisions of Article 264 of the Criminal Code of the Russian Federation, identify the problem of accurate determination of the form of the guilt of the mentioned act committed by the person driving the vehicle when violating road traffic regulations or the rules operation of vehicles.

Two algorithms for calculating the phase transformation, which consists in calculating the outputs of a chain of phase links, are considered. The algorithms are based on the idea of synthesis of a dual phase link, which allows calculating the outputs of a cascade of two-phase links in one pass. To synthesize dual phase links, the representation of digital filters by the space state method is used. The proposed algorithms are implemented in the C language, and they are compared with the direct form of implementation of the phase transformation. During the experiments, the speed of calculating the chain of phase links on a personal computer with an Intel Core i7-37700 processor was estimated using the profiler built into the Visual Studio environment. The results showed that both algorithms allow accelerating the calculation of the chain of phase links by 1.3 and 1.8 times, respectively.

В работе представлена эффективная реализация банка гамматон-фильтров (БГФ) на основе неравнополосного косинусно-модулированного банка фильтров (НКМБФ), использующего фазовое преобразование. Рассмотрены примеры практических задач, в которых применяется банк гамматон-фильтров, проанализированы его основные особенности и недостатки. Приведено описание равнополосного косинусно-модулированного банка фильтров, а также показан процесс синтеза НКМБФ из его равнополосного аналога при помощи фазового преобразования. Разработан оптимизационный метод проектирования фильтра-прототипа НКМБФ для аппроксимации частотных характеристик БГФ. В основе метода лежит мультипликативная модель импульсной характеристики фильтра-прототипа, использующая логистические сигмоидальные функции. Суть предлагаемого метода заключается в оптимизации фильтра-прототипа с целью минимизации среднеквадратичной ошибки между АЧХ БГФи НКМБФ для каждого канала. Выполнена программная реализация на языке Python с использованием библиотеки PyTorch. Проведены экспериментальные исследования предложенного метода. Результаты экспериментов показали, что НКМБФ можно использовать для аппроксимации частотных характеристик БГФ, а результирующая АЧХ имеет монотонные спады за счёт использования логистических сигмоидальных функций. Проведён анализ зависимости результирующей ошибки аппроксимации частотных характеристик банка гамматон-фильтров от количества сигмоид, используемых для синтеза фильтра-прототипа НКМБФ на базе мультипликативной модели импульсной характеристики. Выполнен анализ вычислительной сложности НКМБФ, показано как зависит число операций сложения и умножения от длины импульсной характеристики фильтра-прототипа и числа каналов банка фильтров. Сделан вывод, что использование НКМБФ для реализации банка гамматон-фильтров позволяет существенно уменьшить вычислительные затраты на реализацию гамматон-фильтров по сравнению с прямой реализацией.

The problem of recognizing emotions in a speech signal using mel-frequency cepstral coefficients using a classifier based on the support vector machine has been studied. The RAVDESS data set was used in the experiments. A model is proposed that uses a 306-component suprasegmental feature vector as input to a support vector machine classifier. Model quality was assessed using unweighted average recall (UAR). The use of linear, polynomial and radial basis functions as a kernel in a classifier based on the support vector machine is considered. The use of different signal analysis frame sizes (from 23 to 341 ms) at the stage of extracting mel-frequency cepstral coefficients was investigated. The research results revealed significant accuracy of the resulting model (UAR = 48 %). The proposed approach shows potential for applications such as voice assistants, virtual agents, and mental health diagnostics.

A brief overview of the results of work in the field of “Devices, systems and technologies for medical purposes”, obtained with the direct participation of the authors of the article, is given. The results of the development and research in the field of modeling the effects of physical factors on the human body and digital processing of biomedical signals are presented. The principal aspects of the original systems of long-term monitoring of the human condition and control of the musculoskeletal system are presented.

The article presents the description of three clinical cases of the successful treatment of toe onychomycosis and athlete’s foot of various etiologies using 5% amorolfine antifungal nail lacquer. The first case: a 31-year-old woman was diagnosed with white superficial onychomycosis of great toe caused by Trichophyton rubrum. The treatment with 5% amorolfine once a week for 6 months resulted in full recovery (both mycological and clinical). The second case: a 42-year-old woman developed onychomycosis after the application of decorative coating on her nails; onychomycosis was caused by Scopulariopsis brevicaulis. She was treated with itraconazole pulse therapy and 5% amorolfine lacquer. She fully recovered. The third case: a 65-year-old man with total onychomycosis of 10 toes developed the skin mycosis of the left foot and lower third of the leg. He was prescribed a therapy with sertaconazole cream and 5% amorolfine lacquer. The use of 5% amorolfine lacquer was continued to prevent from recurrent dermatomycosis. Thus, the above mentioned cases are a good example of the advantages of using 5% amorolfine lacquer in the treatment of most toe onychomycosis types caused by any pathogens (dermatophytes, yeasts or molds).

The task of detecting vocal abnormalities is characterized by a small amount of available data for training, as a consequence of which classification systems that use low-dimensional data are the most relevant. We propose to use LASSO (least absolute shrinkage and selection operator) and BSS (backward stepwise selection) methods together to select the most significant features for the detection of vocal pathologies, in particular amyotrophic lateral sclerosis. Features based on fine-frequency cepstral coefficients, traditionally used in speech signal processing, and features based on discrete estimation of the autoregressive spectrum envelope are used. Spectral features based on the autoregressive process envelope spectrum are extracted using the generative method, which involves calculating a discrete Fourier transform of the report sequence generated using the autoregressive model of the input voice signal. The sequence is generated by the autoregressive model so as to account for the periodic nature of the Fourier transform. This improves the accuracy of the spectrum estimation and reduces the spectral leakage effect. Using LASSO in conjunction with BSS allowed us to improve the classification efficiency using a smaller number of features as compared to using the LASSO method alone.

Abstract Allpass transform is a key element of the discrete systems which produces signal processing in warped frequency domain. The paper presents a new short‐critical path algorithm for allpass transform, which essentially consists of calculating the outputs of allpass filters chain. The algorithm is based on the proposed dual allpass filter structure, which computes the outputs of a cascade of two allpass filters in one computational pass. The dual allpass filter structure is obtained using the state variable representation of the allpass filter. The efficiency of the proposed algorithm is analyzed in the context of allpass transform implementation using a general purpose CPU. The results of the experiment show that the use of the proposed algorithm accelerates the calculation of the allpass transform by 1.81 times.

Objectives. The problem of developing a method for calculating small-sized spectral features that increases the efficiency of existing machine learning systems for analyzing and classifying voice signals is being solved.Methods. Spectral features are extracted using a generative approach, which involves calculating a discrete Fourier spectrum for a sequence of samples generated using an autoregressive model of input voice signal. The generated sequence processed by the discrete Fourier transform considers the periodicity of the transform and thereby increase the accuracy of spectral estimation of analyzed signal.Results. A generative method for calculating spectral features intended for use in machine learning systems for the analysis and classification of voice signals is proposed and described. An experimental analysis of the accuracy and stability of the spectrum representation of a test signal with a known spectral composition has been carried out using the envelopes. The envelopes were calculated using proposed generative method and using discrete Fourier transform with different analysis windows (rectangular window and Hanna window). The analysis showed that spectral envelopes obtained using the proposed method more accurately represent the spectrum of test signal according to the criterion of minimum square error. A comparison of the effectiveness of voice signal classification with proposed features and the features based on the mel-frequency kepstral coefficients is carried out. A diagnostic system for amyotrophic lateral sclerosis was used as a basic test system to evaluate the effectiveness of proposed approach in practice. Conclusion. The obtained experimental results showed a significant increase of classification accuracy when using proposed approach for calculating features compared with the features based on the mel-frequency kepstral coefficients.

Introduction. Skin metastases are the result of skin infiltration caused by the proliferation of cells of remotely located malignant tumors occurring with a frequency of 0.6–10.4 % in various oncological processes. They may be a sign of progressive neoplasm or a manifestation of newly diagnosed cancer. Due to a high variability of clinical manifestations leading to misdiagnoses, a limited number of articles describe clinical and dermatoscopic signs of solid tumors metastatic nodes. Aim: to analyze the clinical and dermatoscopic features of skin metastases from breast cancer.Materials and methods. A female patient has complaints of slight pain and itching in the lesions area on the scalp. According to her medical history, a right mastectomy was performed for right breast cancer in 2012, followed by hormone therapy willfully discontinued by the patient. Given the uncommon localization limited with the scalp, the differential diagnosis was made with a spectrum between a cylindroma, multiple basal cell cancer and metastatic carcinoma. A biopsy was carried out to confirm the diagnosis. Results and examination. The pathological skin process revealed itself through multiple nodes up to 2 cm in diameter, pinkish in color, dense, painless on palpation, poorly demarcated. Dermatoscopically: polymorphic vessels on an erythematous background, chrysalis-like structures. Breast cancer metastases were verified by histological and immunohistochemical examination.Conclusion. Skin metastases may be the first sign of cancer recurrence. Dermatoscopy can facilitate differenting them from other skin diseases. Density and diameter of pathological vessels are prognostically significant. Ifdetected lesions do not fit the standard pattern of skin neoplasms, histological verification of the diagnosis is recommended to be carried out.

About 95% of human body serotonin synthesis occurs in the gastrointestinal tract (GI). Lack of sufficient serotonin levels is thought to play a key role in mood disorders, including anxiety disorders. In this study, we looked at a disorder affecting the GI tract, irritable bowel syndrome (IBS), and aimed to determine whether IBS is differentially associated with anxiety disorders in 252 chronic pain patients in the presence of a history of alcohol use disorders (AUD) given that alcohol is an extremely aggressive substance for the GI mucosa. We found that while the prevalence of IBS was not affected by the presence of AUD in chronic pain patients, IBS had significantly higher comorbidity with anxiety disorders in chronic pain patients with comorbid alcohol use disorders. We argue that these findings highlight mechanistic differences in the comorbidity of anxiety disorders with chronic pain and AUD, implicating a central role for GI problems stemming from chronic alcohol use. The findings may have important implications for the treatment of IBS patients with AUD who commonly present with anxiety disorders which could motivate the continuation of problematic drinking and impede recovery success. We propose that addressing GI problems in patients with AUD may help manage AUD and recovery more effectively.

The paper describes an approach to design a system for analyzing and classification of a voice signal based on perturbation parameters and cepstral representation. Two variants of the cepstral representation of the voice signal are considered: based on mel-frequency cepstral coefficients (MFCC) and based on bark-frequency cepstral coefficients (BFCC). The work used a generally accepted approach to calculating the MFCC based on the time-frequency analysis by the method of discrete Fourier transform (DFT) with summation of energy in subbands. This method approximates the frequency resolution of human hearing, but has a fixed temporal resolution. As an alternative, a variant of the cepstral representation based on the BFCC has been proposed. When calculating the BFCC, a warped DFT-modulated filter bank was used, which approximates the frequency and temporal resolution of hearing. The aim of the work was to compare the effectiveness of the use of features based on the MFCC and BFCC for the designing systems for the analysis and classification of the voice signal. The results of the experiment showed that in the case when using acoustic features based on the MFCC, it is possible to obtain a voice classification system with an average recall of 80.6 %, and in the case when using features based on the BFCC, this metric is 83.7 %. With the addition of the set of MFCC features with perturbation parameters of the voice, the average recall of the classification increased to 94.1 %, with a similar addition to the set of BFCC features, the average recall of the classification increased up to 96.7 %.

The purpose of this article is to investigate the application of DFT-modulated filter bank in systems with significant spectral component amplification like hearing aid. There is a description of analysis / synthesis method based on short-time Fourier transform (STFT), which is used in most systems of speech information processing. It is shown that DFT-modulated filter bank is a generalization of STFT-method. In analysis / synthesis system based on DFT-modulated filter bank, the input signal is divided into subbands, passing through the analysis filter bank then each subband is amplified and the last step is to reconstruct the signal with synthesis filter bank. However, in digital systems with significant spectral component amplification, the resulting signal is distorted after reconstruction because of amplification factor difference in each subband. The article provides expressions for the distortion and the aliasing functions, allowing to estimate the distortion value, which appears in analysis / synthesis system of DFT-modulated filter bank. Efficient algorithms for calculating the distortion and the aliasing functions are also offered. In future it is planning to develop a procedure for optimizing the DFT-modulated filter bank based on the proposed efficient algorithms for calculating distortion and spectral aliasing in the filter bank.

A method for the simulation of reduced frequency resolution of the ear in patients with sensorineural hearing loss is proposed. The method is based upon the ability to adjust it according to the audiogram of a concrete person by frame-by-frame signal processing in the frequency domain. Simulation of the effect of the reduced frequency resolution of the ear is achieved by processing the components of amplitude spectrum of the original sound signal by the "smearing" function. The "smearing" function is formed from the amplitude-frequency characteristics of the auditory filters, which bandwidth is determined by the audiogram of the deaf person. The proposed method is implemented in the MATLAB. An experimental study of the effect of the reduced frequency resolution of the ear using the speech intelligibility test was conducted. The experiment involved 15 people who listened the records processed by the proposed method with various settings and noise conditions. Experimental data have shown that reduced frequency resolution of the ear leads to the deterioration in speech intelligibility, especially in the presence of background noise. Based on the answers of the participants of the experiment, the confusion tables of sounds were compiled, reflecting the fact of indistinguishability of sounds similar in frequency, that confirms the correctness of the proposed method.

PurposeThe CT angiography (CTA) spot sign is a strong predictor of hematoma expansion in intracerebral hemorrhage (ICH). However, CTA parameters vary widely across centers and may negatively impact spot sign accuracy in predicting ICH expansion. We developed a CT iodine calibration phantom that was scanned at different institutions in a large multicenter ICH clinical trial to determine the effect of image standardization on spot sign detection and performance.MethodsA custom phantom containing known concentrations of iodine was designed and scanned using the stroke CT protocol at each institution. Custom software was developed to read the CT volume datasets and calculate the Hounsfield unit as a function of iodine concentration for each phantom scan. CTA images obtained within 8 h from symptom onset were analyzed by two trained readers comparing the calibrated vs. uncalibrated density cutoffs for spot sign identification. ICH expansion was defined as hematoma volume growth >33%.ResultsA total of 90 subjects qualified for the study, of whom 17/83 (20.5%) experienced ICH expansion. The number of spot sign positive scans was higher in the calibrated analysis (67.8 vs 38.9% p

The paper investigates the problem of voice activity detection from a noisy sound signal. An extremely compact convolutional neural network is proposed. The model has only 385 trainable parameters. Proposed model doesn’t require a lot of computational resources that allows to use it as part of the “internet of things” concept for compact low power devices. At the same time the model provides state of the art results in voice activity detection in terms of detection accuracy. The properties of the model are achieved by using a special convolutional layer that considers the harmonic structure of vocal speech. This layer also eliminates redundancy of the model because it has invariance to changes of fundamental frequency. The model performance is evaluated in various noise conditions with different signal-to-noise ratios. The results show that the proposed model provides higher accuracy compared to voice activity detection model from the WebRTC framework by Google.

Предложен метод определения патологических изменений голоса на основе анализа модуляционного спектра речевого сигнала. Модуляционный спектр вычисляется при помощи двух последовательных частотно-временных преобразований: первое переводит сигнал в частотную область, второе – в модуляционную. Особенностью предложенного метода является использование неравномерного частотно-временного плана при переводе сигнала в частотную область. Предложен способ оценки модуляционного спектра, основанный на принципах антропоморфической обработки сигналов, имитирующий неравномерное частотно-временное разрешение слуховой системы человека. Использован неравнополосный банк фильтров модулированный дискретным преобразований Фурье, аппроксимирующий шкалу критических частот и повышающий временное разрешение в широких полосах. Банк фильтров использует фазовые (всепропускающие) звенья в качестве элементов задержки, что позволяет получить неравномерный частотно-временной план. Выполнено сравнение предложенного способа с более традиционным способом вычисления модуляционного спектра в критических полосах с фиксированным временным разрешением. Приведены данные экспериментов по использованию параметров модуляционного спектра для определения патологических изменений голоса, обусловленных: 1) боковым амиотрофическим склерозом (БАС); 2) заболеваниями гортани. Отбор и ранжирование параметров модуляционного спектра, использованных в качестве информационных признаков для классификации, выполнен при помощи метода LASSO. Классификация параметров на основе линейного дискриминантного анализа обеспечила высокую точность определения патологии (более 97%). Полученные результаты свидетельствуют о том, что использование неравномерного частотного-временного плана является предпочтительным в случае, когда анализируемый сигнал является протяжным гласным звуком, поскольку обеспечивает более высокую точность обнаружения патологии при меньшем числе модуляционных параметров.

The comparative study of two types of voice signal representation for larynx pathology detection is presented. Parameters obtained in clinical system lingWaves compared to parameters obtained by mel-frequency cepstral analysis. The classifier based on the probabilistic model (logistic regression) was designed to determine the suitability of given parameters for the larynx pathology detection problem. To train the classifier, the base of voice samples of 60 persons was recorded, 30 of which constitute the control group, and the other 30 had various diseases of the larynx (nodules of the vocal folds, laryngeal paralysis, or functional dysphonia). The results show that the classifier based on mel-frequency cepstral parameters (83,8 %) higher than the classifier based on parameters obtained in lingWaves (60,4 %).

The purpose of the work was to develop a speech signal processing method for the hearing pathologies correction based on psychoacoustically motivated transposition of high-frequency components of the signal spectrum to the low-frequency region with subsequent frequency-dependent amplification. To achieve this goal, several tasks related to the development of principles of frequency transposition in a speech signal were solved. The adjustment of the method is carried out according to the audiogram of a deaf person. For frequency transposition, source and target frequency bands are selected. The width of the source frequency band is fixed, while the width of the target band is adaptive. Spectrum transposition is performed only for consonants, the perception of which is more difficult for people with hearing loss. The classification of sounds (into vowel-consonant - pause classes) is implemented using one-layer neural network. The feature vector consists of: the zero crossing rate, short-term energy, short-term magnitude, normalized autocorrelation function and the first spectral moment. To preserve the naturalness of transposed sounds, the concept of equal loudness is used. To compensate for the attenuation in the perception of sound by a deaf person, a frequencydependent signal amplification based on an audiogram is used. The effectiveness of the proposed method was verified experimentally using hearing loss effect simulation. The experiment involved 10 people who were given to listen to the recordings passed through the hearing loss model, as well as recordings passed through the hearing loss model with subsequent correction (using proposed method). The results showed that using the proposed hearing correction method improves speech intelligibility on average by 6 %.