Създаване на модели на сериозни образователни игри с приложение в медицинското обучение

The report presents the serious educational games in the field of medicine and their advantages in the medical education of the scientific community, medical professionals, teachers, researchers, trainers, decision makers, training companies, students, gamers and others. Research and analysis of innovative, technological methods and tools for creating semantically based knowledge for the purposes of training in medicine has been conducted. Research and analysis of innovative methods and tools for modeling and creating computer-based medical educational resources have been made. Attention is paid to the methods for creating computer-based resources in the field of medical education. The report presents videos and a serious educational game that are used in medical universities in medical education.This work was supported by the Bulgarian Ministry of Education and Science under the National Research Programme “Young scientists and postdoctoral students” approved by DCM # 577/17.08.201

Софтуерна програма за анализ на ВСЧ на кардио сигнали, регистрирани чрез електрокардиографско, холтерно и фотоплетизмографско устройство

The article presents a demonstration software program for evaluating three types of cardiac signals. Mathematical methods for analyzing electrocardiographic, photoplethysmographic and Holter signals are considered. The different cardio signals are analyzed from the presented information system and conclusions are drawn regarding their practical use. The article describes software procedures for the analysis and estimation of heart rate variability extracted from the heart rate registration signals.Научното изследване е проведено като част от проекта „Изследване на приложението на нови математически методи за анализ на кардиологични данни“ № КП-06-Н22/5 от 07.12.2018 г., финансиран от Фонд „Научни изследвания“ на Република България

Алгоритми за анализ на кардиологични данни, базирани на уейвлет теория

The report presents tools for analyzing cardiac data using various wavelet transforms. Algorithms for the analysis of heart rate variability, which is a dynamic, non-stationary variable, are presented. Heart rate analysis with mathematically based methods is a topical issue. Electrocardiography and long-term Holter recordings have established themselves as non-invasive medical methods for assessing cardiovascular activity. In the time domain parameters established in practice are determined. Spectral analysis of heart rate variability makes it possible to assess the work of the heart and to assess its condition in the coming days. Spectral analysis is usually performed in three frequency bands and can be done by different mathematical methods. The analyzes were performed on real long-term Holter records for patients with proven heart disease diagnosed by a cardiologist and for people without cardiovascular problems. The presented numerical and graphical results were obtained with the help of the MATLAB software program. Comparative analyzes show differences in the studied frequency parameters between patients with heart disease and healthy individuals. The research performed and the results obtained can be useful in the clinical practice of cardiologists.Научното изследване е проведено като част от проекта „Изследване на приложението на нови математически методи за анализ на кардиологични данни“ № КП06-Н22/5 от 07.12.2018 г., финансиран от Фонд „Научни Изследвания“

Предварителна обработка и математически анализ на PPG сигнали

The report presents the main research trends in the preprocessing and mathematical analysis of photoplethysmographic signals. The use of PPG in recent years has grown in parallel with the deepening penetration of modern technology in people's daily lives. At the same time, the modernization of technology has led to the miniaturization of optical sensors. A detailed overview of the state of PPG technology today and the possibilities of using PPG sensors for reporting and long-term monitoring of the health of people in their daily lives is offered. The report considers methods for noise reduction in photoplethysmographic signals based on the use of discrete wavelet transform and threshold processing of the obtained coefficients. A comparison is made between the presented methods on the basis of evaluation parameters.Научното изследване е проведено като част от проекта „Изследване на приложението на нови математически методи за анализ на кардиологични данни“ № КП-06-Н22/5 от 07.12.2018 г., финансиран от фонд „научни изследвания“

Приложение на линейни методи за анализ на вариабилността на сърдечната честота

The report presents the results of mathematical analyzes of patients with heart failure, ischemic heart disease and a healthy control group. The studies were performed in the time and frequency domains, using linear methods on the heart rate variability of real holter records. The obtained results show significant differences between the obtained parameters in diseased and healthy individuals.Научното изследване е проведено като част от проекта „Изследване на приложението на нови математически методи за анализ на кардиологични данни“ № КП06-Н22/5 от 07.12.2018 г., финансиран от Фонд „Научни Изследвания“

ARIMA модел за прогнозиране на цената на електроенергията на пазара ден напред

Electricity price forecasting becomes a significant challenge on a day-to-day basis and price variations are even more volatile on an hourly basis. Therefore, this paper is used several approaches to analyze the Bulgarian hourly electricity price dynamics in the day-ahead market. Proper analysis crucially depends on the choice of an adequate model. Reviewed are the factors which may influence the electricity spot prices and characteristics of the time series of prices. Methods include and variety of modeling approaches that are applied and evaluated for forecasting electricity prices such as time-series models and regression models. The forecasting technique is to model day-ahead spot prices using well known ARIMA/SARIMA model including stationarity checks, seasonal decompose, differencing, autoregressive modeling, and autocorrelation to analyze and forecast time series hourly data. For each approach, model estimates and forecasts are developed using hourly price data, reshaped, and aggregated data on a daily and monthly basis for the Bulgarian day-ahead market

Създаване на архив на кардиологична база данни

The paper presents an information platform for working with cardiac data registered with ECG, PPG, Holter device. The platform enables input, editing, processing and storage of the registered data and the results of the analyzes carried out on them. The report presents the options for creating an archive of the cardiology database.Научното изследване е проведено като част от проекта „Изследване на приложението на нови математически методи за анализ на кардиологични данни“ № КП-06-Н22/5 от 07.12.2018 г., финансиран от Фонд „Научни изследвания“ на Република България

Прогнозиране на цените на електроенергията ден напред чрез рекурентна невронна мрежа за изследване с дългосрочна-краткосрочна памет

The availability of accurate day-ahead electricity price forecasts is very important for electricity market participants and it is an essential challenge to accurately forecast the electricity price. Therefore, this study proposes an efficient method suitable for electricity price forecasting (EPF) and processing time-series data from the Bulgarian day-ahead market based on a long-short term memory (LSTM) recurrent neural network model. The LSTM model is used to forecast the day-ahead electricity price for the Bulgarian day-ahead market. As inputs to the model are used historical hourly prices for the period between 20.01.2016 and 05.03.2022. The output is the electricity price forecasts for hours and days ahead. The future values of prices are forecasted recursively. LSTM can model temporal dependencies in larger Time Series set horizons without forgetting the short-term patterns. LSTM networks are composed of units that are called LSTM memory cells and these cells contain some gates that process the inputs. Since electricity price is affected by various seasonal effects, the model is trained for several years. The effectiveness of the proposed method is verified using real market data

Interactive Cardio System for Healthcare Improvement

The paper presents an interactive cardio system that can be used to improve healthcare. The proposed system receives, processes, and analyzes cardio data using an Internet-based software platform. The system enables the acquisition of biomedical data using various means of recording cardiac signals located in remote locations around the world. The recorded discretized cardio information is transmitted to the system for processing and mathematical analysis. At the same time, the recorded cardio data can also be stored online in established databases. The article presents the algorithms for the preprocessing and mathematical analysis of cardio data (heart rate variability). The results of studies conducted on the Holter recordings of healthy individuals and individuals with cardiovascular diseases are presented. The created system can be used for the remote monitoring of patients with chronic cardiovascular diseases or patients in remote settlements (where, for example, there may be no hospitals), control and assistance in the process of treatment, and monitoring the taking of prescribed drugs to help to improve people’s quality of life. In addition, the issue of ensuring the security of cardio information and the confidentiality of the personal data of health users is considered.</jats:p

Cardio-Diagnostic Assisting Computer System

The mathematical analysis and the assessment of heart rate variability (HRV) based on computer systems can assist the diagnostic process with determining the cardiac status of patients. The new cardio-diagnostic assisting computer system created uses the classic Time-Domain, Frequency-Domain, and Time-Frequency analysis indices, as well as the nonlinear methods (Poincar&eacute; plot, Recurrence plot, Hurst R/S method, Detrended Fluctuation Analysis (DFA), Multi-Fractal DFA, Approximate Entropy and Sample Entropy). To test the feasibility of the software developed, 24-hour Holter recordings of four groups of people were analysed: healthy subjects and patients with arrhythmia, heart failure and syncope. Time-Domain (SDNN &lt; 50 ms, SDANN &lt; 100 ms, RMSSD &lt; 17 ms) and Frequency-Domain (the spectrum of HRV in the LF &lt; 550 ms2, and HF &lt; 540 ms2) parameter values decreased in the cardiovascular disease groups compared to the control group as a result of lower HRV due to decreased parasympathetic and increased sympathetic activity. The results of the nonlinear analysis showed low values of (SD1 &lt; 56 ms, SD2 &lt; 110 ms) at Poincar&eacute; plot (Alpha &lt; 90 ms) at DFA in patients with diseases. Significantly reducing these parameters are markers of cardiac dysfunction. The examined groups of patients showed an increase in the parameters (DET% &gt; 95, REC% &gt; 38, ENTR &gt; 3.2) at the Recurrence plot. This is evidence of a pathological change in the regulation of heart rhythm. The system created can be useful in making the diagnosis by the cardiologist and in bringing greater accuracy and objectivity to the treatment