Rafał Kotas

Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks

This paper presents a decision support system that aims to estimate a patient׳s general condition and detect situations which pose an immediate danger to the patient׳s health or life. The use of this system might be especially important in places such as accident and emergency departments or admission wards, where a small medical team has to take care of many patients in various general conditions. Particular stress is laid on cardiovascular and pulmonary conditions, including those leading to sudden cardiac arrest. The proposed system is a stand-alone microprocessor-based device that works in conjunction with a standard vital signs monitor, which provides input signals such as temperature, blood pressure, pulseoxymetry, ECG, and ICG. The signals are preprocessed and analysed by a set of artificial intelligence algorithms, the core of which is based on Bayesian networks. The paper focuses on the construction and evaluation of the Bayesian network, both its structure and numerical specification.

Evaluation of the impact of atmospheric pressure in different seasons on blood pressure in patients with arterial hypertension.

OBJECTIVES Atmospheric pressure is the most objective weather factor because regardless of if outdoors or indoors it affects all objects in the same way. The majority of previous studies have used the average daily values of atmospheric pressure in a bioclimatic analysis and have found no correlation with blood pressure changes. The main objective of our research was to assess the relationship between atmospheric pressure recorded with a frequency of 1 measurement per minute and the results of 24-h blood pressure monitoring in patients with treated hypertension in different seasons in the moderate climate of the City of Łódź (Poland). MATERIAL AND METHODS The study group consisted of 1662 patients, divided into 2 equal groups (due to a lower and higher average value of atmospheric pressure). Comparisons between blood pressure values in the 2 groups were performed using the Mann-Whitney U test. RESULTS We observed a significant difference in blood pressure recorded during the lower and higher range of atmospheric pressure: on the days of the spring months systolic (p = 0.043) and diastolic (p = 0.005) blood pressure, and at nights of the winter months systolic blood pressure (p = 0.013). CONCLUSIONS A significant inverse relationship between atmospheric pressure and blood pressure during the spring days and, only for systolic blood pressure, during winter nights was observed. Int J Occup Med Environ Health 2016;29(5):783-792.

Olfactory event-related potentials recordings analysis based on modified EEG registration system

An external effect of the smelling process can be measured as olfactory evoked potentials (OEP). The amplitude of the potentials is in the range of single microvolts. Unfortunately, the precision of such potential measures is low, because of existing “body” noises. Additionally, other brain activities and even human emotion variations can distort the result. Therefore, further researches of electrical properties must be continued. Todays measurement methods of olfactory evoked potentials need several repetition cycles to obtain average values of the necessary potential. Because it is necessary to take into account olfactory weariness (attenuation), a repetition frequency is low and as a consequence, a patient survey is long (usually 20-30 min. using existing equipment).

Decision Support System for Real-Time Monitoring of Patients with Cardiovascular Risks

This paper presents decision support system for estimation of patient’s general state with particular stress laid on cardiovascular and pulmonary diseases. It works in conjunction with a standard medical monitor, which provides input signals such as temperature, blood pressure, pulseoxymetry, ECG, ICG, etc. The signals are preprocessed and analysed by a set of algorithms, the core of which is based on Bayesian networks. As an output the system should give information about detected problems, possible future threats, possible causes and suggestions for further treatment. From the hardware point of view the system is implemented in DaVinci DM3730 embedded microprocessor. Use of the presented system might be especially important in places such as Accident Emergency Departments and Admission Rooms, where small medical team has to take care of many patients in various general conditions.

System for Estimation of Patient’s State – Discussion of the Approach

This chapter presents work on a diagnosis support system with continuous estimation of a sudden cardiac arrest risk. The concept is based on the analysis of the signals received on line from a medical monitor, possibly augmented by clinical data. The need for such solution is motivated and existing approaches towards quantitative estimation of patient’s health are examined. An overview of the system and its building blocks are presented, which allows to define the requirements for the hardware platform and operating system. A discussion follows, explaining the choice made. The design of communication path between the medical monitor decided upon and the discussed system is explained. The prototyping microprocessor board being used is presented, together with detailed description of steps which lead to the development of a fully-featured Linux distribution for this board.

Control and Monitoring System for Composite Materials Fabrication Based on PPS Method

Cubic boron nitride (cBN) is commonly used as a cutting tools material for the sake of its high hardness and wear-resistance. But on the other hand cBN, it requires application of an expensive HPHT (High Pressure High Temperature) to avoid the transformation of cBN into low-hardness h-BN, they must be sintered under a pressure of 5 to 6 GPa which substantially increases their production costs. An alternative method, called PPS (Pulse Plasma Sintering), was proposed to decrease the production costs. The PPS significantly decreases the costs of production and therefore it is very attractive for industry. However, automation and dedicated control system for PPS is required to carefully control parameters and hamper the transformation of cBN into hBN. Only the application of automated production line guarantee high-quality of cubic boron nitride samples. The paper discusses the requirements and presents the architecture of the control system dedicated for pulse plasma sintering system.

Methods of 3D images quality assesment

The article presents the theme of stereoscopic images quality assessment. The first part of the paper describes the definition of stereoscopy and provides a preliminary discussion of the issue. The second part of the paper lists a set of commonly used metrics for the evaluation of 3D image divided into subjective and objective metrics, and these in turn, based on the analogical 2D metrics and metrics created specifically for stereoscopic images.

Control and monitoring system prototype for pulse plasma sintering process

Cubic boron nitride is commonly used as a cutting tools material for the sake of its high hardness and wear-resistance. Pulse Plasma Sintering was proposed to decrease the production costs. This method is very attractive for industry, because it significantly decreases the costs of production. The goal of this paper is to present the control system prototype for Pulse Plasma Sintering processes based on programmable logic controller. Only sintering process with carefully controlled parameters allows to restrict the transformation of cBN into hBN and guarantee production of high-quality cubic boron nitride samples. Thus a dedicated control system was developed to control parameters and therefore to improve the production process. This paper presents also a visualisation system which allows the operator to control, monitor and analyze the production process. Prepared application consists of manual and auto operating mode.

Implementation of artificial intelligence methods on example of cardiovascular diseases risk stratification

The paper presents implementation of artificial intelligence methods on the example of the risk stratification of cardiovascular diseases. In the research were used four methods: artificial neural networks, Bayesian networks, decision trees and simple classification models. The obtained results give us possibility to evaluate automatically a health state of the patient and can be helpful in further decisions concerning medical treatment. The obtained results of our research can be also helpful in diagnosis procedures for evaluation of the probability of sudden cardiac arrest.