Hasan Guler

The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation.

The aim of this article is to develop a knowledge-based therapy for management of rats with respiratory distress. A mechanical ventilator was designed to achieve this aim. The designed ventilator is called an intelligent mechanical ventilator since fuzzy logic was used to control the pneumatic equipment according to the rat’s status. LabVIEW software was used to control all equipments in the ventilator prototype and to monitor respiratory variables in the experiment. The designed ventilator can be controlled both manually and by fuzzy logic. Eight female Wistar-Albino rats were used to test the designed ventilator and to show the effectiveness of fuzzy control over manual control on pressure control ventilation mode. The anesthetized rats were first ventilated for 20 min manually. After that time, they were ventilated for 20 min by fuzzy logic. Student’s t-test for p < 0.05 was applied to the measured minimum, maximum and mean peak inspiration pressures to analyze the obtained results. The results show that there is no statistical difference in the rat’s lung parameters before and after the experiments. It can be said that the designed ventilator and developed knowledge-based therapy support artificial respiration of living things successfully.

Estimation of inspiration and expiration time by using fuzzy control with respect to lung's dynamics

In this study, estimation of inspiration and expiration time in positive pressure ventilator, which is used in intensive care units and implements patients artificial respiratory system that has been disturbed is performed with fuzzy logic controller. The goal of this system is to reduce workload of anaesthetist. Since he/she has many patients in intensive care units so he has no so much time each patients. Fuzzy logic controller determines time of inspiration and expiration. By estimating inspiration and expiration time, lungs dynamics which are resistance and compliance are considered. Performed system is run in the pressure control ventilation (PCV) which is one of the most used modes among other ventilation modes.

Wavelet-based study of valence–arousal model of emotions on EEG signals with LabVIEW

This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence–arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using “db5” wavelet function. Relative features were calculated to obtain further information. Impact of the emotions according to valence value was observed to be optimal on power spectral density of gamma band. The main objective of this work is not only to investigate the influence of the emotions on different frequency bands but also to overcome the difficulties in the text-based program. This work offers an alternative approach for emotion evaluation through EEG processing. There are a number of methods for emotion recognition such as wavelet transform-based, Fourier transform-based, and Hilbert–Huang transform-based methods. However, the majority of these methods have been applied with the text-based programming languages. In this study, we proposed and implemented an experimental feature extraction with graphics-based language, which provides great convenience in bioelectrical signal processing.

LabVIEW-based analysis of EEG signals in determination of sleep stages

The aim of this study is to create an interface for classification of sleep stages. Classification was made by examining of Electroencephalogram (EEG) that plays an important role in understanding and treatment of various diseases. The user interface was generated by means of Laboratory Virtual Instrument Engineering Workbench (LabVIEW) that has visual programming language. EEG signals in the time, frequency and time-frequency domain were analyzed by generated interface. Also EEG signals were studied by using MATLAB as well as LabVIEW. The control of interface was made by using signals that received from Firat University Hospital, signals which found in software and the generated signals by LabVIEW EEG simulator.

Heart Rate Variability (HRV) Based Feature Extraction for Congestive Heart Failure

Heart Rate Variability (HRV) signal which is providing information about variation between consecutive heartbeats has been employed for extracting the parameter related to Congestive Heart Failure (CHF) from Electrocardiography (ECG). There are several studies on HRV analysis and CHF however most of the studies were performed by text-based methods. The main objective of this work is not only search to extract parameters but also to accomplish the difficulties in the text based program. This study presents graphical programming language to investigate features of HRV. Graphical User Interface (GUI) has been developed in LabVIEW to create some method for extraction features related to CHF. HRV are analyzed in the time and frequency domains. Parameters related to the time and frequency domains are derived for healthy ECG and ECG signals having Congestive Heart Failure. Several parameters which are necessary for classification algorithms are obtained.

The Real Time Implementation of a Chaotic System’s Synchronization for Secure Communication

The chaotic systems are preferred in secure communication systems as well as used in many different implementation areas. Real time chaos synchronization is also required for secure communication systems. In this study, the Master–Slave synchronization of chaotic Chen system was performed on a real time implementation for the secure communication systems. Active control method was used in the design of controller required for the realization of synchronization. The successful realization of the Master-Slave synchronization of the Chen system by using the controller designed on MATLAB/Simulink was shown by the obtained simulation results. Moreover, by using analogue outputs of NI-DAQ card, the Master-Slave synchronization of the Chen system was obtained and secure communication was achieved on a real time basis under LabVIEW environment.

The development of a novel knowledge-based weaning algorithm using pulmonary parameters: a simulation study

Weaning is important for patients and clinicians who have to determine correct weaning time so that patients do not become addicted to the ventilator. There are already some predictors developed, such as the rapid shallow breathing index (RSBI), the pressure time index (PTI), and Jabour weaning index. Many important dimensions of weaning are sometimes ignored by these predictors. This is an attempt to develop a knowledge-based weaning process via fuzzy logic that eliminates the disadvantages of the present predictors. Sixteen vital parameters listed in published literature have been used to determine the weaning decisions in the developed system. Since there are considered to be too many individual parameters in it, related parameters were grouped together to determine acid-base balance, adequate oxygenation, adequate pulmonary function, hemodynamic stability, and the psychological status of the patients. To test the performance of the developed algorithm, 20 clinical scenarios were generated using Monte Carlo simulations and the Gaussian distribution method. The developed knowledge-based algorithm and RSBI predictor were applied to the generated scenarios. Finally, a clinician evaluated each clinical scenario independently. The Studentʼs t test was used to show the statistical differences between the developed weaning algorithm, RSBI, and the clinician’s evaluation. According to the results obtained, there were no statistical differences between the proposed methods and the clinician evaluations.

Analysis of Pulmonary and Hemodynamic Parameters for the Weaning Process by Using Fuzzy Logic Based Technology

This study aims to achieve accurate weaning process which is very important for patient under mechanical ventilation. The developed weaning algorithm was designed on LabVIEW software and fuzzy system designer part of LabVIEW software was used during to develop that algorithm. The developed weaning algorithm has six input parameters which are maximum inspiratory pressure (MIP), tidal volume on spontaneous breathing (TVS), minute ventilation (VE), heart rate, respiration per minute (RPM) and body temperature. 20 clinical scenarios were generated by using Monte-Carlo simulations and Gaussian distribution method to evaluate performance of the developed algorithm. An expert clinician was involved to this process to evaluate the each generated clinic scenario to determine weaning probability for the each patient. Student t-test for p < 0.05 was used to show statistical difference between the developed algorithm and clinician’s evaluation. According to student t-test, there is no statistical difference for 98.2 % probability between the developed algorithm and the clinician’s evaluation of weaning probability.