Designing and application of a new medical instrument sterilization system using reactive oxygen species.

The aim of this study is to design and test a new medical sterilization system as an alternative to the techniques used in the sterilization of medical instruments. The designed system, which uses a new oxygen molecule allotrope (NOMA) in the reactive oxygen species, is developed as an alternative to the sterilization systems using other gases. The test was conducted on 12 different materials, each having a surface of 2 cm2, sterilized under 120 °C at 1 atm pressure for 20 min in the NUVE-OT 4060 sterilizer, and all surfaces were contaminated with a biological indicator Geobacillus stearothermophilus cultured in an incubator at 37 °C. Test samples in sterile Petri dishes were placed in a desiccator, and a sample was taken at 30-, 45-, and 60-min test periods and were placed on an agar medium and put in a Memmert incubator IN75 at 37 °C; in the controls conducted following 16-18 h of incubation period, no bacterial growth was observed in the newly designed system tested with gram positive bacilli; moreover, it was observed that the new system was 100% effective in sterilizing the microbes as no growth was observed on the samples. Within the scope of this study, a faster, low-cost, low-temperature, residue-free, and human and environmental friendly system was developed and tested for sterilization of medical devices compared to existing sterilization methods using NOMA.

P300-Based Asynchronous Brain Computer Interface for Environmental Control System.

An asynchronous brain computer interface (A-BCI) determines whether or not a subject is on control state, and produces control commands only in case of subject's being on control state. In this study, we propose a novel P300-based A-BCI algorithm that distinguishes control state and noncontrol state of users. Furthermore, A-BCI algorithm combined with a dynamic stopping function that enables users to select control command independent from a fixed number of intensification sequence. The proposed P300-based A-BCI algorithm uses classification patterns to determine control state and uses optimal operating point of receiver operating characteristics curve for dynamic stopping function. The proposed A-BCI algorithm is also combined with region-based paradigm (RBP) based stimulus interface. The A-BCI algorithm is tested on an internet-based environmental control system. A total of ten nondisabled subjects were participated voluntarily in the experiments. Two-level approach of the RBP-based stimulus interface improves noncontrol state detection accuracy up to 100%. Besides, ratio of incorrect command selection at control state is reduced significantly. At control state, ratio of correct selections, incorrect selections, and missed selections are 93.27%, 1.09%, and 5.63%, respectively. On the other hand, dynamic stopping function enables command selections at least two intensifications. Mean number of intensification sequences to complete the given tasks is 3.15. Thanks to dynamic stopping function, it provides a significant improvement on information transfer rate. The proposed A-BCI algorithm is important in terms of practical BCI systems.

The Use of Stepper Motor-Controlled Proportional Valve for Fio2 Calculation in the Ventilator and its Control with Fuzzy Logic.

This article proposes the employment of a proportional valve that can calculate the amount of oxygen in the air to be given to patient in accordance with the amount of FiO2 which is set from the control menu of the ventilation device. To actualize this, a stepper motor-controlled proportional valve was used. Two counts of valves were employed in order to control the gases with 2 bar pressure that came from both the oxygen and medical air tanks. Oxygen and medical air manometers alongside the pressure regulators were utilized to perform this task. It is a fuzzy-logic-based controller which calculates at what rate the proportional valves will be opened and closed for FiO2 calculation. Fluidity and pressure of air given by the ventilation device were tested with a FlowMeter while the oxygen level was tested using the electronic lung model. The obtained results from the study revealed that stepper motor controlled proportional valve could be safely used in ventilation devices. In this article, it was indicated that fluidity and pressure control could be carried out with just two counts of proportional valve, which could be done with many solenoid valves, so this reduces the cost of ventilator, electrical power consumed by the ventilator, and the dimension of ventilator.

Design and Construction of a Microcontroller-Based Ventilator Synchronized with Pulse Oximeter.

This study aims to introduce a novel device with which mechanical ventilator and pulse oximeter work in synchronization. Serial communication technique was used to enable communication between the pulse oximeter and the ventilator. The SpO2 value and the pulse rate read on the pulse oximeter were transmitted to the mechanical ventilator through transmitter (Tx) and receiver (Rx) lines. The fuzzy-logic-based software developed for the mechanical ventilator interprets these values and calculates the percentage of oxygen (FiO2) and Positive End-Expiratory Pressure (PEEP) to be delivered to the patient. The fuzzy-logic-based software was developed to check the changing medical states of patients and to produce new results (FiO2 ve PEEP) according to each new state. FiO2 and PEEP values delivered from the ventilator to the patient can be calculated in this way without requiring any arterial blood gas analysis. Our experiments and the feedbacks from physicians show that this device makes it possible to obtain more successful results when compared to the current practices.

Implementation of an Embedded Web Server Application for Wireless Control of Brain Computer Interface Based Home Environments.

Brain Computer Interface (BCI) based environment control systems could facilitate life of people with neuromuscular diseases, reduces dependence on their caregivers, and improves their quality of life. As well as easy usage, low-cost, and robust system performance, mobility is an important functionality expected from a practical BCI system in real life. In this study, in order to enhance users' mobility, we propose internet based wireless communication between BCI system and home environment. We designed and implemented a prototype of an embedded low-cost, low power, easy to use web server which is employed in internet based wireless control of a BCI based home environment. The embedded web server provides remote access to the environmental control module through BCI and web interfaces. While the proposed system offers to BCI users enhanced mobility, it also provides remote control of the home environment by caregivers as well as the individuals in initial stages of neuromuscular disease. The input of BCI system is P300 potentials. We used Region Based Paradigm (RBP) as stimulus interface. Performance of the BCI system is evaluated on data recorded from 8 non-disabled subjects. The experimental results indicate that the proposed web server enables internet based wireless control of electrical home appliances successfully through BCIs.

Region based Brain Computer Interface for a home control application.

Environment control is one of the important challenges for disabled people who suffer from neuromuscular diseases. Brain Computer Interface (BCI) provides a communication channel between the human brain and the environment without requiring any muscular activation. The most important expectation for a home control application is high accuracy and reliable control. Region-based paradigm is a stimulus paradigm based on oddball principle and requires selection of a target at two levels. This paper presents an application of region based paradigm for a smart home control application for people with neuromuscular diseases. In this study, a region based stimulus interface containing 49 commands was designed. Five non-disabled subjects were attended to the experiments. Offline analysis results of the experiments yielded 95% accuracy for five flashes. This result showed that region based paradigm can be used to select commands of a smart home control application with high accuracy in the low number of repetitions successfully. Furthermore, a statistically significant difference was not observed between the level accuracies.

Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks.

Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema.

A low-cost mobile adaptive tracking system for chronic pulmonary patients in home environment.

OBJECTIVE: The main objective of this study is presenting a real-time mobile adaptive tracking system for patients diagnosed with diseases such as asthma or chronic obstructive pulmonary disease and application results at home. The main role of the system is to support and track chronic pulmonary patients in real time who are comfortable in their home environment. It is not intended to replace the doctor, regular treatment, and diagnosis. MATERIALS AND METHODS: In this study, the Java 2 micro edition-based system is integrated with portable spirometry, smartphone, extensible markup language-based Web services, Web server, and Web pages for visualizing pulmonary function test results. The Bluetooth(®) (Bluetooth SIG, Kirkland, WA) virtual serial port protocol is used to obtain the test results from spirometry. General packet radio service, wireless local area network, or third-generation-based wireless networks are used to send the test results from a smartphone to the remote database. The system provides real-time classification of test results with the back propagation artificial neural network algorithm on a mobile smartphone. It also provides the generation of appropriate short message service-based notification and sending of all data to the Web server. In this study, the test results of 486 patients, obtained from Atatürk Chest Diseases and Thoracic Surgery Training and Research Hospital in Ankara, Turkey, are used as the training and test set in the algorithm. RESULTS: The algorithm has 98.7% accuracy, 97.83% specificity, 97.63% sensitivity, and 0.946 correlation values. The results show that the system is cheap (900 Euros) and reliable. CONCLUSIONS: The developed real-time system provides improvement in classification accuracy and facilitates tracking of chronic pulmonary patients.

Pulse oximeter based mobile biotelemetry application.

Quality and features of tele-homecare are improved by information and communication technologies. In this context, a pulse oximeter-based mobile biotelemetry application is developed. With this application, patients can measure own oxygen saturation and heart rate through Bluetooth pulse oximeter at home. Bluetooth virtual serial port protocol is used to send the test results from pulse oximeter to the smart phone. These data are converted into XML type and transmitted to remote web server database via smart phone. In transmission of data, GPRS, WLAN or 3G can be used. The rule based algorithm is used in the decision making process. By default, the threshold value of oxygen saturation is 80; the heart rate threshold values are 40 and 150 respectively. If the patient's heart rate is out of the threshold values or the oxygen saturation is below the threshold value, an emergency SMS is sent to the doctor. By this way, the directing of an ambulance to the patient can be performed by doctor. The doctor for different patients can change these threshold values. The conversion of the result of the evaluated data to SMS XML template is done on the web server. Another important component of the application is web-based monitoring of pulse oximeter data. The web page provides access to of all patient data, so the doctors can follow their patients and send e-mail related to the evaluation of the disease. In addition, patients can follow own data on this page. Eight patients have become part of the procedure. It is believed that developed application will facilitate pulse oximeter-based measurement from anywhere and at anytime.

[Effects of radiation emitted from mobile phones on short- term heart rate variability parameters]. / Cep telefonlarindan yayilan radyasyonun kisa dönem kalp hizi degiskenligi parametreleri üzerindeki etkileri.

OBJECTIVE: In this study, the effects of radiation emitted from mobile phone (MP) on heart rate variability (HRV) which is accepted a non-invasive indicator of autonomic nervous system (ANS) were investigated with considering the deficiency of previous studies. METHODS: A randomized controlled study has been designed and utilized with 30 young and healthy volunteers. During the experiment that had three periods, the electrocardiogram (ECG) and respiration signals were recorded and MP was attached to subjects' right ear with a bone. Ten subjects selected randomly were exposed to high -level radiation during the second period (Experimental Group 1). Ten of others were exposed during the third period with maximum level radiation (Experimental Group 2). Ten records were also made while MP was closed as a control. Short -term HRV parameters were obtained and repeated measures ANOVA and suitable post-hoc tests applied to the results. RESULTS: According to the results of the repeated measures ANOVA; there were no significant main effects of groups. However, there were some significant differences in measuring time periods and groups*period interactions. The post-hoc tests showed that mean R to R interval and HF power are significantly changed by maximum radiation emitted from MP. CONCLUSION: Due to the radiation emitted from MPs at maximum power, some changes may occur in HRV parameters that are associated with increased parasympathetic activity. But, the level of these changes is similar to daily activities as excitement, and stand up.

A survey on application of quantitative methods on analysis of brain parameters changing with temperature.

Brain temperature fluctuations occur in consequence of physiological and pathophysiological conditions and indicate changes in brain metabolism, cerebral blood flow (CBF), brain functions and neural damage. Lowering the brain temperature of patients with traumatic brain injuries achieves considerable improvements. When the human brain is cooled down to 30°C, it switches to a sub functional regime where it can live longer with less oxygen, glucose and other supplies. Fluctuations in brain temperature cause changes in brain parameters which can be measured by electroencephalogram (EEG) and transcranial Doppler (TCD). It is very important to understand the temperature dependencies of brain's electrical activity and blood flow and their interrelations considering the good clinical results achieved by lowering the brain temperature of neurologically injured patients. Since protecting the patient's brain is of primary importance in many fields including cardiology, neurology, traumatology and anesthesia it can be clearly seen that this subject is very important. In this study, we survey the "state-of-the-art" in analysis of EEG and TCD brain parameters changing with temperature and present further research opportunities.

Design of a cervical collar device to facilitate and accelerate implementation of first aid.

Frequently there are disasters all over the world-fires, earthquakes, or even some unexpected shocking catastrophes. Hence people injured, or even died. Lifesaving actions begin with the initiation of the chain of survival. With every minute that passes without medical action being taken, the probability of being able to save the patients life decreases by ten percent. After 10 min there is normally no chance of resuscitation being successful. First aid is emergency treatment given before regular medical aid can be obtained. And it is a concept of first hands-on measures performed in a medical emergency by laypersons. The major aim of this study is to develop an easy-feasible cervical collar, for facilitating and accelerating implementation of first aid especially in case of collective injuries. The developed device is different from the cervical collars which are used to treat the neck pain. In the present study, the heartbeat is obtained by detecting pulse with the stethoscope that is a part of the developed device and fixed on the carorid artery. The obtained heartbeat signal has been processed by the electronic control circuit and the used LED has given light according to the patient's life signal. Although there are some disadvantages of the developed system, the precautions for these cases have been taken and the system has been tried to design in order to operate sensibly.

Software development for the analysis of heartbeat sounds with LabVIEW in diagnosis of cardiovascular disease.

In this paper, a time-frequency spectral analysis software (Heart Sound Analyzer) for the computer-aided analysis of cardiac sounds has been developed with LabVIEW. Software modules reveal important information for cardiovascular disorders, it can also assist to general physicians to come up with more accurate and reliable diagnosis at early stages. Heart sound analyzer (HSA) software can overcome the deficiency of expert doctors and help them in rural as well as urban clinics and hospitals. HSA has two main blocks: data acquisition and preprocessing, time-frequency spectral analyses. The heart sounds are first acquired using a modified stethoscope which has an electret microphone in it. Then, the signals are analysed using the time-frequency/scale spectral analysis techniques such as STFT, Wigner-Ville distribution and wavelet transforms. HSA modules have been tested with real heart sounds from 35 volunteers and proved to be quite efficient and robust while dealing with a large variety of pathological conditions.

Multiclass support vector machines for EEG-signals classification.

In this paper, we proposed the multiclass support vector machine (SVM) with the error-correcting output codes for the multiclass electroencephalogram (EEG) signals classification problem. The probabilistic neural network (PNN) and multilayer perceptron neural network were also tested and benchmarked for their performance on the classification of the EEG signals. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and the Lyapunov exponents and classification using the classifiers trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Our research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the EEG signals and the multiclass SVM and PNN trained on these features achieved high classification accuracies.

Automated diagnostic systems with diverse and composite features for Doppler ultrasound signals.

In this paper, we present the automated diagnostic systems for Doppler ultrasound signals classification with diverse and composite features and determine their accuracies. We compared the classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLP), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features. The present study was conducted with the purpose of answering the question of whether the automated diagnostic systems improve the capability of classification of ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Our research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems.

Wavelet-based neural network analysis of internal carotid arterial Doppler signals.

In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multi-layer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect internal carotid artery stenosis and occlusion.

Suppression of impulse noise in medical images with the use of Fuzzy Adaptive Median Filter.

A new rule based fuzzy filter for removal of highly impulse noise, called Rule Based Fuzzy Adaptive Median (RBFAM) Filter, is aimed to be discussed in this paper. The RBFAM filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The filter has three stages. Two of those stages are fuzzy rule based and last stage is based on standard median and adaptive median filter. The proposed filter can preserve image details better then AMF while suppressing additive salt & pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function instead of triangular membership function in order to observe better results. Experimental results indicates that the proposed filter is improvable with increased fuzzy rules to reduce more noise corrupted images and to remove salt and pepper noise in a more effective way than what AMF filter does.

Feature extraction from Doppler ultrasound signals for automated diagnostic systems.

This paper presented the assessment of feature extraction methods used in automated diagnosis of arterial diseases. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Different feature extraction methods were used to obtain feature vectors from ophthalmic and internal carotid arterial Doppler signals. In addition to this, the problem of selecting relevant features among the features available for the purpose of classification of Doppler signals was dealt with. Multilayer perceptron neural networks (MLPNNs) with different inputs (feature vectors) were used for diagnosis of ophthalmic and internal carotid arterial diseases. The assessment of feature extraction methods was performed by taking into consideration of performances of the MLPNNs. The performances of the MLPNNs were evaluated by the convergence rates (number of training epochs) and the total classification accuracies. Finally, some conclusions were drawn concerning the efficiency of discrete wavelet transform as a feature extraction method used for the diagnosis of ophthalmic and internal carotid arterial diseases.

Determining variability of ophthalmic arterial Doppler signals using Lyapunov exponents.

The new method presented in this study was directly based on the consideration that ophthalmic arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for determining variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. The computed Lyapunov exponents of the ophthalmic arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The ophthalmic arterial Doppler signals were classified with the accuracy varying from 93.75% to 97.06%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.

Automatic detection of erthemato-squamous diseases using adaptive neuro- fuzzy inference systems.

A new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the ANFIS classifiers learned how to differentiate a new case in the domain. The six ANFIS classifiers were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. To improve diagnostic accuracy, the seventh ANFIS classifier (combining ANFIS) was trained using the outputs of the six ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of erythemato-squamous diseases were obtained through analysis of the ANFIS. The performances of the ANFIS model were evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has some potential in detecting the erythemato-squamous diseases. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.