Mehmet Recep Bozkurt

Zero valent zinc nanoparticles promote neuroglial cell proliferation: A biodegradable and conductive filler candidate for nerve regeneration

Regeneration of nerve, which has limited ability to undergo self-healing, is one of the most challenging areas in the field of tissue engineering. Regarding materials used in neuroregeneration, there is a recent trend toward electrically conductive materials. It has been emphasized that the capacity of conductive materials to regenerate such tissue having limited self-healing ability improves their clinical utility. However, there have been concerns about the safety of materials or fillers used for conductance due to their lack of degradability. Here, we attempt to use poly(Ɛ-caprolactone) (PCL) matrix consisting of varying proportions of zero valent zinc nanoparticles (Zn NPs) via electrospinning. These conductive, biodegradable, and bioactive materials efficiently promoted neuroglial cell proliferation depending on the amount of Zn NPs present in the PCL matrix. Chemical characterizations indicated that the incorporated Zn NPs do not interact with the PCL matrix chemically and that the Zn NPs improved the tensile properties of the PCL matrix. All composites exhibited linear conductivity under in vitro conditions. In vitro cell culture studies were performed to determine the cytotoxicity and proliferative efficiency of materials containing different proportions of Zn NPs. The results were obtained to explore new conductive fillers that can promote tissue regeneration.

Investigation of effects of time domain features of the photoplethysmography (PPG) signal on sleep respiratory arrests

Obstructive Sleep Apnea Syndrome is one of the major diseases of our century. Diagnosis of this disease is very difficult The reason of the difficulty is the complexity of the system. AHI index is calculated for the diagnosis of the disease as a result of respiratory scoring process. However it is necessary to use four different signals to do this operation. The reduction of signal measurements that discomforts the patient or using different signal measurements will provide the patient to sleep closer to his/her natural sleep environment which will increase the accuracy rate of the studies that are made in literature. Changes occurring in photoplethysmography signal during respiratory events were examined. In this study, the patient data that was used respiratory events were scored. Changes on the photoplethysmography signals were examined The data that were used in this study were respiratory events tagged apnea and hypopnea. For control, the photoplethysmography signals were recorded during normal breathing in sleep. The data were analyzed using the one-way analysis of variance method According to the obtained results, the photoplethysmography signals have significant changes in between the apnea-hypopnea classes and normal classes during respiratory pauses in sleep. However, there are not significant differences between apnea and hypopnea classes. The study concluded that, in the scoring of respiratory events, photoplethysmography could be used more efficiently using a computer software.

The Detection of Normal and Epileptic EEG Signals using ANN Methods with Matlab-based GUI

is common neurological disorder disease in the world. Electroencephalogram (EEG) can provide significant information about epileptic activity in human brain. Since detection of the epileptic activity requires analyzing of very length EEG recordings by an expert, researchers tend to improve automated diagnostic systems for epilepsy in recent years. In this work, we try to automate detection of epilepsy using EEG based on Matlab Graphical User Interface (GUI). Three different types of Artificial Neural Networks (ANN), namely, Feed Forward Backpropagation, Cascade and Elman neural networks, are used for the classification EEG (existence of epileptic seizure or not). Before classification process, we use autoregressive model to data reduction and three different AR model algorithms to calculate the coefficients. Developed Matlab-based GUI provides flexible and visual utilization to observe normal/epileptic EEG and test results. Training parameters and type of neural networks are decided by users on the interface. Performance of the proposed model is evaluated using overall accuracy.

A Survey of SSR Mechanism and Application

This article aims to introduce readers to a recent research topic, sympathetic skin response (SSR). The article provides information on the historical development of sympathetic response since the late 19th century. The article also includes information on the identification of naturally existed amplitude and latency values and classification based on these values, international measurement techniques with the studies conducted within the last hundred years, the factors effecting these measurements according to the studies conducted, and criteria used in the evaluation of the data obtained. In addition, information is given about the studies that have been made.

Statistical analysis of heart rate variability during abnormal respiratory events in obstructive sleep apnea patients

Obstructive Sleep Apnea is a disease that occurs due to respiratory arrest in sleep. The diagnosis of the disease is made with the polysomnography device, which is the gold standard method of diagnosis. Diagnosis is performed by sleep staging and respiratory scoring steps. Respiratory scoring is performed with at least four signals. Technical knowledge is required to connect the electrodes. Moreover, the electrodes are so disturbing that it will delay the patients sleeping time. Alternative systems for polysomnography devices are needed to solve these problems. In the study, Heart Rate Variable (HRV) derived from the photoplethysmographic signal was proposed for respiratory scoring. For the study, 15 features were extracted from HRV. Mann-Whitney U Test was used to determine whether the extracted features were distinguishing for apnea and control groups. According to the results, p < 0.05 belongs to properties 1–9 and 13–15, and these properties were found to be distinctive for the groups. According to the results of the study, respiratory scoring can be performed more practically by using HRV.

Statistical investigation of heart rate variable derived from photoplethysmography signal in sleep staging process

Obstructive Sleep Apnea diagnosis is diagnosed by a specialist doctor, examining the records taken with the polysomnography device. Sleep staging is the first and most important step in diagnosis. In the second step, diagnose with respiratory scoring. Sleep staging can be performed with at least three signals and ten channel electrodes. The electrodes are can be connected only by a qualified technician to the patient. Electrode connection patterns disrupt the patients natural sleep environment. In addition, diagnosis can be made only in the laboratory. There is a need for practical measurement systems to solve these problems. In this study, it is suggested that Heart Rate Variable (HRV) derived from Photopethemography (PPG) signal can be used in sleep staging. In the study, HRV was derived from the PPG signal. 15 features were extracted from the HRV in the time domain. Mann-Whitney U Test was used for statistical analysis and distinctive features have been identified for sleep arousal. Mann-Whitney U Test was used to determine whether the extracted features were a distinguishing feature for the Sleep Awakening status. When the results are examined, it was determined that p < 0.05 for all features discriminant for sleep wakefulness. When the results are examined, HRV derived from the PPG signal can be used to develop a sleep staging system which can be used at home.

Utilizing photophlethysmography signal features and implementing a decision tree to determine sleep cycles-non sleep cycles

Due to the intolerable diagnosis procedures of Obstructive sleep apnea syndrome, there are studies conducted for a more tolerable procedure that would comfort the patients. In our study, we used photoplethysmography signal, which is a signal that is easily detected from the patients body to determine whether the patient is in a sleep stage or a non-sleep stage. We demonstrated the patients sleep stage and non- sleep stage with a decision tree application. The resulting high success rates proved that photoplethysmography signals can be clinically used for sleep staging and decision tree applications are a reliable tool to detect sleep stages.

Sleep staging using photoplethysmography signal and kNN nearest neighbor algorithm

Diagnosis of obstructive sleep apnoea disease is carried out through devices that use Polysomnography method. In the polysomnography method, electroencephalography, electrooculography, electromyography, oral-nasal air flow, torako-abdominal movements, oxygen saturation, electrocardiography, and body position measurements are used as standard parameters. After all these parameters are studied carefully, processes of sleep staging and respiration scoring are realized. Due to the fact that the requirements of polysomnography is too much, that it requires expert personnel, and that they are not suitable to use for houses their natural measurement environments, a new system asking for less requirements is needed. In the diagnosis of the disease, using systems with Photoplethysmography signals are projected to meet the requirements of these systems. Photoplethysmography signals can be measured from any point on skin electrooptically through a non-invasive method. This study visit photoplethysmography sleep staging phase signal of the kNN classification method performed using nearby landscapes en aimed neighborhood algorithm. In the classification method, the nearest neighbor total k = 1, 2, 5, 10, 20 value were repeated work inside. In the results obtained, sleep and wakefulness can be determined with an accuracy rate of 89.46%. The sensitivity value concerning classification was found to be 0.9205, and the specificity value was found to be 0.8719. These results show that photoplethysmography signals can be an alternative for sleep staging.

Alternatively new signal for sleep staging processing in patients with Obstructive Sleep Apnea: Photoplethysmography signal

Diagnosis of Obstructive Sleep Apnea is done by expert doctors by examining biological signals which is obtain from the patient help of polysomnography device. Review, consists of two stages which are sleep staging and respiratory scoring. Sleep staging is done using Electroencephalogram, Electromyogram and Electrooculogram signals. Derivation of signal format gives discomfort to the patient. In order to connect the electrodes to the patient, there is a need expert technicians. In addition, the system is not suitable for use at home. When considering all these disadvantages, practical system is needed to make sleep staging. In this study, Photoplethysmography signal use will be suggested for alternative to the signals used in sleep staging process. Photoplethysmography signal can measure through the skin of any part of the body with noninvasive method. In the study, the characteristic features of Photoplethysmography signals were analyzed whether it is distinctive for sleep and wake statistically by means of Mann-Whitney U Test. According to the results obtained p <; 0.05 and all properties are meaningful for sleep-wake. All features can be used as distinctive for the sleep-wakefulness are considered and also a practical sleep staging system be realized using Photoplethysmography signal.

Retinal Nerve Fiber Layer detection at Optical Coherence Tomography image for the diagnosis of glaucoma

Glaucoma is characterized by increased intraocular pressure, retinal ganglion cell degeneration, optic disc cupping and visual field loss forming is a chronic optic neuropathy. Glaucoma is an irreversible worldwide and the leading cause of preventable blindness. In this study we worked on an algorithm to detect the Retinal Nerve Fiber Layer (RNFL) thickness using Optical Coherence Tomography (OCT) images the glaucoma at image processing techniques.