Person: AKBULUT, FATMA PATLAR
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Dr. Öğr. Üyesi
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AKBULUT
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FATMA PATLAR
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Publication Metadata only A smart wearable system for short-term cardiovascular risk assessment with emotional dynamics(Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1Gb, Oxon, England, 2018-11) Akan, Aydın; AKBULUT, FATMA PATLAR; 2918Recent innovative treatment and diagnostic methods developed for heart and circulatory system disorders do not provide the desired results as they are not supported by long-term patient follow-up. Continuous medical support in a clinic or hospital is often not feasible in elderly or aging populations; yet, collecting medical data is still required to maintain a high-quality of life. In this study, a smart wearable system design called Cardiovascular Disease Monitoring (CVDiMo), which provides continuous medical monitoring and creates a health profile with the risk of disease over time. Systematic tests were performed with analysis of six different biosignals from two different test groups with 30 participants. In addition to examining the biosignals of patients, using the physical activity results and stress levels deduced from the emotional state analysis achieved a higher performance in risk estimation. In our experiments, the highest accuracy of determining the short-term health status was obtained as 96%.Publication Restricted Wearable Sensor-Based Evaluation of Psychosocial Stress in Patients With Metabolic Syndrome(Elsevier, 2020) AKBULUT, FATMA PATLAR; İkitimur, Barış; Akan, AydınThe prevalence of metabolic disorders has increased rapidly as such they become a major health issue recently. Despite the definition of genetic associations with obesity and cardiovascular diseases, they constitute only a small part of the incidence of disease. Environmental and physiological effects such as stress, behavioral and metabolic disturbances, infections, and nutritional deficiencies have now revealed as contributing factors to develop metabolic diseases. This study presents a multivariate methodology for the modeling of stress on metabolic syndrome (MES) patients. We have developed a supporting system to cope with MES patients' anxiety and stress by means of several biosignals such as ECG, GSR, body temperature, SpO(2), glucose level, and blood pressure that are measured by a wearable device. We employed a neural network model to classify emotions with HRV analysis in the detection of stressor moments. We have accurately recognized the stressful situations using physiological responses to stimuli by utilizing our proposed affective state detection algorithm. We evaluated our system with a dataset of 312 biosignal records from 30 participants and the results showed that our proposed method achieved an average accuracy of 92% and 89% in distinguishing stress level in MES and other groups respectively. Both being the focus of an MES group and others proved to be highly arousing experiences which were significantly reflected in the physiological signal. Exposure to the stress in MES and cardiovascular heart disease patients increases the chronic symptoms. An early stage of comprehensive intervention may reduce the risk of general cardiovascular events in these particular groups. In this context, the use of e-health applications such as our proposed system facilitates these processes.Publication Metadata only Smart wearable patient tracking systems(IEEE, 345 E 47th St, New York, Ny 10017 USA, 2015) Akan, Aydın; AKBULUT, FATMA PATLAR; 2918Nowadays Smart Wearable Systems (SWS) subject, emerges as one of the most popular research topic. Becouse of being comprimised from multiple disciplines, this area is favored by researchers of different areas. Devices of SWS are designed to measure physiological parameters for illnesses, chronic diseases, daily activity monitoring and other purposes, now it is finding more and more place in our lives. We can see the effects of SWS in health, sports, entertainment, education, business and other fileds, especially for the projects of health sector many products are available to the end users. Different SWS that are classified according to its features and methods of use have common features such as ability to make high-capacity wireless communication and housing embedded sensors. When invetigating the studies of SWS that include various forms and numbers of sensors, it is seen that most preferred sensors are; ECG sensor, Glucose sensor, body temperature sensor, blood pressure sensor, SpO(2) sensor and Accelerometer. General details of SWS and leading research projects from past to present are described within the scope of this article.Publication Embargo A decision support system to determine optimal ventilator settings(Biomed Central Ltd, 236 Grays Inn Rd, Floor 6, London Wc1X 8Hl, England, 2014) Akkur, Erkan; Akan, Aydın; Yarman, B. Sıddık; AKBULUT, FATMA PATLARBackground: Choosing the correct ventilator settings for the treatment of patients with respiratory tract disease is quite an important issue. Since the task of specifying the parameters of ventilation equipment is entirely carried out by a physician, physician ' s knowledge and experience in the selection of these settings has a direct effect on the accuracy of his/her decisions. Nowadays, decision support systems have been used for these kinds of operations to eliminate errors. Our goal is to minimize errors in ventilation therapy and prevent deaths caused by incorrect configuration of ventilation devices. The proposed system is designed to assist less experienced physicians working in the facilities without having lung mechanics like cottage hospitals. Methods: This article describes a decision support system proposing the ventilator settings required to be applied in the treatment according to the patients ' physiological information. The proposed model has been designed to minimize the possibility of making a mistake and to encourage more efficient use of time in support of the decision making process while the physicians make critical decisions about the patient. Artificial Neural Network (ANN) is implemented in order to calculate frequency, tidal volume, FiO(2) outputs, and this classification model has been used for estimation of pressure support /volume support outputs. For the obtainment of the highest performance in both models, different configurations have been tried. Various tests have been realized for training methods, and a number of hidden layers mostly affect factors regarding the performance of ANNs. Results: The physiological information of 158 respiratory patients over the age of 60 and were treated in three different hospitals between the years 2010 and 2012 has been used in the training and testing of the system. The diagnosed disease, core body temperature, pulse, arterial systolic pressure, diastolic blood pressure, PEEP, PSO2, pH, pCO(2), bicarbonate data as well as the frequency, tidal volume, FiO(2), and pressure support / volume support values suitable for use in the ventilator device have been recommended to the physicians with an accuracy of 98,44%. Performed experiments show that sequential order weight/bias training was found to be the most ideal ANN learning algorithm for regression model and Bayesian regulation backpropagation was found to be the most ideal ANN learning algorithm for classification models. Conclusions: This article aims at making independent of the choice of parameters from physicians in the ventilator treatment of respiratory tract patients with proposed decision support system. The rate of accuracy in prediction of systems increases with the use of data of more patients in training. Therefore, non-physician operators can use systems in determination of ventilator settings in case of emergencies.