Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
Browse
9 results
Search Results
Item Detection of Visual Impairment From Retinal Fundus Images with Deep Learning(2022) Olcer, Didem; Erdas, Cagatay BerkeItem Prediction of Frailty Grade Using Machine Learning Models(2022) Erdas, Cagatay Berke; Olcer, DidemNowadays, frailty is becoming a major issue for the aging population. Frailty grading is important for patient quality of life because it is a geriatric syndrome of decreased physiological reserve that leads to increased susceptibility to physical stress factors and susceptibility to cardiovascular diseases. In this context, this study seeks a solution to the fragility rating regression problem with K Nearest Neighbor, Decision Tree, Random Forest, Extra Trees and CatBoost models using time domain features extracted from the triaxial accelerometer signals collected during the TUG test. Moreover, estimating the grade of frailty involved in addition to diagnosing people with the disease will provide physicians with more detailed information about the patient and allow accurate and effective treatment/supportive treatment.Item A Machine Learning Based Approach to Detect Survival of Heart Failure Patients(2020) Erdas, Cagatay Berke; Olcer, DidemOne of the diseases with high prevalence among the consequences of cardiovascular diseases is heart failure. Heart failure is a condition in which the muscles in the heart wall become faded and dilated, limiting the heart's ability to pump blood. As time passes, the heart cannot meet the proper blood requirement in the body, and as a result, the person has difficulty breathing. As the human age increases, the incidence of heart failure gradually increases, and the rate of mortality due to heart failure also increases. In this context, close monitoring of people suffering from this disease will significantly increase the survival rate. In this study, a machine learning-based system is proposed to predict the mortality-survival status of patients with heart failure. Thus, by identifying people with mortality risk, the survival probability of the patients may increase with more effective and close follow-up.Item A Deep Learning Based Approach to Detect Neurodegenerative Diseases(2020) Erdas, Cagatay Berke; Sumer, Emre; AGA-5711-2022Studies conducted by the World Health Organization (WHO) show that more than a billion people worldwide suffer from neurological disorders and the lack of effective diagnostic procedures affects treatment. Characterizing specific motor symptoms to facilitate their diagnosis can he useful in monitoring disease progression and effective treatment planning. Classification of highly prevalent neurodegenerative diseases (NDD) such as Parkinson's disease (PH), Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease (HH) is of clinical importance, One of the methods used in the literature to detect these neurodegenerative diseases is gait analysis-based classification. in this study, 12 different features fed a unidimensional Convolutional Neural Network (CNN) deep learning algorithm-based model, and aims to detect PD, HD, and ALS diseases was studied. The unidimensional CNN deep learning model fed with 12 features achieved 78,92%, 84,39% and 92,09% classification accuracy for control against HH, control. against PH, and control detection problems against ALS. Again, the relevant classifier produced 84,75% accuracy with the approach developed to separate all neurodegenerative disease specimens (NDD) under a single label as a class, and to distinguish these diseases against the current control.Item Parkinson's Disease Monitoring from Gait Analysis via Foot-Worn Sensors(2018) Asuroglu, Tunc; Acici, Koray; Erdas, Cagatay Berke; Toprak, Munire Kilinc; Erdem, Hamit; Ogul, Hasan; https://orcid.org/0000-0002-3821-6419; https://orcid.org/0000-0001-7979-0276; AAC-7834-2020; HDM-9910-2022; AAJ-8674-2021Background: In Parkinson's disease (PD), neuronal loss in the substantia nigra ultimate in dopaminergic denervation of the stiratum is followed by disarraying of the movements' preciseness, automatism, and agility. Hence, the seminal sign of PD is a change in motor performance of affected individuals. As PD is a neurodegenerative disease, progression of disability in mobility is an inevitable consequence. Indeed, the major cause of morbidity and mortality among patients with PD is the motor changes restricting their functional independence. Therefore, monitoring the manifestations of the disease is crucial to detect any worsening of symptoms timely, in order to maintain and improve the quality of life of these patients. Aim: The changes in motion of patients with PD can be ascertained by the help of wearable sensors attached to the limbs of subjects. Then analysing the recorded data for variation of signals would make it possible to figure an individualized profile of the disease. Advancement of such tools would improve understanding of the disease evolution in the long term and simplify the detection of precipitous changes in gait on a daily basis in the short term. In both cases the apperception of such events would contribute to improve the clinical decision making process with reliable data. To this end, we offer here a computational solution for effective monitoring of PD patients from gait analysis via multiple foot-worn sensors. Methods: We introduce a supervised model that is fed by ground reaction force (GRF) signals acquired from these gait sensors. We offer a hybrid model, called Locally Weighted Random Forest (LWRF), for regression analysis over the numerical features extracted from input signals to predict the severity of PD symptoms in terms of Universal Parkinson Disease Rating Scale (UPDRS) and Hoehn and Yahr (H&Y) scale. From GRF signals sixteen time-domain features and seven frequency-domain features were extracted and used. Results and conclusion: An experimental analysis conducted on a real data acquired from PD patients and healthy controls has shown that the predictions are highly correlated with the clinical annotations. Proposed approach for severity detection has the best correlation coefficient (CC), mean absolute error (MAE) and root mean squared error (RMSE) values with 0.895, 4.462 and 7.382 respectively in terms of UPDRS. The regression results for H&Y Scale discerns that proposed model outperforms other models with CC, MAE and RMSE with values 0.960, 0.168 and 0.306 respectively. In classification setup, proposed approach achieves higher accuracy in comparison with other studies with accuracy and specificity of 99.0% and 99.5% respectively. Main novelty of this approach is the fact that an exact value of the symptom level can be inferred rather than a categorical result that defines the severity of motor disorders. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.Item A Random Forest Method to Detect Parkinson's Disease via Gait Analysis(2017) Acici, Koray; Erdas, Cagatay Berke; Asuroglu, Tunc; Toprak, Munire Kilinc; Erdem, Hamit; Ogul, Hasan; 0000-0001-7979-0276; 0000-0003-4153-0764; 0000-0002-3821-6419; 0000-0003-3467-9923; AAJ-8674-2021; AAC-7834-2020; ITV-2441-2023; HDM-9910-2022Remote care and telemonitoring have become essential component of current geriatric medicine. Intelligent use of wireless sensors is a major issue in relevant computational studies to realize these concepts in practice. While there has been a growing interest in recognizing daily activities of patients through wearable sensors, the efforts towards utilizing the streaming data from these sensors for clinical practices are limited. Here, we present a practical application of clinical data mining from wearable sensors with a particular objective of diagnosing Parkinson's Disease from gait analysis through a sets of ground reaction force (GRF) sensors worn under the foots. We introduce a supervised learning method based on Random Forests that analyze the multi-sensor data to classify the person wearing these sensors. We offer to extract a set of time-domain and frequency-domain features that would be effective in distinguishing normal and diseased people from their gait signals. The experimental results on a benchmark dataset have shown that proposed method can significantly outperform the previous methods reported in the literature.Item Texture of Activities: Exploiting Local Binary Patterns for Accelerometer Data Analysis(2016) Asuroglu, Tunc; Acici, Koray; Erdas, Cagatay Berke; Ogul, Hasan; 0000-0003-4153-0764; 0000-0002-3821-6419; 0000-0003-3467-9923; AAC-7834-2020; HDM-9910-2022Recognition of activities through wearable sensors such as accelerometers is a recent challenge in pervasive and ubiquitous computing. The problem is often considered as a classification task where a set of descriptive features are extracted from input signal to feed a machine learning classifier. A major issue ignored so far in these studies is the incorporation of locally embedded features that could indeed be informative in describing the main activity performed by the individual being experimented. To close this gap, we offer here adapting Local Binary Pattern (LBP) approach, which is frequently used in identifying textures in images, in one-dimensional space of accelerometer data. To this end, we exploit the histogram of LPB found in each axes of input accelerometer signal as a feature set to feed a k-Nearest Neighbor classifier. The experiments on a benchmark dataset have shown that the proposed method can outperform some previous methods.Item Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors(2021) Erdas, Cagatay Berke; Guney, Selda; 0000-0003-3467-9923With the spread of wearable sensors, the solutions to the task of activity recognition by using the data obtained from the sensors have become widespread. Recognition of activities owing to wearable sensors such as accelerometers, gyroscopes, and magnetometers, etc. has been studied in recent years. Although there are several applications in the literature, differently in this study, deep learning algorithms such as Convolutional Neural Networks, Convolutional LSTM, and 3D Convolutional Neural Networks fed by Convolutional LSTM have been used in human activity recognition task by feeding with data obtained from accelerometer sensor. For this purpose, a frame was formed with raw samples of the same activity which were collected consecutively from the accelerometer sensor. Thus, it is aimed to capture the pattern inherent in the activity and due to preserving the continuous structure of the movement.Item Neurodegenerative disease detection and severity prediction using deep learning approaches(2021) Erdas, Cagatay Berke; Sumer, Emre; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021; AGA-5711-2022Neurodegenerative diseases (NDDs) such as amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD) can manifest themselves anatomically by degeneration in the brain as well as motor symptoms. The motor symptoms can affect walking dynamics in a disease-specific fashion; characteristically they disrupt gait. As the severity of the disease increases, walking ability deteriorates. We examined the effect of NDDs such as ALS, HD, and PD on gait and developed a convolutional long short-term memory (ConvLSTM) and threedimensional convolutional learning network (3D CNN)-based approach to detecting neurodegenerative conditions and predicting disease severity.