Gokhan Altan

A new approach to early diagnosis of congestive heart failure disease by using HilbertHuang transform

Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the hearts inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most important cause of CHF. This study focuses on the diagnosis of both the CHF and the CAD. The Hilbert-Huang transform (HHT), which is effective on non-linear and non-stationary signals, is used to extract the features from R-R intervals obtained from the raw electrocardiogram data. The statistical features are extracted from instinct mode functions that are obtained applying the HHT to R-R intervals. Classification performance is examined with extracted statistical features using a multilayer perceptron neural network. The designed model classified the CHF, the CAD patients and a normal control group with rates of 97.83%, 93.79% and 100%, accuracy, specificity and sensitivity, respectively. Also, early diagnosis of the CHF was performed by interpretation of the CAD with a classification accuracy rate of 97.53%, specificity of 98.18% and sensitivity of 97.13%. As a result, a single system having the ability of both diagnosis and early diagnosis of CHF is performed by integrating the CAD diagnosis method to the CHF diagnosis method.

ECG based Human identification using Logspace Grid Analysis of Second Order Difference Plot

In this study, Second Order Difference Plot (SODP) features are used for ECG based human identification. SODP is a method that allows to determine the features with the statistical analysis of the situations obtained from distributions and the distribution of each of successive points on an unstable and linear signals. ECG records of 90 individuals in Physionet ECG-Id database are used in the study. These records are divided into segments with logarithmic grid, number of points in each segment was examined. Extracted features are classified using 2 Fold Cross Validation with kNN classifier, speed and performance of identification system were investigated. As a result, ECG based Human identification using Logspace Grid Analysis of SODP was performed in a very short time with 91.52% success.

A fuzzy expert system design to monitor patient's condition during heart surgery

The aim of this study is to design a Fuzzy Expert System to trace vital functions of the patient for directing the courses of the surgery during the Coronary Bypass Surgery (CBS). The designed fuzzy expert system presents and monitors four of the vital functions (Blood pressure, Hemoglobin, Pulse and Beta-blocker) of the patient and also interprets them to indicate current situation of the patient during the operation. The output of the system is indicator for the patient during the surgery in visual and audial forms that have (1) very thick, (2) thick, (3) normal, (4) thin and (5) very thin forms of the signal. The data (Course of the patient during the surgery) obtained from designed fuzzy expert system are compared with the data in the literature.It has been seen that better results are observed with designed system. The system can be viewed as an alternative for existing methods to monitor for directing the course of the coronary surgery.

Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis

Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on the proposed structures by fully connected layers depending on of the artificial neural networks. The optimization of the predefined classification parameters for the supervised models eases reaching the global optimality with exact zero training error. The autoencoder (AE) models are the highly generalized ways of the unsupervised stages for the DL to define the output weights of the hidden neurons with various representations. As alternatively to the conventional Extreme Learning Machines (ELM) AE, Hessenberg decomposition-based ELM autoencoder (HessELM-AE) is a novel kernel to generate different presentations of the input data within the intended sizes of the models. The aim of the study is analyzing the performance of the novel Deep AE kernel for clinical availability on electroencephalogram (EEG) with stroke patients. The slow cortical potentials (SCP) training in stroke patients during eight neurofeedback sessions were analyzed using Hilbert-Huang Transform. The statistical features of different frequency modulations were fed into the Deep ELM model for generative AE kernels. The novel Deep ELM-AE kernels have discriminated the brain activity with high classification performances for positivity and negativity tasks in stroke patients.

Deep learning with 3D-second order difference plot on respiratory sounds

Abstract The second order difference plot (SODP) is a nonlinear signal analysis method that visualizes two consecutive data points for many types of biomedical signals. The proposed method is based on analysing quantization of 3D-space which is originated using three consecutive data points in signal. The obtained 3D-SODP space was segmented into 3–10 spaces using octants, spheres and cuboid polyhedrons of which centroids are at the origin. Lung sound is an indispensable tool for respiratory and cardiac diseases. The study is focused on classifying the lung sounds from at risk level and the interior level of chronic obstructive pulmonary disease (COPD). The COPD is one of the most deadliest and common respiratory diseases which come into existence as a consequence of smoking. The smokers for a few years are qualified as at risk level of COPD (COPD-0). The 12 channels of lung sounds from the RespiratoryDatabase@TR were utilized in the analysis of the proposed 3D-SODP quantization method. The lung sounds are auscultated synchronously from posterior and anterior sides of subjects using two digital stethoscopes by a pulmonologist clinician in Antakya State Hospital, Turkey. Deep Belief Networks (DBN) algorithm was preferred in the classification stage. It has a greedy layer-wise pre-training which is based on restricted Boltzmann machines and optimizes the pre-trained weights using supervised iterations. The proposed DBN model had 2 hidden layers with 270 and 580 neurons, respectively. The conjunction usage of 3D-SODP quantization features with the DBN separated the lung sounds from different levels of COPD with high classification performance rates of 95.84%, 93.34% and 93.65% for accuracy, sensitivity and specificity, respectively. The results indicate that the 3D-SODP quantization on respiratory sounds has ability to diagnose the levels of the COPD using the deep learning model. Especially, the octant-based quantization is effective on lung sounds with high generalization capability using a small number of feature set dimension.

A review on respiratory sound analysis using machine learning

Auscultation of the respiratory sounds is an inexpensive and effective method for diagnosing cardio-pulmonary disorders using lung sounds from chest and back. Nowadays, high system performances in the management of robust processes that require great attention were increased using the computer-aided analysis methods and the developments of the diagnosis system. Analysis of the respiratory sounds with computer-aided systems allows objective and useful assessments. In this study, a brief description of the abnormal respiratory sounds was presented. The main aims of the study are performing a systematic review about methods and the machine learning algorithms that are used to classify the abnormal respiratory sounds for diagnosis of cardio-pulmonary disorders and evaluating the development of possible methods on respiratory sounds in the future.