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Bulunan: 6 Adet 0.000 sn
Yazar/lar : ALTAN, Gökhan ✕Koleksiyon : Mühendislik ve Doğa ... ✕
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Erişime Açık

A Multistage Deep Learning Algorithm for Detecting Arrhythmia

ALLAHVERDİ, Novruz | KUTLU, Yakup | ALTAN, Gökhan

Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervised and supervised training. Arrhythmia is a cardiac irregularity caused by a problem of the heart. In this study, a multi-stage DBN classification is proposed for achieving the efficiency of the DBN on arrhythmia disorders. Heartbeats from the MITBIH Arrhythmia database are classified into five groups which are recommended by AAMI. The Wavelet packet decomposition, higher order statistics, morphology and Discrete Fourier transform techniques were utilized to extract features. The classification pe ...Daha fazlası

Konferans Bildirisi 2017 IEEE 6 Görüntülenme
Erişime Açık

Deep Learning for COPD Analysis Using Lung Sounds

ALLAHVERDİ, Novruz | ALTAN, Gökhan | KUTLU, Yakup

Deep Learning(DL) algotithms have become popular with the detailed analyzing capabilities with many hidden layers in recent years. The size of hidden layer in the classifier models is complately correlated with the analyzing capability of the proposed mode. Multiple hidden layers and neuron size in the hidden layers enhance the analyzing capability of the models,whereas increasing the training time.

Konferans Bildirisi 2018 3 Görüntülenme
Erişime Açık

Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

ALLAHVERDİ, Novruz | ALTAN, Gökhan | KUTLU, Yakup

An electroencephalogram (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects. It can be used to detect potential problems associated with brain disorders. The aim of this study is assessing the clinical usefulness of EEG which is recorded from slow cortical potentials (SCP) training in stroke patients using Deep belief network (DBN) which has a greedy layer wise training using Restricted Boltzmann Machines based unsupervised weight and bias evaluation and neural network based supervised training. EEGs ...Daha fazlası

Makale 2016 8 Görüntülenme
Erişime Açık

A New Approach to Early Diagnosis of Congestive Heart Failure Disease by Using Hilbert-Huang Transform

ALLAHVERDİ, Novruz | KUTLU, Yakup | ALTAN, Gökhan |

Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart's 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 ddata. The statistical features are extracted from instinct mode functions that are obtained applying ...Daha fazlası

Makale 2016 4 Görüntülenme
Erişime Açık

A Multistage Deep Belief Networks Application on Arrhythmia Classification

ALLAHVERDİ, Novruz | ALTAN, Gökhan | KUTLU, Yakup

An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders. The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years. This study introduces a multistage deep learning classification model for automatic arrhythmia classification. The proposed model includes a multi-stage classification system that uses ECG waveforms and the Second Order Difference Plot (SODP) features using a Deep Belief Network ...Daha fazlası

Makale 2016 4 Görüntülenme
Erişime Açık

Diagnosis of Coronary Artery Disease Using Deep Belief Networks

ALLAHVERDİ, Novruz | KUTLU, Yakup | ALTAN, Gökhan

In this study, a decision-support system is presented to aid cardiologists during the diagnosis and to create a base for a new diagnosis system which separates two classes (CAD and no-CAD patients) using an electrocardiogram (ECG). 24 hour filtered ECG signals from PhysioNet were used. 15 second short-term ECG segments were extracted from 24 hour ECG signals to increase the number of samples and to provide a convenient transformation in a short period of time. The Hilbert-Huang Transform, which is effective on non-linear and nonstationary signals, was used to extract the features from short-te ...Daha fazlası

Konferans Bildirisi 2016 3 Görüntülenme
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