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