dc.description.abstract | 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-term ECG
signals. Instinct Mode Function (IMF) was extracted by applying Empirical Mode
Decomposition to short-term ECG signals. The Hilbert Transform (HT) was
applied to each IMF to obtain instantaneous frequency characteristics of the
signal. Dataset was created by extracting statistical features from HT applied to
IMF. Deep Belief Networks (DBN) which have a common use in Deep Learning
algorithms were used as the classifier. DBN classification accuracy in the
diagnosis of the CAD is discussed. The extracted dataset was tested using the
10-fold cross validation method. The test characteristics (sensitivity, accuracy
and specificity) that are the basic parameters of independent testing in the
medical diagnostic systems were calculated using this validation method. Shortterm ECG signals of CAD patients and no-CAD groups were classified by the
DBN with the rates of 98.05%, 98.88% and 96.02%, for accuracy, specificity and
sensitivity, respectively.
The DBN model achieved higher accuracy rates than the Neural Network | |