| dc.description.abstract | The Electrocardiogram (ECG) signal reflects the performance of the human heart as an
electrical signal. It consists of three main waves (P, QRS complex, and T), and is
recorded by an ECG machine in the form of 12 leads which include valuable
information about the functional activities of the human heart and cardiovascular
system. It is annotated manually by a cardiologist to diagnose cardiac disease, but for a long time ECG recordings were performed to get an effective measure of heart rate
variability. The generated ECG data is huge and the probability of wrong analysis or
misreading by manual annotation is increased. Therefore, many computerized based
techniques have been proposed in literature for analyzing and detecting ECG waves,
and at a lower rate for diagnosing cardiac diseases. In this thesis, a new robust
intelligent system has been proposed to perform an accurate diagnosis of a high risk
cardiac disease named left ventricular hypertrophy (LVH). Four approaches are
developed within the proposed ECG system to improve the performance of processing
the ECG signal with respect to the existing methods and to discover new system for
diagnosing cardiac disease based on the computerized intelligence technique. The first
proposed approach is a digital recovery system which addresses the limitation of digital
12 lead ECG data by reconstructing it from the colour scanned image of the ECG
printed chart. This approach is implemented by four image processing steps and
captures raw ECG data with respect to the baseline which is detected by the same
approach. Furthermore, it is reliable for different ECG morphologies and printout
charts. The reconstructed data is evaluated qualitatively and quantitatively using some
predefined standard features. The analytic results demonstrate the consistency and
robustness of this approach to generate 12 lead ECG data with high precision (98%).
The second and third approaches are proposed to detect ECG waves and then delineate
all time characteristics of these waves. In contrast to the existing methods, both
approaches are based on straightforward algorithms that perform instantaneous
processing for the ECG signal. As a result, detection operation is executed in a high
speed which reaches (4.5s per 650,000 beats) for QRS complex and (2.7s per 225,000
beats) for P&T waves. The based technique in both detection approaches has the
advantage of rising falling edge mutation as a base rule for delineating subject. This
technique reduces undetected beats and provides accurate detection results exceeding
ones in up to date existing methods. The fourth proposed approach is a diagnostic
system for LVH cardiac disease based on proposed diagnostic criterion. In contrast to
the conventional LVH diagnostic criteria, the decision in the proposed criterion is
computed by three logical expressions; two of which are determined by a combination
of classic criteria, whereas the third is obtained by eight ECG voltages and takes two
different levels for each gender. These expressions are represented by the membership
functions in the proposed design of the fuzzy inference system. The proposed
diagnosing system is validated by fifty ECG records, in which the validation results
score were perfect (100%) in terms of sensitivity, specificity, and accuracy, while the
best diagnosing accuracy achieved by traditional diagnostic criteria does not exceed
90%. | en_US |
