Background: For many decades, the ECG was the
workhorse of non-invasive cardiac test and today although
other techniques provide more details about the structural
anomalies in congenital heart diseases, ECG is likely to be
part of clinical evaluation of patients with such diseases
because it is inexpensive, easy to perform and in certain
situations may be both sensitive and specific.
Objective: this study carried out to identify the pattern of
ECG study in patients with TOF.
Methods: this is a retrospective study of 200 patients
with TOF, referred to Ibn Al-Bitar cardiac center from
April 1993 to May 1999. The diagnosis of TOF established
by echocrdiographic, catheterization and angiographic
study. For each patient, the ECG tracing had been analyzed
for rhythm, p-wave, P-R interval, QRS axis, duration and
T-wave in V1 and any chamber enlargement.
Results: The ECG analysis revealed that all patients had
sinus rhythm, normal P-R interval and normal p-wave
duration and amplitude, and normal QRS duration. All
studied patients had one criteria of RVH and 95% of them
had two or more of such criteria.
Conclusion: we found that in the absence of RVH
criteria, the diagnosis of TOF is unlikely and the present of
northwest axis should indicate canal type VSD
In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare
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