Abstract: Phonocardiography is important in appraisal of
congenital heart disease and pulmonary hypertension as it reflects the
duration of right ventricular systoles. The systolic murmur in patients
with intra-cardiac shunt decreases as pulmonary hypertension
develops and may eventually disappear completely as the pulmonary
pressure reaches systemic level. Phonocardiography and auscultation
are non-invasive, low-cost, and accurate methods to assess heart
disease. In this work an objective signal processing tool to extract
information from phonocardiography signal using Wavelet is
proposed to classify the murmur as normal or abnormal. Since the
feature vector is large, a Binary Particle Swarm Optimization (PSO)
with mutation for feature selection is proposed. The extracted
features improve the classification accuracy and were tested across
various classifiers including Naïve Bayes, kNN, C4.5, and SVM.
Abstract: The paper presents a novel screening method to
indicate congenital heart diseases (CHD), which otherwise could
remain undetected because of their low level. Therefore, not
belonging to the high-risk population, the pregnancies are not subject
to the regular fetal monitoring with ultrasound echocardiography.
Based on the fact that CHD is a morphological defect of the heart
causing turbulent blood flow, the turbulence appears as a murmur,
which can be detected by fetal phonocardiography (fPCG). The
proposed method applies measurements on the maternal abdomen
and from the recorded sound signal a sophisticated processing
determines the fetal heart murmur. The paper describes the problems
and the additional advantages of the fPCG method including the
possibility of measurements at home and its combination with the
prescribed regular cardiotocographic (CTG) monitoring. The
proposed screening process implemented on a telemedicine system
provides an enhanced safety against hidden cardiac diseases.
Abstract: In this study, fuzzy rule-based classifier is used for the
diagnosis of congenital heart disease. Congenital heart diseases are
defined as structural or functional heart disease. Medical data sets
were obtained from Pediatric Cardiology Department at Selcuk
University, from years 2000 to 2003. Firstly, fuzzy rules were
generated by using medical data. Then the weights of fuzzy rules
were calculated. Two different reasoning methods as “weighted vote
method" and “singles winner method" were used in this study. The
results of fuzzy classifiers were compared.