Toward a Reliable Classifier For Arrhythmia Diagnosis

Illnesses related to the heart are one of the major reasons for death all over the world causing many people to lose their lives in last decades. The good news is that many of those sicknesses are preventable if they are spotted in early stages. On the other hand, the number of the doctors are much lower than the number of patients. This will makes the auto diagnosing of diseases even more and more essential for humans today. Furthermore, when it comes to the diagnosing methods and algorithms, the current state of the art is lacking a comprehensive study on the comparison between different diagnosis solutions. Not having a single valid diagnosing solution has increased the confusion among scholars and made it harder for them to take further steps. This master thesis will address the issue of reliable diagnosing algorithm. We investigate ECG signals and the relation between different diseases and the heart’s electrical activity. Also, we will discuss the necessary steps needed for auto diagnosing the heart diseases including the literatures discussing the topic. The main goal of this master thesis is to find a single reliable diagnosing algorithm and quest for the best classifier to date for heart related sicknesses. Five most suited and most well-known classifiers, such as KNN, CART, MLP, Adaboost and SVM, have been investigated. To have a fair comparison, the ex-periment condition is kept the same for all classification methods. The UCI repository arrhythmia dataset will be used and the data will not be preprocessed. The experiment results indicates that AdaBoost noticeably classifies different diseases with a considera-bly better accuracy.

Biomarkers, medical treatment and fetal intrapartum surveillance in intrahepatic cholestasis of pregnancy

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific disorder characterized by maternal pruritus and elevated liver enzymes. It usually begins in the third trimester of pregnancy and resolves spontaneously after delivery. ICP is considered benign for the pregnant woman, but it is associated with an increased risk for unexplained term stillbirth and preterm delivery. There are no specific laboratory markers to diagnose ICP. The diagnosis is currently based on the presence of maternal pruritus and elevated values of alanine aminotransaminases (ALT) and serum bile acids (BA). Recently, ursodeoxycholic acid (UDCA) has been used for treatment. Mechanisms leading to intrauterine fetal death (IUFD) may be multifactorial and are unknown at present. For this thesis, 415 pregnant women with ICP were studied. The aim was to evaluate the value of the liver enzyme glutathione S-transferase alpha (GSTA) as a specific marker of ICP and to assess the effect of maternal UDCA therapy on maternal laboratory values and fetal outcome. The specific markers predisposing the fetus to heart arrhythmia were studied by comparing waveform analysis of fetal electrocardiograms (FECG) during labor in pregnancies complicated by ICP with controls. The levels of maternal GSTA were high and the values correlated with the value of ALT in patients with ICP. UDCA therapy reduced the values of the liver enzymes and alleviated maternal pruritus, but it did not influence maternal hormonal values. Although the newborns experienced an uneventful perinatal outcome, severe ICP was still associated with preterm birth and admission to the neonatal intensive care unit (NICU). There were no significant differences in intrapartum FECG findings between fetuses born to ICP women and controls.