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Πτυχιακές εργασίες

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Τίτλος Phonocardiogram signal classification using deep learning models
Άλλος τίτλος Αλγόριθμοι επεξεργασίας ιατρικών σημάτων: φωνοκαρδιογράφημα
Συγγραφέας Καλαiτζή Ελένη
Σύμβουλος διατριβής Στυλιανού, Ιωάννης
Καφεντζής, Γεώργιος
Περίληψη Heart disease is the leading cause of death in all continents except Africa. Heart diseases include coronary heart disease, heart failure, hypertensive heart disease, congenital heart disease, valvular heart disease, and structural heart abnormalities. This thesis aims to improve the task of automatic risk detection of structural heart abnormalities from digital phonocardiogram (PCG) data with applications to pediatric heart disease screening. In a previous study, a number of convolutional neural network (CNN)-based systems operating on mel-frequency cepstral coefficients (MFCCs) of phonocardiogram signals are shown to perform better than systems using conventional machine learning guided by hand-crafted features. These systems were trained using time-frequency representations of segmental PCG frames. Various techniques for segmentation and time-frequency representations were designed to model the input recordings. Training and testing has been conducted on high-quality databases while each test produces five random experiments. Systems were evaluated using Receiver Operating Curve-Area under Curve, Sensitivity, Specificity, Accuracy, F1 score, and Matthews Correlation Coefficient. In the context of this thesis, we emphasize on the improvement of pre-existing Convolutional Neural Network (CNN) models. We undertake this endeavor while adhering to the identical preprocessing methodology employed in a prior study. A Batch Normalization layer is included in pre-existing architectures, showing improvement in all performance metrics. Additionally, we introduce an enhanced CNN architecture combining Batch Normalization and an additional convolutional layer that outperforms previous models and presents significant improvement in all performance metrics.
Γλώσσα Αγγλικά
Ημερομηνία έκδοσης 2023-11-22
Συλλογή   Σχολή/Τμήμα--Σχολή Θετικών και Τεχνολογικών Επιστημών--Τμήμα Φυσικής--Πτυχιακές εργασίες
  Τύπος Εργασίας--Πτυχιακές εργασίες
Μόνιμη Σύνδεση https://elocus.lib.uoc.gr//dlib/e/9/f/metadata-dlib-1697782568-167239-31750.tkl Bookmark and Share
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