Author, Institution: Naseha Wafa Qammar, Kaunas University of Technology
Science area, field of science: Natural Sciences, Informatics, N009
Research supervisor: Prof. Dr. Hab. Minvydas Kazys Ragulskis (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Dissertation Defence Board of Informatics Science Field:
Prof. Dr. Hab. Rimantas Barauskas (Kaunas University of Technology, Natural Sciences, Informatics, N009) – chairperson
Prof. Dr. Dalia Čalnerytė (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Prof. Dr. Hamid Reza Karimi (Politecnico di Milano, Italy, Natural Sciences,
Informatics, N 009)
Assoc. Prof. Dr. Mantas Landauskas (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Prof. Dr. Vadimas Starikovičius (Vilnius Gediminas Technical University, Natural Sciences, Informatics, N009)
Dissertation defence meeting will be at Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73-402, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino 50, Kaunas) and on the internet: N. W. Qammar_el. dissertation.pdf
© N. W. Qammar, 2025 “The text of the thesis may not be copied, distributed, published, made public, including by making it publicly available on computer networks (Internet), reproduced in any form or by any means, including, but not limited to, electronic, mechanical or other means. Pursuant to Article 25(1) of the Law on Copyright and Related Rights of the Republic of Lithuania, a person with a disability who has difficulties in reading a document of a thesis published on the Internet, and insofar as this is justified by a particular disability, shall request that the document be made available in an alternative form by e-mail to doktorantura@ktu.lt.”
Annotation: This dissertation presents a novel approach for the analysis of electrocardiogram (ECG) signals using advanced matrix-based algorithms for the detection and monitoring of cardiac diseases. The research addresses the challenge of analysing complex, non-linear, and non-stationary biomedical time series by proposing algorithms based on Perfect Matrices of Lagrange Differences (PMLD), Hankel matrices, and the Secondary Matrix Framework (SMF). The dissertation introduces innovative methods for extracting subtle variations in ECG parameters such as RR, JT, QRS, AP, and DP intervals. These methods are validated through statistical techniques and compared with existing algorithms such as Singular Value Decomposition (SVD) and Permutation Entropy (PE). A particular focus is placed on early detection of cardiovascular conditions like atrial fibrillation and complexity collapse phenomena. The study uses real-world ECG data obtained during stress tests and ambulatory monitoring, with data preprocessing and algorithm development conducted in MATLAB. The proposed indicators and classification models enhance the sensitivity to individual cardiac signal characteristics and support clinical decision-making. This work demonstrates the potential of matrix-based techniques for accurate and scalable ECG signal analysis in modern cardiology