Author, institution: Andrius Petrėnas, Kaunas University of Technology
Science area, field: Technological Sciences, Electrical and Electronic Engineering
The Doctoral Dissertation is available at the library of Kaunas University of Technology (K. Donelaičio St. 20, Kaunas).
Scientific supervisor: Prof. dr. Vaidotas MAROZAS (Kaunas University of Technology, Technological Sciences, Electrical and Electronics Engineering – 01T)
Dissertation defence board of Electrical and Electronic Engineering Science Field:
Prof. Dr. Algimantas Valinevičius (Kaunas University of Technology, Technological Sciences, Electrical and Electronics Engineering – 01T) – chairman;
Assoc. Prof. Dr. Elena Jasiūnienė (Kaunas University of Technology, Technological Sciences, Electrical and Electronics Engineering – 01T),
Assoc. Prof. Dr. Algimantas Kriščiukaitis (Lithuanian University of Health Sciences, Biomedical sciences, Biophysics – 02B),
Dr. Raimondas Kubilius (Lithuanian University of Health Sciences, Biomedical sciences, Medicine – 06B),
Prof. Dr. Pablo Laguna (University of Zaragoza, Technological Sciences, Electrical and Electronics Engineering – 01T).
Annotation:
In this doctoral thesis, a clinically relevant scientific-technological problem of brief episode paroxysmal atrial fibrillation (AF) detection in ambulatory monitoring applications is covered. AF is a progressive disease, with primary AF episodes being usually brief and rarely occurring therefore treatment success highly depends at what stage of arrhythmia development AF is detected. While existing technologies for AF detection are suitable for detection of prolonged AF, there is an unsolved issue of reliable detection of self-terminating and usually asymptomatic paroxysmal AF episodes. For this purpose, two high performing approaches for detection of paroxysmal AF have been proposed. One of them was developed to analyse the time intervals between adjacent contractions of the ventricles, hence various signals containing heart rhythm information can be used as a subject for analysis. A low-complexity structure of the heart rhythm analysing AF detector makes it possible to implement the algorithm in a low-energy device for use in long-term monitoring applications. On the other hand, the other AF detector was developed solely for analysis of electrocardiogram signals. Since both heart rhythm and morphology information is included into the AF detection process, such an AF detector is well suited for detection of brief (< 30 s) AF episodes. The method has potential to be used for AF detection in high risk patient groups, i.e., after ischemic stroke or acute myocardial infarction.
