M. Rinkevičius “Cardiovascular signal analysis algorithms for characterizing sleep apnea” doctoral dissertation defence

Author, Institution: Mantas Rinkevičius, Kaunas University of Technology

Science area, field of science: Technological Sciences, Electrical and Electronic Engineering, T001

Research supervisor: Prof. Dr. Vaidotas Marozas (Kaunas University of Technology, Electrical and Electronic Engineering, T001)

Dissertation Defence Board of Electrical and Electronic Engineering Science Field:
Prof. Dr. Elena Jasiūnienė (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering, T001) – chairperson
Assoc. Prof. Dr. Timo Leppanen (University of Eastern Finland, Finland, Technological Sciences, Electrical and Electronic Engineering, T001)
Prof. Dr. Dangirutis Navikas (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering, T001)
Prof. Dr. Evelina Pajėdienė (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine, M001)
Prof. Dr. Renaldas Raišutis (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering, T001)

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: M. Rinkevičius el. dissertation.pdf

© M. Rinkevičius, 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: Obstructive sleep apnea (OSA) is a common sleep disorder marked by recurring constraints of the upper airway, which cause reduced or halted breathing during sleep. Globally, over a billion individuals are affected by sleep apnea syndrome, with some countries reporting prevalence rates above 50%. OSA patients often require increased healthcare services and have a higher likelihood of developing type 2 diabetes, mood and anxiety disorders, and cardiovascular diseases. In addition, latest research suggests potential associations between OSA and cardiac arrhythmias such as atrial fibrillation. In medical practice, the apnea-hypopnea index (AHI) serves as the benchmark for identifying the presence and severity of OSA. However, the AHI has been criticized for failing to capture pertinent clinical characteristics and being an inadequate tool for predicting clinical outcomes. At present, the significance of the AHI as a diagnostic metric of severity for clinically relevant OSA is diminishing. This thesis argues that the AHI alone is insufficient for evaluating OSA severity, as it only measures the number of events per hour of sleep and does not account for other crucial factors such as potential cardiovascular effects. In order to monitor relevant cardiovascular effects in OSA patients, electrocardiogram (ECG) and photoplethysmogram (PPG) signal analysis can be utilized. This doctoral thesis proposes and explores a novel cross-recurrence properties-based method for characterizing OSA severity, considering potential association to cardiac arrhythmias. The proposed method estimates cross-recurrence properties between specific feature time series, extracted from ECG and PPG signals.