| Abstract [eng] |
One of the most important public health tasks is to ensure early diagnosis of heart rhythm disorders to reduce the Disability Adjusted Life Years (DALY) index (1). Disorders known as "irregular rhythms," according to the European Heart Association, account for 282 billion euros across Europe, with each Lithuanian resident incurring a cost of 745 euros annually (2). Smart technology systems unlock new possibilities in the field of heart disorder diagnostics and patient condition monitoring, providing a way to remotely record vital signs and offer health-promoting recommendations through automated means. The recording of heart activity indicators using smart wearable devices is based on photoplethysmography and electrocardiography sensors. This thesis discusses the operating principles of these sensors, their advantages, and limitations. After conducting a literature review using databases such as PubMed, Scopus, and Web of Science, 17 open-access publications were selected that evaluate classification methodologies for heart arrhythmias using artificial intelligence-based algorithms. Based on the data processing methods and algorithm structures described in these publications, a comparison is made with industry innovations; conclusions and recommendations related to the progress in heart arrhythmia classification using smart technologies are formulated. |
