- Kuva Lumoava
The doctoral dissertation in the field of Cardiology will be examined at the Faculty of Health Sciences at Kuopio Campus. The public examination will be streamed online.
What is the topic of your doctoral research? Why is it important to study the topic?
The purpose of this study was to evaluate the feasibility, reliability, and accuracy of both professional-evaluation and automatic arrhythmia analysis for the detection of atrial fibrillation (AF) with novel mobile health (mHealth) measurement techniques. AF is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient’s life, imposing a significant burden on public health, and the healthcare budget. Embolic stroke is one of the most severe complications of AF. The detection of AF is important when making the clinical decision to initiate anticoagulation therapy for the prevention of thromboembolic events. However, the detection and diagnosis of AF still remains a major clinical challenge due to AF’s asymptomatic and intermittent nature. Novel consumer-grade mHealth products with automatic arrhythmia detection could provide a practical and cost-effective solution for AF-screening.
What are the key findings or observations of your doctoral research?
The novel mHealth monitoring techniques, necklace-ECG, mHealth patch, and heart belt provided high quality ECG and ECG-based heart rate variability (HRV) data resulting in high sensitivity and specificity of AF detection. Thus, these new wearable mHealth monitoring techniques with AI arrhythmia analysis represent new, promising methods for AF screening.
What are the key research methods and materials used in your doctoral research?
All of this project’s patients were recruited from the emergency department of Kuopio University Hospital (KUH). In Study I, a thirty-second electrocardiogram (ECG) was recorded with a single-lead necklace-embedded ECG recording device. The necklace-ECG recordings in 145 patients (66 AF and 79 sinus rhythm, SR) were interpreted by two cardiologists as well as the novel artificial intelligence (AI) arrhythmia detection algorithm. In Studies II-III, a single- lead ECG-based HRV mHealth patch measurement and a single-lead heart belt ECG were recorded for 24 h. The AI arrhythmia detection algorithm was used for automatic AF-screening from HRV data in 178 patients (79 AF and 99 SR) and from ECG data in 159 patients (73 AF and 86 SR). In addition, in Study III, four researchers visually analyzed the heart belt ECG recordings. In Study III, A concomitant Holter measurement served as a reference device in a user experience survey in a high-risk group of patients over 65 years of age. In all three studies, simultaneously registered 3-lead ECGs (Holter) served as a golden standard for the final rhythm diagnosis. The studies presented in this thesis are related to a larger study entity that examines the identification of arrhythmias by different methods.
The doctoral dissertation of Elmeri Santala, Licentiate of Medicine, entitled Novel ECG-based technologies in the detection of atrial fibrillation, will be examined at the Faculty of Health Sciences. The Opponent in the public examination will be Docent Mika Lehto of the University of Helsinki, and the Custos will be Professor Juha Hartikainen University of Eastern Finland. The public examination will be held in Finnish.
Public examination online (in Finnish)
For further information, please contact: Onni Elmeri Santala, Lic Med, elmeris (a) uef.fi, tel. +358 503010879