Martin Jagelka

Dr. Martin Jagelka

orcid

Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 February 2025 in Biosensors
Verified
Journal Article
Open access
PDF file
DVDiana Vitazkova
Diana Vitazkova
HKHelena Kosnáčová
Helena Kosnáčová
DTDaniela Turonova
Daniela Turonova
EFErik Foltán
Erik Foltán
MJMartin Jagelka
Martin Jagelka
Erik VavrinskyErik Vavrinsky
Erik Vavrinsky
This paper explores the progressive era of sleep monitoring, focusing on wearable and remote devices contributing to advances in the concept of home polysomnography. We begin by exploring the basic physiology of sleep, establishing a theoretical basis for understanding sleep stages and associated changes in physiological variables. The review then moves on to an analysis of specific cutting-edge devices and technologies, with an emphasis on their practical applications, user comfort, and accuracy. Attention is also given to the ability of these devices to predict neurological disorders, particularly Alzheimer’s and Parkinson’s disease. The paper highlights the integration of hardware innovations, targeted sleep parameters, and partially advanced algorithms, illustrating how these elements converge to provide reliable sleep health information. By bridging the gap between clinical diagnosis and real-world applicability, this review aims to elucidate the role of modern sleep monitoring tools in improving personalised healthcare and proactive disease management.

31 December 2024 in Preprints.org
Verified
Preprint
Open access
PDF file
DVDiana Vitazkova
Diana Vitazkova
HKHelena Kosnáčová
Helena Kosnáčová
DTDaniela Turonova
Daniela Turonova
EFErik Foltán
Erik Foltán
MJMartin Jagelka
Martin Jagelka
Erik VavrinskyErik Vavrinsky
Erik Vavrinsky
This paper explores the progressive era of sleep monitoring, focusing on wearable and remote devices, moving towards the concept of home polysomnography. We begin by examining the basic physiology of sleep, establishing a theoretical foundation for understanding sleep stages and associated changes in physiological variables. The review then moves on to analyse specific cutting-edge devices and technologies, emphasizing their practical applications, user comfort, and accuracy. Attention is also given to the ability of these devices to predict neurological disorders, particularly Alzheimer’s and Parkinson’s disease. The paper highlights the integration of hardware innovations, targeted sleep parameters, and partially advanced algorithms, illustrating how these elements converge to provide reliable information on sleep health status. By bridging the gap between clinical diagnosis and real-world applicability, this review aims to clarify the role of modern sleep monitoring tools in improving personalized healthcare and proactive disease management.