With early monitoring and identification of symptoms, sleep disorders could be treated more effectively.
Sleep disorders are highly prevalent in our current busy society, yet their diagnosis is time-consuming and costly. Established at the University of Eastern Finland in 2017, the Sleep Technology and Analytics Research Group, STAR, explores mechanisms underlying sleep disorders and develops automated methods for their diagnostics.
The STAR research group is led by Timo Leppänen, the University of Eastern Finland’s newly appointed Professor of Biomedical Engineering specialising in sleep analytics.
“My research focuses on sleep and sleep disorders. In recent years, advances in technology and computational methods have taken sleep research forward as well. Early-stage monitoring and diagnosis of sleep disorders with the help of artificial intelligence and wearable sensors is a particular focus at our university. However, sleep disorders remain widely underdiagnosed, as translating research innovations into clinical practice is both challenging and time-consuming.”
Leppänen notes that while age, obesity and male sex are recognised risk factors for sleep apnoea, they do not provide a reliable basis for early risk assessment.
Moreover, the cornerstone of sleep apnoea diagnosis – polysomnography – is an expensive procedure, requiring a sleep laboratory and trained personnel to attach the electrodes, monitor the recording and analyse the results. This is why especially in Finland, diagnosis typically relies on a simplified at-home nocturnal polygraphy study.
“Beyond the complexity of diagnostic measurements, the most significant gaps in sleep research include a lack of benchmark datasets from healthy individuals, as well as a limited understanding of the factors contributing to disease onset, and of assessing disease progression,” Leppänen explains.
The STAR research group investigates typical signal features and biomarkers associated with healthy sleep and sleep disorders, while also developing predictive models for comorbidities and symptom assessment.
“We’re also developing automated, AI-based diagnostic methods for sleep disorders and testing the feasibility of various wearable sensors for early-stage diagnosis, as well as for monitoring sleep quality and treatment outcomes,” Leppänen notes.
With simpler measurement solutions and more advanced automated analysis methods, individuals suffering from sleep disorders could be identified more efficiently and brought into care sooner.
We strive to offer unique and novel insight into the factors and early indicators associated with the onset of sleep disorders, as well as into the risk factors associated with disease progression.
Timo Leppänen
Professor
Research-based knowledge on the microfeatures and indicators typical of sleep disorders could thus be used in early-stage diagnostics, enabling timely interventions and lifestyle changes that can help prevent sleep disorders.
“Increasingly accurate signal processing methods could enable better and increasingly cost-effective diagnostics and treatment effectiveness monitoring.”
For further information, please contact:
Timo Leppänen, [email protected], https://uefconnect.uef.fi/timo.leppanen/
TIMO LEPPÄNEN
Professor of Biomedical Engineering, especially sleep analytics, 1 May 2025–
- BSc, University of Eastern Finland, 2014
- MSc, University of Eastern Finland, 2015
- PhD, University of Eastern Finland, 2016
- Title of Docent, University of Eastern Finland, 2020
Key roles
- Honorary Associate Professor, The University of Queensland, Brisbane, Australia, 2024–
- Associate Professor, University of Eastern Finland, 2022–2025
- European Sleep Research Society, Board Member of the Scientific Committee, 2022–
- Finnish Sleep Research Society, Board Member, 2023–
