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Sleep Europe 2024 | Wearable devices & deep learning: alternatives to polysomnography for sleep disorder diagnostics
Juuso Huovila, PhD Candidate, University of Eastern Finland, Kuopio, Finland, discusses the limitations of traditional polysomnography in sleep disorder diagnostics and presents his research on wearable devices as a more convenient alternative. He developed deep learning models using electroencephalogram (EEG) and photoplethysmogram (PPG) signals, comparing their accuracy against manual scoring, and found that while EEG models provided higher accuracy, PPG models offered more stable results and greater potential for long-term monitoring. This interview took place at the Sleep Europe 2024 congress in Seville, Spain.
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