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The UNICA Sleep HRV Analysis tool: an integrated open-source tool for heart rate variability analysis during sleep.
Physiol Meas
January 2025
Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, Via Marengo, Cagliari, Sardegna, 09123, ITALY.
Heart rate variability (HRV) analysis during sleep plays a key role for understanding autonomic nervous system function and assessing cardiovascular health. The UNICA Sleep HRV analysis (UNICA-HRV) tool is a novel, open-source MATLAB tool designed to fill the gap in current HRV analysis tools. In particular, the integration of ECG and HRV data with hypnogram information, which illustrates the progression through the different sleep stages, ease the computation of HRV metrics in polysomnographic recordings.
View Article and Find Full Text PDFAutomatic sleep staging based on 24/7 EEG SubQ (UNEEG medical) data displays strong agreement with polysomnography in healthy adults.
Sleep Health
December 2024
Product Management, T&W Engineering A/S, Lillerød, Denmark.
Goal And Aims: Performance evaluation of automatic sleep staging on two-channel subcutaneous electroencephalography.
Focus Technology: UNEEG medical's 24/7 electroencephalography SubQ (the SubQ device) with deep learning model U-SleepSQ.
Reference Method/technology: Manually scored hypnograms from polysomnographic recordings.
Overnight Sleep Staging Using Chest-Worn Accelerometry.
Sensors (Basel)
September 2024
Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands.
Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer.
View Article and Find Full Text PDFWhat radio waves tell us about sleep!
Sleep
January 2025
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine-learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep.
View Article and Find Full Text PDFPerformance Investigation of Somfit Sleep Staging Algorithm.
Nat Sci Sleep
July 2024
Medical Innovations, Compumedics Limited, Abbotsford, Victoria, Australia.
Purpose: To investigate accuracy of the sleep staging algorithm in a new miniaturized home sleep monitoring device - Compumedics® Somfit. Somfit is attached to patient's forehead and combines channels specified for a pulse arterial tonometry (PAT)-based home sleep apnea testing (HSAT) device with the neurological signals. Somfit sleep staging deep learning algorithm is based on convolutional neural network architecture.
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