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Clinical Trial
. 2024 Jul 22:16:1027-1043.
doi: 10.2147/NSS.S463026. eCollection 2024.

Performance Investigation of Somfit Sleep Staging Algorithm

Marcus McMahon  1 , Jeremy Goldin  2 , Elizabeth Susan Kealy  3 , Darrel Joseph Wicks  4 , Eugene Zilberg  5 , Warwick Freeman  5 , Behzad Aliahmad  5
Affiliations
Clinical Trial

Performance Investigation of Somfit Sleep Staging Algorithm

Marcus McMahon et al. Nat Sci Sleep. .

Abstract

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.

Patients and methods: One hundred and ten participants referred for sleep investigation with suspected or preexisting obstructive sleep apnea (OSA) in need of a review were enrolled into the study involving simultaneous recording of full overnight polysomnography (PSG) and Somfit data. The recordings were conducted at three centers in Australia. The reported statistics include standard measures of agreement between Somfit automatic hypnogram and consensus PSG hypnogram.

Results: Overall percent agreement across five sleep stages (N1, N2, N3, REM, and wake) between Somfit automatic and consensus PSG hypnograms was 76.14 (SE: 0.79). The percent agreements between different pairs of sleep technologists' PSG hypnograms varied from 74.36 (1.93) to 85.50 (0.64), with interscorer agreement being greater for scorers from the same sleep laboratory. The estimate of kappa between Somfit and consensus PSG was 0.672 (0.002). Percent agreement for sleep/wake discrimination was 89.30 (0.37). The accuracy of Somfit sleep staging algorithm varied with increasing OSA severity - percent agreement was 79.67 (1.87) for the normal subjects, 77.38 (1.06) for mild OSA, 74.83 (1.79) for moderate OSA and 72.93 (1.68) for severe OSA.

Conclusion: Agreement between Somfit and PSG hypnograms was non-inferior to PSG interscorer agreement for a number of scorers, thus confirming acceptability of electrode placement at the center of the forehead. The directions for algorithm improvement include additional arousal detection, integration of motion and oximetry signals and separate inference models for individual sleep stages.

Keywords: deep learning; forehead electroencephalography; home sleep apnea testing; interscorer agreement; polysomnography.

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Conflict of interest statement

Dr Jeremy Goldin is a medical consultant for Compumedics Pty Ltd. Ms Elizabeth Kealy reports personal fees from Compumedics, during the conduct of the study; personal fees from ResMed and Compumedics, outside the submitted work. Mr Darrel Wicks reports grants from Compumedics Ltd, during the conduct of the study. The authors report no other conflicts of interest in this work.

Figures

Figure 1
Figure 1
Somfit device.
Figure 2
Figure 2
Study flow chart.
Figure 3
Figure 3
Percent agreement between Somfit algorithm and consensus PSG hypnogram.
Figure 4
Figure 4
Ninety-five percent confidence intervals for percent agreement between all pairs of manual PSG hypnograms and percent agreement between Somfit algorithm and consensus PSG hypnogram.
Figure 5
Figure 5
Percent agreement between Somfit algorithm and consensus PSG hypnogram (five sleep stages) across different patient characteristics.
See this image and copyright information in PMC

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