First, some algorithms are not free, sitting behind paywalls and/or not publicly available. There are likely several reasons for this. The overall accuracy was not reported, though the mean F1-score-a measure reflecting the quality of the prediction against the consensus scoring of five human experts-was 0.79 for healthy adults and 0.76 for patients with sleep apnea.ĭespite this growing number of automated tools, accurate automated sleep staging has yet to become a de facto standard in the field. Finally, the recent U-Sleep algorithm ( Perslev et al., 2021) was established using PSG recordings from 15,660 participants of 16 clinical studies. A year later, the ‘SeqSleepNet’ algorithm ( Phan et al., 2019) was published, which represented a method trained and tested using a 20-fold cross-validation of 200 nights of PSG data, with an overall accuracy of 87.1%. The freely available ‘Stanford-stage’ algorithm ( Stephansen et al., 2018) was trained and evaluated on 10 clinical cohorts (~3000 recordings), and presented with an accuracy of 87% against consensus human sleep stage scoring. Here, the overall accuracy ranged from 89.8% in healthy adults/adolescents to 72.1% in patients with Parkinson’s disease. Thereafter, and algorithm termed ‘Z3Score’ ( Patanaik et al., 2018) was published, trained and evaluated on ~1700 PSG recordings from four datasets. The overall Cohen’s kappa on the testing set was 0.68 (n = 1000 PSG nights). Sun et al., 2017 reported an algorithm that was trained and evaluated on 2000 PSG recordings from a single-sleep clinic. For a more in-depth review, we refer the reader to Fiorillo et al., 2019. While an exhaustive review of such sleep-staging algorithms is beyond the scope of this article, we offer an overview of some of the most relevant algorithms within the last 5 years. Several such automatic sleep-staging algorithms have emerged in recent years. That is, different human sleep-scoring experts presented with the same recording will likely end up with somewhat dissimilar sleep-staging evaluations, and even the same expert presented with the same recording assessed at two different time points will arrive at differing results.Īdvances in machine learning have motivated efforts to try and classify sleep using automated systems. Moreover, the same scoring individual typically experiences low intrascorer agreement of the same sleep recording (~90%, Fiorillo et al., 2019). As if not more critical, this human-scored approach suffers from issues of lower than desirable interscorer consistency of agreement (~83% agreement, Rosenberg and Van Hout, 2013). It is a non-trivial, time-intensive process, with visual scoring of a single night of human sleep requiring up to 2 hr to complete by a well-trained individual. However, since sleep staging is performed visually by human experts, it represents a pragmatic bottleneck. Each epoch is then assigned a sleep stage based on standard rules defined by the American Academy of Sleep Medicine (AASM, Berry et al., 2012 Iber et al., 2007).Įvery night, thousands of hours of sleep are recorded in research, clinical, and commercial ventures across the globe. The classification of sleep stages across the night provides information on the overall architecture of sleep across the night, as well as the duration and proportion of the sleep stages, all of which inform the diagnosis of sleep disorders and specific diseases states.Ĭurrently, such sleep scoring is typically performed by humans, accomplished by first dividing the night of PSG recording into 30 s segments (called ‘epochs’). Polysomnography (PSG) – the simultaneous measurement of brainwaves, eye movements, muscle activity, heart rate, and respiration – is the gold standard for objective physiological quantification of human sleep. Considering this impact, the demand for quantifying human sleep at a research-, clinical-, and consumer-based level has increased expeditiously over the past decade ( Fleming et al., 2015 Shelgikar et al., 2016). Improving sleep health has therefore emerged as a preventive strategy to reduce the risk of cardiovascular and metabolic disease, all-cause mortality risk, and more recently, the accumulation of Alzheimer’s disease pathology within the brain ( Cappuccio et al., 2010 Leary et al., 2020 Cappuccio and Miller, 2017 Ju et al., 2014 Winer et al., 2020). Within the brain, sufficient sleep facilitates optimal learning, memory, attention, mood, and decision-making processes ( Ben Simon et al., 2020 Walker, 2009). Adequate sleep supports a panoply of physiological body functions, including immune, metabolic, and cardiovascular systems ( Besedovsky et al., 2019 Cappuccio and Miller, 2017 Harding et al., 2020).

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