Reinventing polysomnography in the age of precision medicine

Lim, Diane C.; Mazzotti, Diego R.; Sutherland, Kate; Mindel, Jesse; Kim, Jin‐Young; Cistulli, Peter A.; Magalang, Ulysses J.; Pack, Allan I.; Chazal, Philip de; Penzel, Thomas; Investigators, Sagic

doi:10.1016/j.smrv.2020.101313

Cited by 75 publications

(35 citation statements)

References 93 publications

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“…28,40,41 Furthermore, intelligent digital algorithms can be applied to expand the array of PSG metrics, such as sleep depth, arousal characteristics, and hypoxic burden, among others. 42 The big data generated by thousands of polysomnographic recordings are an excellent substrate for novel analytical approaches. Innovative scoring techniques are expected to come out of the application of artificial intelligence.…”

Section: Precision In Sleep Medicinementioning

confidence: 99%

Future Treatment of Sleep Disorders

Pevernagie

2021

Sleep Medicine Clinics

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Sleep medicine is cataloged according to a conventional disease classification system. Disease models are rooted in the pathophysiology of sleep. Polysomnography and other tests are used to demonstrate pathophysiological mechanisms underlying the currently known sleep disorders. Although many patients with sleep disorders may be adequately managed by this pathophysiological approach, therapeutic results are insufficient in some subjects, the causes of which lie in nonspecificity of symptoms, coincidental association between symptoms and pathophysiological endotype, as well as co-occurrence of two or more pathologic mechanisms affecting sleep. As co-occurrence of different pathogenetic mechanisms may produce phenotypes that are at odds with the idealized description of classic sleep disorders, the result of standard therapeutic interventions may be disappointing. The mechanisms underlying the expression of certain traits may be a substrate for targeted treatment. Treatable traits are characterized by biomarkers with predictive value as to beneficial treatment response. The challenge for the future is to gradually embrace the principles of systems medicine and to shift gear toward managing treatable traits in sleep disorders surpassing the limits of the traditional nosologic approach.

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Section: Precision In Sleep Medicinementioning

confidence: 99%

Future Treatment of Sleep Disorders

Pevernagie

2021

Sleep Medicine Clinics

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“…But this combination is still under discussion and outcome studies are needed for achieving conclusive results. This approach requires further discussion and leads to the idea of ‘reinventing polysomnography’ and more specifically, the polysomnography (PSG) reporting [ 5 ▪ ]. However, this is a long-term goal because mortality and morbidity data need to be collected.…”

Section: Introductionmentioning

confidence: 99%

Alternative algorithms and devices in sleep apnoea diagnosis: what we know and what we expect

Penzel

Fietze

Glos

2020

Current Opinion in Pulmonary Medicine

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Purpose of review Diagnosis of sleep apnoea was performed in sleep laboratories with polysomnography. This requires a room with supervision and presence of technologists and trained sleep experts. Today, clinical guidelines in most countries recommend home sleep apnoea testing with simple systems using six signals only. If criteria for signal quality, recording conditions, and patient selection are considered, then this is a reliable test with high accuracy. Recent findings Recently diagnostic tools for sleep apnoea diagnosis become even more simple: smartwatches and wearables with smart apps claim to diagnose sleep apnoea when these devices are tracking sleep and sleep quality as part of new consumer health checking. Alternative and new devices range from excellent diagnostic tools with high accuracy and full validation studies down to very low-quality tools which only result in random diagnostic reports. Due to the high prevalence of sleep apnoea, even a random diagnosis may match a real disorder sometimes. Summary Until now, there are no metrics established how to evaluate these alternative algorithms and simple devices. Proposals for evaluating smartwatches, smartphones, single-use sensors, and new algorithms are presented. New assessments may help to overcome current limitations in sleep apnoea severity metrics. Video abstract .

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“…The most common way to provide a detailed assessment of sleep is to record a polysomnogram (PSG) which provides range of signals from a sleeping patient [7]. Sleep technicians then manually assess the PSG.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Apnoea and Non-apnoea Arousals from the Polysomnogram using a Neural Network Classifier

Chazal

Sadr

2020

2020 Computing in Cardiology Conference (CinC)

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In this study we present a system for automated processing of signals from the polysomnogram (PSG) for the detection of apnoea and non-apnoea arousals. The PSG signals were divided into 15 second epochs and 59 time-and frequency-domain features were derived for each epoch. Features from adjacent 4 epochs were combined and processed with a bank of ten feed-forward neural networks each with a single hidden layer of 20 units. The system outputs a 200 Hz annotation signal containing probability estimates that each sample was associated with an apnoea or non-apnoea arousal, or noarousal. Data from the Physionet Computing in Cardiology Challenge 2018 was used to develop and test the system. Performance of the system was assessed using three class and two class metrics. With the system classifying three classes, the volume under the receiver operator characteristic (ROC) surface was 0.75 with an optimal specificity of 0.72, a sensitivity of 0.76 for the apnoea arousals, and a sensitivity of 0.69 for the nonapnoea arousals. When the two arousal classes were combined into one arousal class, the area under the precision recall curve was 0.74, the area under the ROC curve was 0.91, with an optimal specificity and sensitivity of 0.85.

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Reinventing polysomnography in the age of precision medicine

Cited by 75 publications

References 93 publications

Future Treatment of Sleep Disorders

Future Treatment of Sleep Disorders

Alternative algorithms and devices in sleep apnoea diagnosis: what we know and what we expect

Prediction of Apnoea and Non-apnoea Arousals from the Polysomnogram using a Neural Network Classifier

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