New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Abstract

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for "non-circadian rhythm" sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The "endpoint" of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

Keywords: apnea/hypopnea index; circadian rhythm; insomnia; polysomnography; precision medicine; signal processing; sleep apnea; sleep disordered breathing.

FIGURE 1

Electroencephalography activity during sleep.

FIGURE 2

Schematic overview of the current metrics derived from standard polysomnography and the potential to make better use of these extensive neurophysiological signals provide novel insight into sleep neurobiology, treatment prediction and to better link with key clinical and health outcomes. Refer to the text for further detail. CPAP = continuous positive airway pressure, CV = cardiovascular, EEG = electroencephalography, EMG = electromyography, EOG = electrooculography, ECG = electrocardiography, HGNS = hypoglossal nerve stimulation, MAS = mandibular advancement splint, PPG = Photoplethysmography, REM = rapid eye movement, SpO₂ = estimated arterial blood oxygen saturation and UA = upper airway.

FIGURE 3

Spectrogram of sleep EEG signals using methods developed in Prerau et al. (2017). A transition from slow wave sleep (1) to N2 sleep (3) with an arousal in the middle (2) is observed. Slow wave sleep is characterized by high absolute power at frequencies less than 4 Hz and very little power at high frequencies, thus making identification of high frequency (8–16 Hz) arousals straight-forward. The transition from arousal to N2 sleep is also very specific, with a reduction in high frequency power, a sparse low frequency burst (likely reflecting K-complexes), sometimes followed by a burst of 12–16 Hz activity.

FIGURE 4

Cyclical variation in delta power across the night.

FIGURE 5

Schematic of the four key endotypic traits that contribute to OSA pathophysiology. (A) Impaired pharyngeal anatomy/collapsible upper airway. Non-anatomical endotypes include: (B) Poor pharyngeal dilator muscle function including poor responsiveness/activation to negative pharyngeal pressure/airway narrowing, (C) a low respiratory arousal threshold (waking up too easily to minor pharyngeal narrowing events); and (D) Unstable respiratory control/increased sensitivity to minor changes in CO₂ (high loop gain). Each of these endotypes is a target for therapy or a “treatable trait.” Adapted from Carberry et al. (2018) and Aishah and Eckert (2019).

FIGURE 6

Schematic of novel and emerging approaches to monitor the sleeping environment and track key health measures via “the bedroom of the future.” Refer to the text for further detail.