Hypersomnia

Essentially, a sleep detection method is a function that classifies the sleep state of a patient. Most sleep detection methods such as wrist actigraphy or mobile apps consider a binary function, where the state can be classified as Awake/Sleep. More sophisticated methods consider a ternary function: Awake/NREM/REM. And, finally, the most advanced methods, such as polysomnography—often used as the gold standard—consider a quinquenary function: Awake/N1/N2/N3/REM. Hence, any method can produce a two-dimensional chart where the X-axis is Time, and the Y-axis is the State of the Patient. In the particular case of polysomnography, the Y-axis has five possible values; thus, it can determine the sleep stage of the patient at any time, and study the transitions occurring between the states. Of course, a sleep study such as a polysomnography often produces much more complementary information that can be used, e.g., to diagnose sleep diseases. Among the information reported by a polysomnography we find oxygen saturations, limb movements, apneas, respiratory events by body position, etc. The interested reader is referred to Robertson, Marshall & Carno (2014) , Pandi-Perumal, Spence & BaHammam (2014) and Armon et al. (2016) for information about sleep study reports and their interpretation and usage.
The information that is common to the majority of sleep detection methods is the one that refers to a binary state classification (i.e., Awake/Sleep), because this is achieved by the basic methods, and subsumed by the advanced methods. Table 1 defines the basic parameters that can be collected by a binary state classification method. In grey, we show the primary data that should be collected by the sleep detection device and, in white, we show the most important parameters that can be derived from the primary data.
These parameters are particularly useful to determine the kind of sleep of patients, and each single parameter is relevant for a different sleep disorder or disease. For instance, the sleep onset, sleep latency, and total sleep time are essential to diagnose patients with insomnia. Similarly, an excess in the awakening and arousal indices suggests increased sleep fragmentation. In addition to the number of sleep states that they are able to detect, a sleep detection method can be classified according to other functional and operational characteristics, such as their underlying technology, which in turn directly affects their precision and validity.
In Fig. 2, we present a taxonomy of sleep detection methods. They all can be classified into two main groups according to whether they need medical assistance (Medical Assistance) or not (Self-Assessment). In this respect, there are methods that have been classified as not requiring medical assistance, such as Questionnaires and Sleep Diaries, even though their interpretation should be normally done by a professional. However, in the current state of the art there are many systems such as mobile apps that provide custom sleep questionnaires and produce reports without medical assistance. Hence, they are classified as Self-Assessment. They both deserve a deep discussion and will be explained separately in ‘Medical Assistance Methods’ and ‘Self-Assessment Methods’, respectively.
Self-Assessment methods include subjective methods such as questionnaires and sleep diaries (the figure lists some instances), and objective methods based on hardware sensors, which in turn can be classified as Contact devices or Contactless devices, depending on whether they need to be in contact with the patient’s body during sleep. Those devices that are based on the echo produced by signals can be further classified into Sonar, Radar, and Lidar devices. All of them will be explained in a dedicated section.

As part of the baseline questionnaire, participants reported five sleep characteristics, based on which we further identified five healthy phenotypes, including no usual insomnia complaints, adequate sleep duration (7 to < 9 h/day), no snoring, morning chronotype, and no frequent daytime sleepiness [9 (link)]. We scored participants from 0 to 5, according to the count of healthy characteristics and categorized them into three groups: “healthy sleep” (≥ 4 composite sleep score); “intermediate sleep” (2 or 3 score); and “poor sleep” (≤ 1 score). This categorization has been proven to distinguish different CVD risk profiles, and the simple addition approach showed a similar predictive power to a weighted score [9 (link)]. The original questions and options were provided in Additional file 1: Table S2. These definitions and scores have shown excellent convergent validity with CVD incidence and mortality [8 (link), 9 (link)].
We identified recent clinical sleep disorder events (2 years before enrolment) based on inpatient admissions, primary care clinical events, and sleep disorder-specific prescriptions (BNF Chapter 4 Sect. 1: hypnotics and anxiolytics). Modified from the definition provided by the American Sleep Association and American Academy of Sleep Medicine [16 , 17 (link)], we distinguished five different sleep disorders, including insomnia, hypersomnia, sleep-related breathing disorders, circadian rhythm sleep disorders, and parasomnias (including sleep-related bruxism). In addition, we grouped hypersomnia, circadian rhythm sleep disorders, parasomnias, non-specific sleep disorders (e.g., “poor sleep pattern”), and sleep medication prescriptions without a corresponding clinical event into “other sleep disorders.” We provided detailed codes for each classification system in Additional file 1: Table S3-5. Since the clinical diagnosis of sleep disorders mainly refers to more than one self-reported sleep characteristic, we did not further integrate both self-reported and clinically unhealthy sleep.