RU2749068C2 - System and method for sleep research - Google Patents

Abstract

FIELD: medicine.SUBSTANCE: group of inventions relates to medicine, namely to the system and method of sleep research. In this case, the physiological parameters of the subject are monitored during sleep and the sleep phase of the subject is monitored using a set of sensors. It is determined from the output of the sensor set that the sensor is malfunctioning or disconnected so that intervention in the subject's condition is required to maintain or repair the sleep research system. A time point for performing the intervention depending on the subject's sleep phase is determined by the processor from the output of a set of sensors of the subject's sleep phase. The output data are provided with respect to the point in time for performing the intervention that is minimally disturbing to the subject.EFFECT: effective sleep research is provided with minimal impact on the patient's sleep by performing the intervention at the optimal time, i.e. when the sleep phase reaches a preferred phase, in a way that is less likely to wake the patient up and thus potentially does not affect sleep research results.13 cl, 6 dwg

Description

FIELD OF TECHNOLOGY

The present invention relates to systems for conducting studies of nocturnal sleep in a subject.

LEVEL OF TECHNOLOGY

It is well known that poor or abnormal sleep is both widespread and medically undesirable. It is known that sleep patterns can be improved if the patient is aware of their current sleep quality.

By providing feedback on sleep quality, patients can change behavior and / or adjust sleep patterns in ways that improve sleep quality. It is known that indicating the quality of sleep may require identifying the different sleep phases during sleep, as well as the transitions between them. It is known that sleep phases typically include a rapid eye movement (REM) phase and one or more non-rapid eye movement (REM) sleep phases.

It is also widely known that polysomnography (PSG) can be used to analyze, detect and / or determine the current sleep phase of a subject. Such analysis, for example, is performed as part of a sleep study.

During nighttime sleep studies, various incidents may arise that require intervention to correct and ensure high quality measurements. A common example is a sensor dropped due to patient movement. Existing PSG systems can detect a number of cases and generate an alarm based on missing or incorrect measurement values sent to a separate control panel.

In addition, night sleep studies typically involve room and patient surveillance cameras to provide visual detection of cases by the technician present during sleep. After that, as a rule, a specialist who is present during sleep enters the room to the patient and solves the problem.

Although the above approach provides high quality measurements from a technical point of view, since the operating time of the PSG system is maximized, problem solving can also interfere with the patient's sleep. Due to the conditions in which the patient is located, his sleep can no longer be considered optimal, and it should not worsen even more.

DISCLOSURE OF THE INVENTION

The present invention is defined in the claims.

According to the examples, in accordance with an aspect of the present invention, there is provided a sleep research system comprising:

a set of sensors for monitoring physiological parameters of a subject during sleep as part of a sleep study and for monitoring a subject's sleep phase;

processor capable of:

determining from the output of the sensor set that the sensor is abnormal or disconnected so that intervention is required in the subject's condition to maintain or repair the sleep research system; and

determining from the output of the set of sensors the point in time for performing the intervention depending on the sleep phase of the subject and providing the output with respect to the point in time for performing the intervention.

The system, for example, provides an optimal timing for an intervention in real time, which minimizes potential sleep disturbance, overall resulting in higher quality sleep study results. Thus, the time to perform the intervention provides is minimally disturbing to the subject.

The system is configured to detect cases that require intervention and generate appropriate output such as an alarm. In addition, the current sleep phase is determined in real time. Based on the sleep phase, the optimal timing takes into account what the appropriate intervention would be at a given time, since awakening the patient during some sleep phases is less disturbing to sleep than during other sleep phases.

The set of sensors may comprise a first set of sensors for monitoring physiological parameters of a subject during sleep and a second set of sensors for monitoring sleep phase, the first set and the second set being mutually exclusive.

For example, the first set of sensors can monitor parameters such as blood oxygen levels, heart rate, respiratory rate, and eye and arm and / or leg movements. The second set of sensors may, for example, contain EEG (electroencephalographic) sensors.

The set of sensors may comprise a first set of sensors for monitoring physiological parameters of a subject during sleep and a second set of sensors for monitoring sleep phase, the second set being a subset of the first set.

For example, the second set may contain EEG sensors that are also used as part of sleep research. In addition, the first set may contain selected sensors that monitor parameters such as blood oxygen levels, heart rate, respiratory rate, eye and leg movements, and EEG. Thus, the indication of the optimal moment in time does not require the use of any additional sensors in addition to those already present for the study of sleep.

For example, intervention may be necessary if the normal robot sensor (from the first set) is disrupted or the sensor is dropped from the subject. This will be detected based on the sensor output.

Thus, the set of sensors may comprise at least an EEG sensor for monitoring sleep phases. PPG (photoplethysmographic) sensor can be used to monitor heart rate and / or respiration. Together, the sensors can provide the ability to implement a polysomnography system.

Outputs that are indicative of the point in time for the intervention to be performed may include:

the output at the point in time to perform the intervention and / or

pre-point in time outputs for performing the intervention, which provide a prediction of when the intervention will be performed.

Thus, the user of the system can be indicated when an intervention is planned, either at a given moment in time, or before a specified moment in time, or in both cases.

In addition, the processor can be configured to determine the expected time to the end of sleep or the end of the sleep study, and also be able to take this into account when determining the time to perform the intervention.

In addition, in the examples, according to yet another aspect of the present invention, there is provided a sleep research method comprising:

monitoring the physiological parameters of the subject during sleep and monitoring the sleep phase of the subject using a set of sensors;

determining, from the output of the sensor set, that the sensor is malfunctioning or disconnected such that intervention is required in the subject's condition to maintain or repair the sleep research system; and

determining from the output of the set of sensors a point in time to perform the intervention depending on the sleep phase of the subject and providing an output with respect to the point in time for performing the intervention.

This method provides for the maintenance of the sleep study system during the sleep study in a manner that is less likely to wake the patient up and thus potentially does not interfere with the sleep study results.

Monitoring may include:

using a first set of sensors to monitor the physiological parameters of a subject during sleep; and using a second set of sensors to monitor sleep phases, the first set and the second set being mutually exclusive.

Instead, monitoring can include:

using a first set of sensors to monitor the physiological parameters of a subject during sleep; and using a second set of sensors to monitor sleep phases, the second set being a subset of the first set.

Monitoring may include EEG monitoring of sleep phase and / or PPG monitoring.

Providing outputs that are indicative of the point in time for an intervention may include:

issuing an output at a point in time to perform an intervention and / or

providing pre-point in time output for performing the intervention, which provides a prediction of the point in time for performing the intervention.

In addition, the expected time until the end of sleep can be determined, and this can be taken into account when determining the point in time to perform the intervention.

The present invention may be at least partially implemented in software.

BRIEF DESCRIPTION OF DRAWINGS

Examples of the present invention will be described in more detail below with reference to the accompanying drawings, in which:

in fig. 1 shows a set of signals used to determine sleep phases;

in fig. 2 shows how different sleep phases can occur during the night;

in fig. 3 shows a part of FIG. 2;

in fig. 4 shows a sleep research system;

in fig. 5 shows a method for studying sleep; and

in fig. 6 shows a general computer architecture suitable for performing signal processing.

CARRYING OUT THE INVENTION

The present invention provides a sleep research system comprising a set of sensors for monitoring physiological parameters of a subject during sleep as part of a sleep study and for monitoring a subject's sleep phase. It is determined whether intervention in the subject's condition is necessary to maintain or repair the sleep research system. If so, then based on the sleep phase of the subject, time is also obtained to perform the intervention, in particular so that it is minimally disturbing to the subject.

Sleep testing can be a polysomnographic test that is used to diagnose or rule out many types of sleep disorders, including narcolepsy, idiopathic hypersomnia, periodic limb movement syndrome (PMS), behavior disorder during REM, parasomnia, and sleep apnea.

In a conventional polysomnographic examination, brain waves are recorded using an EEG (electroencephalographic) sensor, blood oxygen levels are recorded using a PPG (photoplethysmographic) sensor, and heart rate and respiratory rate are recorded using PPG and / or ECG (electrocardiographic) sensors, as well as movement eyes and feet during examination - using optical sensors, accelerometers or other sensors.

The type of information that is monitored for sleep research depends on its purpose. One common goal is the diagnosis of sleep apnea, in which the main information to be obtained is the cases of apnea and hypopnea received, as determined by breathing sensors and PPG sensors. The set of sensors required for this type of sleep study, for example, is built in as part of a home sleep study system that does not require EEG detection. It is also a very common type of sleep study in sleep research laboratories, in which case a wider range of parameters are measured.

Sleep research may involve sensors that provide the ability to monitor sleep phases. Alternatively, additional sleep monitoring sensors may be provided as separate information for sleep studies if the sleep sensors are intended for a different purpose.

Other sensors, such as EEG sensors, ECG sensors, and PPG sensors, can be used to provide an indication of sleep phases. Thus, even if the EEG sensors that were originally used to detect sleep phases subside or otherwise cease to function, other sensors that are present as part of the sleep research system can be used to continue to detect sleep phases.

During sleep, subjects typically alternate between a rapid eye movement (REM) phase and three or four different REM phases, depending on the models and / or definitions used. The phases of NBDH are usually referred to as phase 1 - phase 3 or phase 4, as appropriate. By defining sleep phases for a subject, a phase pattern can be obtained.

FIG. 1 shows the characteristic signs of brain activity in different phases of sleep. In particular, in FIG. 1 shows general illustrative graphs for an EEG (electroencephalographic) study, an EOG (electrooculographic) study, and an EMG (electromyographic) study during wakefulness, the REM phase, and the NRDH phases.

EEG measures the electrical activity of the brain, EOG measures eye movement, and EMG measures muscle activity, for example using a surface electrode or an array of electrodes.

Typically, EEG signals, for example, can be measured and / or received using one or more electrodes located on the subject's head. The amplitude of these signals, as well as the characteristics of peaks, troughs, carotid spindles, k-complexes, slow waves, and / or frequency characteristics in these signals can be analyzed to distinguish the subject's current sleep phase. For example, slow waves are known to be more common in sleep phase 3 and sleep phase 4, while sleep spindles may be more common in sleep phase 2.

FIG. 2 shows a subject going through five sleep phases over a period of approximately 8 hours of sleep, as recorded by EEG. FIG. 3 shows in more detail approximately the first two hours of FIG. 2. For example, in FIG. 3 shows the carotid spindle in phase 2, in which the amplitude increases at the center of the carotid spindle. Analysis of the signals in FIG. 1 and 2, for example, frequency analysis can be used to distinguish between different sleep phases, for example, based on differing characteristics during the sleep phase.

There are various known systems for monitoring sleep phase, i. E. to determine sleep phases that are not limited to a combination of EEG, EOG and EMG signals.

The most typical system uses at least an EEG signal as shown above, obtained using electrodes attached to the scalp. The measured activity of the brain, even from the EEG signal, can be classified as sleep phases of NBDH (N1, N2, N3), REM phase of rapid eye movement and phase of wakefulness.

However, it has been suggested that other parameters such as ECG and / or respiration and body movement signals could be used. For example, this is suggested in WO 2009/128000.

In addition, it is known to provide stimuli to a subject and use responses to them to provide an indication or confirmation of a sleep phase. This approach is, for example, disclosed in WO 2014/118693. In this system, sensory stimuli can include visual stimuli, auditory stimuli, tactile stimuli, olfactory stimuli, electromagnetic stimuli, somatosensory stimuli, other sensory stimuli, and / or any combination and / or sequence thereof. Thus, the source of the stimulus may include one or more of a light source, speaker, electro-acoustic transducer, vibrating element or device, device or system configured / configured to generate odors, or electrodes.

Any known sensor arrangement can be used in the present invention to provide a sleep phase determination for a subject. It should be noted that in this context, the state of wakefulness is included as one of the phases of sleep.

FIG. 4 shows the sleep research system.

It contains a set of sensors for monitoring the physiological parameters of a subject during sleep as part of a sleep study and for monitoring the subject's sleep phase.

The sensors include an EEG sensor 12 in the form of a set of electrodes for the scalp, an EOG sensor 14 in the form of an optical system built into a pair of glasses, an ECG sensor 16, an EMG sensor 18 (in this example attached to the arm), a PPG sensor 20 ...

They are just examples of sensor types that can be used to collect physiological information about a subject. Other examples are accelerometers, force belts (chest and abdomen), oral heat sensors, nasal discharge sensors, microphones.

The output from a subset of these sensors can be interpreted to provide a sleep phase determination.

The usual set of channels recorded during polysomnography (PSG) have both redundant and some sensor signals that are more important than others. For example, typically there are four or more EEG channels and two amplification channels. Although not ideal, the recording can only be recorded with one EEG channel and one amplifier channel.

Since sleep studies are usually done to assess sleep-related breathing disorders, airflow signals may be more important than, for example, EMG signals from a leg. The sleep practitioner would not risk waking the patient up to correct the EEG electrodes if there was still a usable EEG channel, but would wait for the natural awakening to enter and adjust the signal. However, if there is no suitable flow path, this leads to patient concern.

It should be noted that sleep research can also be used for purposes other than breathing disorders. For example, if there is a suspicion of restless legs syndrome (RLS), it is very important to place EMG sensors on the legs.

The processor 22 is configured to determine, from the output of the sensor set, whether it is necessary to intervene in a subject's condition to maintain or repair the sleep research system.

This usually occurs when the sensor is disconnected from the subject or refuses to work for some other reason. Processor 22 may detect this based on received signals that are inconsistent with normal signal capture.

The processor can then determine, from the output of the set of sensors (i.e., those that are still working correctly), the subject's current sleep phase.

From this sleep phase information, the optimal timing for the intervention, which is less disturbing to the subject, can be derived.

The system includes an output interface device 24, which in this case is shown as a remote handheld device, such as a smartphone, with which the processor communicates. It provides an alarm that the system needs service, as well as an indication of when intervention is most appropriate. An output indicating the optimum time is given.

This system provides optimal timing for interventions in real time, which minimizes potential sleep disturbances, overall resulting in higher quality sleep study results.

By far the best option would be to enter the room when the patient is awake. Otherwise, there is a strong preference for avoiding REM sleep disturbances, as REM sleep is shorter during the night and breathing is often very different from other phases of consciousness. During slow wave sleep (N3 and N4), patients are less likely to wake up due to the technician entering the room. Thus, the intervention can be performed when subjects are in either N3 or N4 sleep phases. Manipulating a patient subject during these sleep phases is less disturbing and thus less likely to result in awakening than during other sleep phases.

The timing of when to enter or not enter a room will in practice depend on a decision matrix that estimates the urgency of signal correction. In particular, it is determined whether or not the test without signal is suitable, as well as the patient's level of consciousness.

The system can compensate for the lack of sleep professionals by helping them make decisions.

Sleep research itself may require identification of sleep phases, but this is not essential. Thus, in some cases, the sensors for sleep research (first set) are completely separate from those (second set) that are needed to perform sleep phase detection. In other cases, sensors overlap with those involved in sleep research as a subset of sensors that can be used to provide sleep phase detection.

As is clear from FIG. 4, a set of sensors for sleep research can monitor parameters such as blood oxygen level (PPG sensor 20), heart rate (ECG sensor 16), respiration rate (PPG sensor 20 or other sensors not shown) as well as movement of the eyes (EOG sensor 16) and arms or legs (EMG sensor 18). The second set of sensors may, for example, contain EEG sensors (helmet 12 of EEG sensors).

The output indicating the optimal time can be issued at the appropriate time, i. E. when the sleep phase reaches the preferred phase. However, based on the analysis of the sleep pattern up to this point, an estimate can also be made of when the sleep phase is likely to reach this phase. Thus, a service alarm can be issued as well as an estimate of when the service should be performed. When the actual desired sleep phase is reached, an additional alert may be issued. In addition, the processor can determine the expected time until the end of sleep and take this into account when determining the optimal point in time.

More generally, in addition to determining sleep phases, other factors can be considered, one example of which is the time until the end of the sleep study. After that, all the information can be combined to determine the best time to solve the problem. The decision can also be based on the importance rank of the various cases found and their combinations. For example, only if a certain severity threshold value related to a certain sleep phase is met will an alarm be issued.

The output device can show the current sleep phase and can show the results of a prediction algorithm, for example, based on small but significant changes in the sleep pattern, which can be used to predict the forthcoming change in the sleep phase. As stated above, this can be used to predict when a solution to a problem will be appropriate in the future.

The output device may be an integral part of the system, for example located at a control room where sleep research is monitored. Thus, there is no need for a separate remote device as shown.

FIG. 5 illustrates a sleep study method comprising monitoring physiological parameters of a subject during sleep at step 50. These parameters are used at step 52 to determine the subject's sleep phase.

At 54, it is determined whether the subject needs to be intervened to maintain or repair the sleep research system. This is based on abnormalities in the signals received from one or more sensors.

At 56, an optimal time is determined for the intervention so that it is less disturbing to the subject.

At 58, an output is provided that is indicative of the optimum point in time. They can be issued at the optimal point in time or before the optimal point in time, or in both cases.

This method maintains the sleep study system during the sleep study in a manner that is less likely to wake up the patient and thus potentially does not interfere with the sleep study results.

Processor 22 implements an algorithm for processing sensor data. FIG. 6 shows an example of a computer 60 for implementing a processor.

Computer 60 includes, but is not limited to, PCs, workstations, laptop computers, PDAs, readers, servers, storage, and the like. In general, in terms of hardware architecture, computer 60 may include one or more processors 61, memory 62, and one or more I / O devices 63 that are coupled to communicate via a local interface (not shown). The local interface can be, for example, but not limited to, one or more buses or other wired or wireless connections as known in the art. The local interface can have additional elements such as controllers, buffers (caches), drivers, repeaters, and receivers to enable communication. In addition, the local interface may include address, control and / or data connections to provide appropriate communications between the above components.

Processor 61 is a hardware device for executing software that can be stored in memory 62. Processor 61 can be virtually any dedicated or commercially available processor, central processing unit (CPU), digital signal processor (DSP), or a multi-processor sub-processor. associated with the computer 60, and the processor 61 may be a semiconductor microprocessor (in the form of a microchip) or a microprocessor.

Memory 62 may comprise any one or a combination of volatile memory elements (e.g. random access memory (RAM), such as dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and nonvolatile memory elements (e.g. , ROM, Erasable Programmable Read Only Memory (EPROM), Electronically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (EPROM), Tape, CD ROM, Disk, Floppy Disk, Cartridge, cassette or the like, etc.). In addition, memory 62 may include electronic, magnetic, optical, and / or other types of media. It should be noted that the memory 62 may have a distributed architecture where the various components are located remotely from each other, but they can be accessed by the processor 61.

The software in memory 62 may contain one or more separate programs, each of which contains an ordered list of executable instructions to implement logical functions. The software in memory 62 includes a suitable operating system (OS) 64, a compiler 65, source code 66, and one or more applications 67, in accordance with illustrative embodiments.

Application 67 contains a number of functional components such as compute blocks, logic blocks, function blocks, processes, operations, virtual blocks and / or modules.

The operating system 64 controls the execution of computer programs and provides programming, I / O control, file and data management, memory management and communication management, and related services.

Application 67 can be a source program executed by a program (object code), a script, or any other entity containing a set of instructions to be executed. If it is a source program, then the program is usually translated by a compiler (such as compiler 65), a build program, an interpreter program, or the like, which may or may not be included in memory 62 so as to work properly with operating system 64. In addition Moreover, application 67 can be written as an object-oriented programming language that has data classes and methods, or a procedure programming language that has programs, subroutines and / or functions, for example, but not limited to, C, C ++, C #, Pascal, BASIC, API requests, HTML, XHTML, XML, ASP scripts, JavaScript, FORTRAN, COBOL, Perl, Java, ADA, .NET and the like.

The I / O devices 63 may include input devices such as, for example, but not limited to, a mouse, keyboard, scanner, microphone, camera, and so on. In addition, the I / O devices 67 may also comprise output devices such as, but not limited to, a printer, a display, etc. Finally, the I / O devices 63 may further comprise devices. which transmit both input and output data, such as, but not limited to, a NIC or modulator / demodulator (to access remote devices, other files, devices, systems, or network), radio frequency (RF) or other transceiver, telephone interface, bridge, router, etc. The I / O devices 63 also contain components for communicating over various networks such as the Internet or intranet.

When computer 60 is operating, processor 61 is configured to execute software stored in memory 62 to communicate data to and from memory 62 and to generally control the operation of computer 60 in accordance with the software. The application 67 and the operating system 64 are read in whole or in part by the processor 61, possibly they are buffered in the processor 61 and then executed.

It should be noted that when the application 67 is implemented in software, the application 67 may be stored on virtually any computer-readable medium for use by or in connection with any system or method associated with / associated with a computer application. In the context of this document, a computer-readable medium can be an electronic, magnetic, optical, or other physical device or medium that can contain or store a computer program for use by or in connection with a system or method associated with / associated with the use of a computer.

The example above is mainly aimed at preventing the patient from waking up. However, there are times when intervention is necessary and the subject must be awakened - for example, to ensure his wellness or safety. In this case, the sleep phase can be chosen so that the subject's awakening is less anxious. This will be another point in time for intervention when the patient cannot be woken up.

As an example, the concept of awakening a patient at an optimal time is described in US 8876737. It is contemplated that awakening may occur during the N1 and N2 phases of sleep. Thus, the system in accordance with this application can determine the optimal time, as N1 and N2, if it is determined that the patient should be awakened.

Thus, the term "less disturbing to the subject" can include his intentional awakening or his intentional non-awakening.

The examples above are based on detecting a sensor failure and determining the correct time for intervention. In addition, the optimal timing for intervention may be based on predicted sensor failure rather than detected sensor failure. Thus, if a sensor failure is predicted, the actual failure can be prevented by early replacement or re-attachment of the sensor. For example, such a prediction could be based on a degradation in data quality, where the current data quality is still sufficient to obtain the desired information, but the trend in data quality shows that this will stop in the near future. As an example, a flow sensor can slowly fall out, resulting in a decrease in flow amplitude despite the force belts showing consistent breathing forces.

Thus, "determining if a subject needs to be intervened to maintain or repair the sleep research system" includes detecting a sensor failure or detecting degradation of the sensor signal, which is indicative of future sensor failure. This may include any other adaptations or transducer adjustments needed during a sleep study.

Other variants of the disclosed embodiments may be understood and implemented by a person skilled in the art in the implementation of the claimed invention in practice, after familiarization with the figures, description and the attached claims. In the claims, the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The mere fact that some measures are indicated in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. All reference numbers in the claims should not be construed as limiting the scope.

Claims (27)

1. A sleep research system comprising: a set of sensors (12, 14, 16, 18, 20) configured to monitor the physiological parameters of a subject during sleep as part of a sleep study, and also configured to monitor the subject's sleep phase; processor (22) configured to: determining from the output of the set of sensors (12, 14, 16, 18, 20) that the sensor is malfunctioning or disconnected so that intervention in the subject's state is necessary to maintain or repair the sleep research system; characterized in that the processor is also configured to determine from the output data of the set of sensors (12, 14, 16, 18, 20) the sleep phase of the subject and determine from the output data of the set of sensors (12, 14, 16, 18, 20) the point in time for performing the intervention depending on the sleep phase of the subject and providing an output with respect to the point in time for performing the intervention that is minimally disturbing to the subject. 2. The system according to claim 1, in which the set of sensors contains: a first set of sensors for monitoring the physiological parameters of a subject during sleep; and a second set of sensors for monitoring sleep phases. 3. A system as claimed in any one of the preceding claims, wherein the set of sensors comprises at least an EEG sensor for monitoring sleep phases. 4. A system according to any of the preceding claims, wherein the set of sensors comprises at least a PPG sensor, wherein the sleep research system is a polysomnographic system. 5. The system according to any of the previous paragraphs, in which the output related to the point in time for performing the intervention contains: the output at the point in time to perform the intervention and / or output before a point in time to perform an intervention that predicts a specified point in time. 6. A system according to any of the preceding claims, in which the processor (22) is also configured to determine the expected time to the end of sleep and to be taken into account when determining the time point for performing the intervention. 7. A method for studying sleep, including: (50) monitoring the physiological parameters of the subject during sleep and (52) monitoring the sleep phase of the subject using a set of sensors (12, 14, 16, 18, 20); (54) determining from the output of the sensor set that the sensor is malfunctioning or disconnecting the sensor so that intervention is necessary in the subject's state to maintain or repair the sleep research system; and (56) determining, from the output of the sensor set, the sleep phase of the subject and the point in time to perform the intervention depending on the sleep phase of the subject, and outputting the output on the point in time for performing the intervention that is minimally disturbing to the subject. 8. The method according to claim 7, wherein the monitoring comprises: using the first set of sensors to monitor the physiological parameters of the subject during sleep; and using the second set of sensors to monitor the sleep phase. 9. A method according to any one of claims. 7, 8, in which monitoring includes EEG monitoring of the sleep phase and / or PPG monitoring. 10. The method according to any one of claims. 7-9, in which the sleep study is a polysomnographic study. 11. The method according to any one of claims. 7-10, in which the output of the output in relation to the point in time for performing the intervention includes: issuing an output at a point in time to perform an intervention and / or providing pre-point in time output for performing the intervention, which provides a prediction of the point in time for performing the intervention. 12. The method according to any one of claims. 7-11, which includes determining the expected time until the end of sleep and taking it into account when determining the point in time for performing the intervention. 13. Computer-readable media containing a computer program containing a computer program code configured to perform the method according to any one of claims. 7-12 when running the specified program on the computer.

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