FR3059556A1 - METHODS OF OPTIMIZING THE SLEEP OF AN INDIVIDUAL - Google Patents

Abstract

The invention relates to a method and a device for aiding the optimization of sleep for an individual. During a sleep period of the individual, a measurement signal representative of a physiological signal of the individual is acquired. From the measurement signal, a personal cycle or structure characteristic of the individual's sleep is determined. From the cycle or the structure, an optimal time is determined from among an optimal time for waking up of the individual according to a predefined bedtime, and an optimal time for going to bed for the individual according to a preset alarm clock. This optimal time is provided to the individual.

Description

(54) METHODS FOR OPTIMIZING THE SLEEP OF AN INDIVIDUAL.

FR 3 059 556 - A1 _ The invention relates to a method and a device for optimizing sleep for an individual.

During a sleep period of the individual, a measurement signal representative of a physiological signal of the individual is acquired,

From the measurement signal, a cycle or a personal structure characteristic of the individual's sleep is determined.

From the cycle or structure, an optimal time is determined from an optimal time for waking the individual based on a predefined bedtime, and an optimal bedtime for the individual based on an hour. alarm clock.

This optimal time is provided to the individual.

i

METHODS OF OPTIMIZING THE SLEEP OF AN INDIVIDUAL.

The present invention relates to methods of helping to optimize sleep for an individual.

To choose a wake-up time after a sleep period such as a night or a nap, an individual usually has some information which he combines to choose the appropriate moment. Among this information, some correspond to known external elements, for example a time at which the individual has planned an activity the next morning or even a duration of transport or preparation time, others are more subjective as a duration of sleep adapted to its circadian rhythm.

The person thus combines this information to set a wake-up time which will guarantee them, unless there is an unforeseen event, to be able to participate in the chosen activity. She can then, for example, enter this time in a selector of an alarm clock comprising a clock. When the clock reaches the time value indicated by the selector, a wake-up stimulus will be triggered, for example a sound or tactile stimulus, to wake the user.

The same type of combination of information can be used to choose a bedtime. The latter can in particular be a function of the time of awakening, for example so as to maintain a minimum duration of sleep.

In this way, the user defines his sleep time on the basis of his habit and some external information.

However, by such methods, waking time can occur in the middle of a deep sleep range of the user. In this case, the user can take a long time to get out of sleep completely and in particular wake up with a feeling of significant fatigue. In addition, the sleep duration that the user chooses is mainly the result of subjective criteria linked to his impression of fatigue when choosing the time to go to bed and wake up. The user then risks choosing a too long or too short duration of sleep which will potentially lead to a difficult awakening or will risk shifting subsequent periods of sleep, for example by waking up too late to be able to go to bed early the following night.

The present invention improves this situation.

More specifically, the methods, devices and systems according to the invention make it possible to optimize the selection of the waking or sleeping time by the user.

The method according to the invention, implemented by a device and / or system for helping to optimize sleep, makes a prediction of one or more optimal hours for going to bed or for getting up.

The method thus makes it possible to warn, for example in the evening, of the ideal time to go to bed or to wake up, using a predictor of cycle or of sleep structure which determines an optimal window for sleeping or waking up. The predictor uses for example the history of the encephalographic data of sleep, data on the lifestyle of the user and the estimated duration of the sleep. The predictive wake-up call can then extrapolate the person's sleep pattern.

The method can also offer several options for waking up. On weekends, for example, it may indicate two possible hours of waking up based on recent history. He can also suggest a wake-up time more suited to the user's lifestyle and well-being: for example, offer the user to wake up at 11am on a Sunday morning, rather than 2pm to avoid it have trouble falling asleep at night.

The actual wake-up stage of the user can employ a controlled wake-up method, device or system that guarantees a maximum wake-up time while making it even easier to wake the user and reduce the feeling of residual fatigue.

Such a controlled awakening includes, over a period preceding an hour determined by the user, a stimulation generation operation whose intensity is a function of the user's sleep depth so as to generate a gradual awakening of the user. In this way, the user is no longer in deep sleep at the moment of waking up. Furthermore, this also makes it possible to ensure the security of the alarm time. For this, a conventional alarm clock, that is to say generating a stimulus only at the time defined by the user, can be set up on the mobile device at the time determined by user. If the intelligent alarm clock alone allows the user to wake up, a signal is transmitted from the device to the mobile device via a long-distance link, for example Bluetooth, to cancel the conventional alarm clock. This signal is for example transmitted following the withdrawal of the device from the head of the user when the user wakes up. A short distance link can then be activated, for example Bluetooth, and can send a signal, for example of the iBeacon type for iOS. If this signal is not sent, the classic alarm clock activates at the appointed time.

To this end, the first object of the invention is a method of helping to optimize sleep for an individual in which:

during at least one sleep period of an individual, there is continuously acquired a measurement signal representative of a physiological signal of an individual, from said measurement signal, at least one cycle or a personal structure characteristic of sleep is determined of the individual,

from of said cycle or of the structure characteristic we determines at less a optimal time among one hour optimal awakening of The individual in

according to a predefined bedtime, and an optimal bedtime of the individual according to a predefined wake-up time, said optimal time is provided to the individual.

In preferred embodiments of the invention, it is possible optionally to have recourse to one and / or the other of the following arrangements:

the cycle or the characteristic sleep structure of the individual is determined by calculating at least one of an average duration of the cycle or of the sleep structure of the individual, an average number of cycles or sleep structures, an average time to fall asleep, an average hour of bedtime, an average hour of waking up;

- an optimal waking time of the individual is determined as a function of a predefined sleeping time, or an optimal time of sleeping of the individual as a function of a predefined waking time, so that a difference between said bedtime and said waking time as a function of a number of sleep cycles is such that h _r awakening ^- hcoucher ^- Tendormissement + tT _C y _C i _e where T _cycle is an average duration of a sleep cycle for the user, k is a number of the user's sleep cycles, T _in sleep is an average time for the user to fall asleep, h to _sleep is a bedtime and _{wakeup time} ii is a waking time;

- We determine the cycle or the characteristic structure of sleep of the individual according to a history of encephalographic data of sleep of one individual;

the individual's sleep cycle or structure is determined by determining a diagram of the individual's sleep phases during an average night of the individual, said diagram of sleep phases comprising phases in which the individual is considered able to be awakened with minimal discomfort;

- a plurality of individual sleep phase diagrams is determined for a plurality of individual life rhythms, said rhythms being in particular functions of a day in a week, a week in a month, a month in the year and / or a calendar listing activities planned or carried out by the individual;

a sleep debt is determined as a function of a personal sleep history of the individual determined from a measurement signal measured at most a few days before said determination of sleep debt, in particular at most seven days before said determination , and determining said optimum time as a function of said sleep debt;

- an adjustment of at least one is determined among an average duration of the cycle or of sleep structure of the individual, an average number of cycle or of sleep structure, an average time of falling asleep, an average hour of bedtime, an average hour of waking up personalized sleep time, said adjustment being determined according to a personal sleep history of the individual determined from a measurement signal measured at most a few days before said determination, in particular at plus seven days before said determination, and said optimal time is determined based on said adjustment;

- an adjustment of at least one is determined from an average duration of the individual's cycle or sleep structure, an average number of sleep cycles or structure, an average time to fall asleep, an average hour of bedtime, an average waking time personalized sleep time, said adjustment being determined as a function of the predefined bedtime, and said optimal time is determined as a function of said adjustment;

- a circadian rhythm of the individual is determined, and said optimal time is determined as a function of said circadian rhythm;

- an automatic learning algorithm is implemented from the measurement signal;

- We continuously acquire a measurement signal of a physiological signal from an individual plurality of sleep periods of the individual. Other characteristics and advantages of the invention will appear during the following description of one of its forms of embodiment, given by way of nonlimiting example, with reference to the accompanying drawings.

In the drawings:

FIG. 1 is a schematic view of a device according to an embodiment of the invention,

FIG. 2 is a schematic view of a system according to an embodiment of the invention comprising a device as illustrated in FIG. 1 and a remote module,

FIG. 3 is a flowchart illustrating an embodiment of a method according to the invention,

In the various figures, the same references designate identical or similar elements.

representative during a

FIG. 1 illustrates a wake-up system 1 suitable for implementing a method according to a mode of the invention. The system 1 includes an acquisition device 2 as well as, if necessary, a remote module 3, for example a smart phone or a server.

The device 2 includes electrodes 4, for example dry electrodes, which measure brain activity in real time, a processing unit 5 on board to process the signal. The device 2 may optionally include one or more loudspeaker (s) 6 which may be a conventional loudspeaker or a bone conduction loudspeaker in order to broadcast sounds, for example to serve as an alarm clock.

The processing unit 5 may include one or more processors as well as memories and analog signal processing modules.

The device 2 may comprise a single chassis 7 on which the electrodes 4, the processing unit 5 and, if applicable, the speaker (s) 6 are mounted. The device 2 may also include a battery. The battery 8 can be mounted on the chassis 6.

The system 1 and / or the device 2 are capable of implementing a method of helping to optimize the sleep of an individual as illustrated in FIG. 3.

Such a method firstly comprises a step a) consisting in continuously acquiring a measurement signal representative of a physiological signal of an individual during at least one period of sleep of the individual.

The measurement signal can be a physiological electrical signal such as, for example, an encephalogram measurement or another measurement of activity of the individual, for example a measurement of heart rate or also a measurement of respiration.

A physiological electrical signal can in particular comprise an electroencephalogram (EEG), an electromyogram (EMG), an electrooculogram (EOG), an electrocardiogram (ECG) or any other biosignal measurable on the individual.

In particular, the measurement signal notably advantageously comprises an electroencephalogram (EEG) of the individual.

The measurement signal is then analyzed to determine at least one cycle or a personal structure characteristic of the individual's sleep.

Thus, for example, an analysis of the measures of the cerebral, ocular and muscular activity makes it possible to evaluate the depth of the sleep of the individual during a period of sleep.

For this, the processing unit 5 of the device and / or the remote module 3 can for example implement one or more algorithms for recognizing predefined shapes on the measurement signal S so as to identify slow oscillations, complexes K , sigma waves, an alpha rhythm, or even alarm clocks in the measurement signal S.

Cycles and / or a personal structure characteristic of the individual's sleep can then be identified.

The cycle or structure can be an instant at the beginning or end of a sleep phase or a duration of sleep phase, an order of repetition of sleep phases or any other particularity of a diagram of sleep phases of the individual.

The characteristic sleep cycle or structure of the individual can also be a diagram of sleep phases comprising phases in which the individual is considered capable of being awakened with minimal discomfort.

The cycle or the characteristic structure of sleep of the individual can thus be for example determined by calculating at least one among an average duration of the cycle or of the sleep structure of the individual, an average number of cycles or structures of sleep, an average time of falling asleep, an average hour of going to bed, an average hour of waking up.

In a first exemplary embodiment of the invention, the duration of a sleep cycle is considered for example to be roughly stable during a sleep period.

We can then determine optimal waking and / or sleeping times based on a multiple of the average cycle time of a user.

So for example we can have:

hréveil ^- hcoucher ^- Tendormissement + k. T _r cycle i.e. hr wake up sleep (k T _C y _C i _e + T _in do2?

With:

T _cyc ie: average duration of a sleep cycle for The user k: number of cycle of sleep of the user falling asleep : time way falling asleep The user ^ bedtime (k) 1 time to sleep, , depending on the number cycle h _wakeup i: hour awakening The hours of sleep are proposed

the h _layer r for different number of possible cycles k.

This model gives a simple vision of the operating principle of predictive alarm clock.

In another embodiment of the invention, the predictor uses a history of sleep measurement data, data on the user's lifestyle and estiméeο the estimated / defined time of awakening in order to predict a hypnogram of the night to come.

The data used are for example

- an estimated or defined wake-up time,

- EEG data to take into account the cycles and structure of the previous nights, and / or

- data on the lifestyle of the individual, for example working hours, a day in a week, a week in a month, a month in the year, a calendar listing activities planned or carried out by the individual, user habits,

- factors influencing sleep.

The factors influencing sleep can for example be chosen from the following list:

Factors influencing sleep Effects on sleep Alcohol intake - Reduces latency falling asleep - Increases the duration and frequency of awakenings in second part of the night - Increases the duration and rate of light slow sleep - Reduces deep slow sleep Caffeine intake in coffee, tea or certain drinks - Delays falling asleep Tobacco intake - Delays falling asleep evening meal rich in fatty meats, calories, vitamin C taking amphetamines, corticosteroids, anti-tuberculosis drugs, some anti-depressants. Practice of an activity physical during the day - increases the duration of deep sleep

Intellectual activity before going to bed - increases deep sleep the nd duration of Duration of nights of sleep A night of sleep previous former more short increase the deep sleep and increases sleep the need in Structure of the nights of A deficit of sleep previous sleep deep aura tendency to get recover c The other ' a night at Any other data likely to modify the need of sleep or structure some sleep

In one embodiment of the invention, a sleep debt is determined as a function of the individual sleep history of the individual determined from the measurement signal and said optimal time is determined as a function of said sleep debt.

The sleep debt can in particular be determined from the signal measured at most a few days before said determination of sleep debt, for example at most seven days before said determination.

It is therefore a recent sleep debt which has an effect on the quality of sleep of the individual.

We can then determine an adjustment, as a function of said sleep debt, of at least one of an average duration of the cycle or of sleep structure of the individual, an average number of cycle or of sleep structure, a average time to fall asleep, an average hour of bedtime, an average hour of waking up personalized sleep time.

In another embodiment, the circadian rhythm of the individual will also be determined from the history of his sleep structure and his cerebral and cardiac activity, his duration of falling asleep, his activity or his feeling during the day. , weather or sunshine, time of year, time zone, individual's travel history and possibly other physiological measures such as saliva samples.

An arbitration is then carried out based on the measurement of the circadian cycle, the sleep history, the measurement of the sleep debt and the waking time desired to determine an optimal bedtime and possibly an ideal activity preceding bedtime.

In certain embodiments of the invention, an automatic learning algorithm can be implemented from the measurement signal, to determine an optimal waking or sleeping time taking into account all of the input data. indicated above.

The various methods and devices described above can be combined with one another if necessary.

Claims (12)

1. Method for optimizing sleep for an individual in which: - during at least one sleep period of an individual, a measurement signal representative of a physiological signal of an individual is continuously acquired, - from said measurement signal, at least one cycle or a personal structure characteristic of the individual's sleep is determined, - from said cycle or from the characteristic structure, at least one optimal hour is determined from among an optimal hour of waking the individual as a function of a predefined bedtime, and an optimal time of sleeping of the individual as a function a preset wake-up time, - the said optimal time is provided to the individual. 2. Method according to claim 1, in which the cycle or the characteristic sleep structure of the individual is determined by calculating at least one of an average duration of the cycle or of the sleep structure of the individual, a number average sleep patterns or patterns, average time to fall asleep, an average hour of bedtime, an average hour of waking up. 3. Method according to any one of claims 1 and 2, wherein it is determined - an optimal waking time for the individual based on a predefined bedtime, or - an optimal bedtime for the individual as a function of a predefined waking time, so that a difference between said bedtime and said waking time as a function of a number of sleep cycles is such that hréveil ^- hcoucher ^- Tendormissement + kT _C y _C i _e where T _cycle is an average duration of a sleep cycle for the user, k is a number of sleep cycle of the user, T _in sleep is an average time the user _falls asleep, _bedtime is bedtime and _wakeup time is a waking time. 4. Method according to any one of claims 1 to 3, in which the cycle or the characteristic structure of sleep of the individual is determined according to a history of encephalographic data of sleep of the individual. 5. Method according to any one of claims 1 to 4, in which the individual sleep cycle or structure of the individual is determined by determining a diagram of the individual's sleep phases during an average night of the individual, said sleep phase diagram comprising phases in which the individual is considered capable of being awakened with minimal discomfort. 6. The method of claim 5, wherein a plurality of individual sleep phase diagrams are determined for a plurality of individual life rhythms, said rhythms being in particular functions of a day in a week, d '' a week in a month, a month in the year and / or a calendar listing activities planned or carried out by the individual. 7. Method according to any one of claims 1 to 6, in which a sleep debt is determined as a function of a personal sleep history of the individual determined from a measurement signal measured at most a few days before said determination of sleep debt, in particular at most seven days before said determination, and in which said optimal time is determined as a function of said sleep debt. 8. Method according to any one of claims 1 to 7, in which an adjustment of at least one is determined from an average duration of cycle or of sleep structure of the individual, an average number of cycle or of sleep structure, an average time to fall asleep, an average hour of bedtime, an average hour of waking up personalized sleep time, said adjustment being determined as a function of an individual's personal sleep history determined from a measurement signal measured at most a few days before said determination, in particular at most seven days before said determination, and in which said optimal time is determined as a function of said adjustment. 9. Method according to any one of claims 1 to 8, in which an adjustment of at least one is determined from an average duration of the individual's cycle or sleep structure, an average number of cycles or sleep structure, average time to fall asleep, average time to sleep, average time to wake up personalized sleep time, said adjustment being determined as a function of the predefined bedtime, and in which said optimal time is determined according to said adjustment. 10. Method according to any one of claims 1 to 9, in which a circadian rhythm of the individual is determined, and in which said optimal time is determined as a function of said circadian rhythm. 5 11. Method according to any one of claims 1 to 10, in which an automatic learning algorithm is implemented from the measurement signal. 10 12. Method according to any one of claims 1 to 11, in which a measurement signal representative of a physiological signal of an individual is continuously acquired during a plurality of sleep periods of the individual. 1/2