fig1 is a diagram showing the different successive sleep stages of a patient during a night &# 39 ; s sleep . this sleep is in the form of a series of cycles during which , from the waking state , the patient enters deeper and deeper cycles of slow wave sleep , from stage i ( drowsiness ) and ii ( light sleep ) to stages iii and iv ( deep sleep ). then , usually after a short return to stage i , the patient enters a stage called rem ( rapid eye movements ) sleep , which is characterized by high electrical activity of the brain . the cycles follow each other until the final morning awakening . the different stages correspond to distinct forms of brain activity characterized by specific eeg tracings , recognizable on a record performed for example during polysomnography . the course of the sleep period of the patient can be interspersed by micro - awakenings . depending on the importance of the micro - awakening , the patient may either return to the same stage , or be down from one stage or directly be boarded to a lighter sleep stage . according to an exemplary embodiment , the kinesthetic stimulation device provides a non - invasive technique for determining , practically in real time , the current sleep stage of an asleep person without using an eeg examination ( without setting up a multitude of electrodes on the patient &# 39 ; s head ) and with minimal or no risk of causing micro - awakenings that would be deleterious . according to various embodiments , the kinesthetic stimulation device uses the current sleep stage information to modulate a kinesthetic stimulation , in particular so as to end episodes of sleep apnea ( or hypopnea ), without causing a patient micro - awakening , which would have negative consequences thereby losing any benefit to reducing apnea . fig2 schematically illustrates the main components of a system ( e . g ., a kinesthetic stimulation system , etc .) used for this purpose . this system includes a holter recorder 10 connected to various sensors or electrodes 12 , 14 , 16 , to measure physiological signals , such as heart rate , respiration , oxygen saturation , pulse wave , phonocardiogram , etc . in the following , we will focus mainly on the heart rate , which is a simple parameter to obtain , but this is not restrictive and the invention may be implemented from other physiological signals collected from the patient . the system further includes a device for kinesthetic stimulation , with a generator housing including control box 18 producing pulses applied to kinesthetic stimulation effector 20 , for example including of a vibrator disposed in a sensitive region of the skin , typically ( in the adults ) in the region of the mastoid bone in the vicinity of the ear . vibrotactile stimulation applied to the skin by the effector 20 is detected by sensory receptors or mechanoreceptors in the body , and this information is then transmitted via the sensory nerves to the autonomous central nervous system . the effector 20 is for example a transducer of the type c10 - 100 of precision microdrives or c2 tactor of engineering acoustics . the type of transducer may be a transducer that weighs a few grams . the transducer may be capable of emitting vibrations through an integrated vibrator excited by pulse trains of varying amplitude and duration , typically at a frequency of 250 hz which is the resonance nominal frequency of this particular effector and which is also the frequency at which the skin mechanoreceptors are the most sensitive . other types of effectors can of course effectively be used . the control box 18 is controlled by a microcontroller and is configured for adjusting the intensity ( that is to say , energy ) of kinesthetic stimulation , by controlled variation of the amplitude and / or the number , the duration and / or the frequency of the pacing pulse trains forming the signal applied to the effector 20 . the system also includes a housing 22 coupled to the holter device 10 and to the control box 18 via a wire connection or wirelessly 24 , 26 in order to receive data from the holter device 10 , process such data and generate control information of kinesthetic stimulation in response thereto to be transmitted to the control box 18 . alternatively , data processing and control of the control box 18 can be operated from within the holter device 10 and transmitted by a link 28 to the housing 18 . fig3 illustrates , in a given patient , the autonomic response to a kinesthetic stimulation , expressed here in terms of changes in heart rate . such a measure is obtainable via the sensors 12 , 14 , 16 . the acquisition of the heart rate is not limiting , as the methods may be implemented with other techniques for quantifying various other autonomic functions . for example , acquiring the blood pressure or heart sounds ( by a phonocardiographic sensor or an endocardial acceleration sensor ). the treatment is based on the kinesthetic stimulation applied in a sensitive area of the skin where the effector 20 may stimulate the autonomic pathways without generating a patient wake . such an area may preferably be in the region of the ear . responses to the stimulation of the autonomic system may thus be measured . thus , in a study related to patients with sas data from two psg recording nights , they were compared . randomly , the patient spent a night without kinesthetic stimulation and the other night with kinesthetic stimulation of variable energy and applied at regular intervals . the analysis of these results showed that during nights with stimulation more autonomous micro - awakenings were observed , showing the effectiveness of the stimulation to activate the autonomic system . furthermore , the duration of sleep and the duration of the various stages were not significantly different , indicating that the stimulation did not result in disintegration of sleep . changes in mean heart rate recorded for different levels of kinesthetic stimulation applied to the patient at any time during sleep ( sleep stages all together ) are shown in fig3 . at t = 0 , a burst of stimulation pulses is applied at a given energy level and then stopped at t = 5s . changes in heart rate induced by the stimulation , representative of the autonomic response of the patient , are illustrated by the characteristics l 1 to l 5 , which correspond to increasing stimulation energies . typically , the response is biphasic , with an increase in heart rate followed by a decrease below the initial baseline , then back to an approximately stable frequency after twenty cycles after the end of the stimulus . this biphasic response is due to a sympathetic initial activation due to stimulation ( increased heart rate ), followed by a parasympathetic compensation response ( deceleration curve ). it can also be observed that the autonomic response , as measured on the characteristics l 1 - l 5 by the respective amplitudes rep 1 - reps corresponding to the maximum excursion of the heart rate after stimulation , increases with the energy of this stimulation . the observation of the post - stimulation frequency change allows evaluating the significance of the autonomic response of the patient . according to an exemplary embodiment , the treatment provided by the system relates to the use of measurable autonomic response for two purposes : determination of the sleep stage at a given time ; and depending on the sleep stage thus determined , in the case of detected sleep apnea , the application of kinesthetic stimulation to stop apnea , but the energy of which is modulated according to the sleep stage in order not to cause micro - awakenings to the patient . fig4 and 5 show , in the form of box - plots , the results of a study on a population of patients on which the importance of the autonomic response ( measured from the change in heart rate ) in response to the application of a stimulation pulse kinesthetic was assessed , in various circumstances . in fig4 , the vertical axis represents the response rate considered significant in the patient population ( e . g . autonomic responses producing a heart rate excursion of at least 7 bpm ) respectively in the following situations : all sleep stages combined , at the waking state , at the stage i sleep state , at the stage ii sleep state , and at the stage iii sleep state . it should be noted that , in the case of a population of patients with respiratory disorders such as sleep apnea , none reached the deepest stage iv . in each of these situations , the central line represents the median of the samples and its position in the box is used to assess the symmetry of the data . the lower and upper lines of the box represent the empirical quartiles of order p = ¼ ( first quartile ) and p = ¾ ( last quartile ). the height of the box is thus the interquartile range . the two lines of the upper and lower limits show the maximum x m and minimum x m values identified for the considered case . fig4 shows that , for different sleep stages , the importance of the autonomic response following a kinesthetic stimulation decreases as the sleep stage becomes deeper ( e . g ., increases , etc .). the response during stage ii is lower than that during stage i , and that in stage iii ( which has been reached by only one patient in the study population ) is lower than that in stage ii . in other words , to trigger the same autonomic response , for example to stop a single apnea episode , the deeper the sleep stage is , the higher the kinesthetic stimulation energy may be . in fig5 , with the same conventions as before , the micro - awakenings rate values ( determined by a polysomnographic analysis , for example ) for the same sleep stages as those in fig5 are illustrated : all cumulated sleep stages , stage i , ii , iii and rem . it is shown that the micro - awakenings rate also depends on the sleep stage . for a given energy , the more the sleep stage corresponds to a deep sleep , the more the micro - awakenings rate decreases . it will be possible to increase the energy of the kinesthetic stimulation when the sleep stage moves to a deeper level , without the risk of increasing the micro - awakenings . in other words , the energy of stimulation may be increased when the stage increases , to ensure the effectiveness of the response ; conversely , when a light stage is detected again , the stimulation energy has to be reduced again to avoid causing a wake of the patient . the determination of the sleep stage of the patient at a given time can be advantageously used for the therapy of sleep apnea , by appropriately modulating kinesthetic stimulation so as not to induce micro - awakenings . this use , however , is not limitative and the detection of sleep stages may be used for other therapeutic purposes or other diagnostic purposes , for example , an analysis by simple methods of the course of a night &# 39 ; s sleep in a patient so as to have a record of the successive stages over time , including whether the patient sleep reaches the deepest , most restorative , stages or if an underlying disorder prevents him from reaching these stages . with reference to fig6 and 7 , an exemplary implementation of the method is described , for automatically determining sleep stages based on the autonomic response to a kinesthetic stimulation . the algorithm begins with a search for the patient to detect his falling asleep thereof , that is to say , the transition from waking to stage i sleep state ( step 30 ). this detection of the sleep / wake state of the patient may be effected for example by a known technique of overlap of information delivered by a physiological sensor ( minute ventilation sensor , mv ) and an activity sensor ( accelerometer sensor , g ), with monitoring of the heart rate . such a technique is described for example in ep 1317943 a1 ( sorin crm s . a . s previously known as ela medical ). after falling asleep is detected , the patient is necessarily in stage i . therefore , an initialization of the autonomic response corresponding to the first stage is carried out ( step 32 ). this initialization of step 32 is described in more detail in fig7 . first , the energy of kinesthetic stimulation is initialized to the minimum level allowed by the kinesthetic effector stimulation ( step 34 ). a first stimulation is applied ( step 36 ) and the autonomic response is measured , typically by measuring the deflection of the heart rate ( hr ) following the application of the pulse ( step 38 ). if the heart rate variation is sufficient ( test 40 ), for example at least 7 bpm , then the applied energy is saved and stored for the night and the measured hr response is recorded as corresponding to the response at the stage i ( step 42 ). heart rate variation is considered sufficient when it results in a variation of the autonomic response to this stage and to the next stage — because the autonomic response decreases when the stage increases . in the case when the observed response is not sufficient , then the energy is increased by one step ( step 44 ) and the method is repeated ( steps 36 and following ) with the new stimulation energy . in another implementation , the step 36 may include a group of stimulation in order to obtain an average autonomic response , or stimulation with two different energies , to verify a variation in frequency between the different energies is obtained . referring back to fig6 , after the initialization ( step 32 ), periodically or when it is optimal to estimate the current sleep stage , kinesthetic stimulation is applied with the energy that has just been determined , and the autonomic response is measured ( step 46 ). if the hr response did not significantly change ( test 48 ), no special action is undertaken and the method is repeated periodically ( typically every five minutes , timing of step 50 ). in the same method as for step 36 , step 46 may also include a group of stimuli , the evaluation of the step 48 then being made on the average of the observed responses . if , however , the level of the autonomic response is significantly varied ( test 48 ) compared to the previously tested and stored level , this indicates that the sleep stage was probably changed and the new stage has to be determined ( step 52 ). the new sleep stage is determined both i ) depending on the hr response , that is to say the variation in heart rate observed in response to the stimulation , and ii ) according to the recent history sleep stages in of the patient . to this end , in a first determination approach , it will be considered that a reduction in the hr response reveals the transition into a deeper sleep , while an increase of this response reveals the transition to a lighter sleep . the significance of this decrease / increase may also be used to assess the sleep stage based on data from statistical studies on a patient population , studies that have evaluated typical decreased / increased amplitudes according to different transitions : stage i / stage ii , stage ii / stage iii , stage iii / stage i , etc . data from a preliminary study thus showed that the hr response decreases by an average of 18 % between stage i and ii , of 20 % between stage i and iii and of 10 % between stage i and rem stage . however , a transition to wakefulness causes an increase of 22 % of the hr response . another approach is a stochastic , markov and semi - markov , approach . the hidden markov models are automata with a finite state number stochastically and non - deterministically describing a system . the basic structure of a markov model includes of a set of states s =( s 1 , s 2 . . . sn ) connected to each other by a probability defined in a transition table . the adjective “ hidden ” here translates the fact that the issuance of observations from a state follows a random relationship and that the underlying method ( sleep stage ) is not directly observable ( it is “ hidden ”). this random characteristic of the measures which , added to the properties of the markov processes , provides the flexibility and power of this approach . in the case of first - order models , the system state at time t depends only on the state of the system at time t − 1 , which defines a pure markov process . hidden semi - markov models are similar to markov models , but the system state at time t depends not only on the state at t − 1 , but also of other parameters such as the length of stay in the current state . in the approach proposed here , each state of the markov or semi - markov model is a sleep stage and the probability of transition from one stage to another depends on both observable phenomenon ( autonomic response ) and on a transition probabilities matrix learned from a database , such as that already established in the studies cited above . this type of model estimates , given the observable phenomenon and the learned transition matrix , the state of a system at any time t . the algorithm of fig8 and 9 shows an example of treatment of respiratory disorders of sleep apnea or hypopnea type per modulated kinesthetic stimulation depending on the current sleep stage of the patient , according to an exemplary embodiment . the algorithm starts with the detection of the falling asleep of the patient ( step 54 ) according to a similar technique to the one described above in step 30 of fig6 for the discrimination between the various sleep stages . after the falling asleep is detected , the patient is now in stage i , and the stimulation energy of the various stages are then initialized ( step 56 ). a first technique may provide fixed values , progressively increasing as the stages become deeper , to the stimulation energy of the different stages . these values can be the same for all patients ( values calculated from averages of clinical observations ), or can be individualized after preliminary assessment during polysomnography . these values can also be initialized from a first efficient energy ( calculated in step 42 in fig7 ) and then changes in relative variation ( percentage ) or absolute variation (“ delta ”) of the first value for the other sleep stages can be calculated , these variations being directly collected from clinical observations . another technique is to apply the minimum stimulation as determined in step 42 of fig7 . when it is detected that the stage is changed , the energy of the stimulation must be modified . if the stage is deeper , energy is increased to find a significant change in heart rate parameter . if the stage is lighter , the energy level starts from the minimum energy and back . specifically , the initialization step is illustrated in fig9 , once the sleep is detected ( step 58 identical to step 54 of fig8 ) a first stimulation is applied with the minimum energy ( step 60 ). if a measurable response is not detected ( test 62 ), then the stimulation energy is increased by one step ( step 64 ) and the stimulation is repeated until a measurable response is found . the energy thus adjusted is stored as the effective energy corresponding to stage i ( step 66 ). the energies corresponding to the other stages are determined from this value , either by adding a fixed value or by increasing it by a predetermined rate . during the initialization phase 56 , we can also determine the maximum stimulation energy value beyond which a wake is to be feared and this for each sleep stage . this maximum value can be derived from energies evaluated for the different stages by adding a predetermined margin , or may have been determined in a prior polysomnography . back to fig8 , once the initialization is completed ( step 56 ), the algorithm enters into a research phase of the emergence of a respiratory disorder ( step 68 ). various methods of detection of the occurrence of an apnea or hypopnea has been described for example in ep 1319421 a1 , ep 1433496 a1 or ep 1584288 a1 , all three in the name of sorin crm s . a . s , previously known as ela medical , each of which is incorporated herein in their entireties . these documents may be referred to for more details on the method to operate the detection and diagnosis of sleep disorders . it is also possible , if the patient is not implanted , to use a nasal cannula ( 78 in fig2 ) with an appropriate sensor for directly detecting the interruption of the normal respiratory flow . as soon as a condition is detected , the algorithm determines the current sleep stage ( step 70 ), this information being obtained in particular by implementation of the algorithm described above with reference to fig6 and 7 . kinesthetic stimulation is then delivered ( step 72 ) with the energy which had been determined for the current sleep stage , during the initialization step 56 . the effectiveness of kinesthetic stimulation is evaluated ( step 74 ), that is to say , the device determines if the therapy that has been applied has been effective or not . this efficiency can be assessed during the event , immediately , that is to say it is observed whether we are in the presence of a characteristic episode of debut of apnea followed by a rapid recovery of breathing revealing the apnea stop consecutive to kinesthetic stimulation . another method to evaluate the effectiveness of the therapy is , alternatively or in addition , to make a count of the number of events indicative of a respiratory disorder in a given period , for example 5 or 10 minutes , and to check if , based on a history , this count indicates a decrease in the severity of symptoms or not . in any event , if the therapy was not effective , the stimulation energy is increased by one step ( step 76 ), and this up to a predetermined maximum corresponding to the limit that could cause a micro - awakening , therefore with the risk that the treatment itself produce deleterious effects .
a device for treating sleep apnea by discriminating between successive sleep stages of a patient includes a generator configured to produce stimulation pulses , a stimulator that receives the stimulation pulses produced by the generator and delivers stimulation to the patient , a sensor configured to measure a biological parameter of the patient , and a controller . the controller is configured to determine a state of the patient based on the biological parameter , perform a sleep analysis based on the state of the patient , activate the generator to trigger production of the stimulation pulses , determine a variation of the biological parameter subsequent to the production of the stimulation pulses , determine a response of the patient to the stimulation pulses according to the variation of the biological parameter , and determine a sleep stage of the patient based on the response .