Method for determining a scale for the degree of obstructive sleep apnoea and / or the after-effect thereof; such as daytime sleepiness

Abstract

The present invention relates to a method for determining a scale for the degree of obstructive sleep apnoea and / or the after-effect thereof using the following steps: - defining a scale for the degree of obstructive sleep apnoea and / or the after-effect thereof, - providing two EEG measurement signals from an electroencephalograph at the electroencephalograph points of an international 10-20 EEG system, - dividing the EEG measurement signals into frequency bands, - determining at least one cross frequency modulation index using data from at least two different frequency bands, - determining the scale for the degree of obstructive sleep apnoea and / or the after-effect thereof using the at least one cross frequency modulation index. The invention further relates to a device for performing the method.

Description

[0001] Description:

[0002] Method for determining a measure of the degree of obstructive sleep apnea and / or its sequelae, such as daytime sleepiness

[0003] Technical area:

[0004] The present invention relates to a method for determining a measure of the degree of obstructive sleep apnea and / or its consequence, such as daytime sleepiness.

[0005] State of the art:

[0006] Sleep apnea is a nocturnal breathing disorder characterized by repeated, prolonged pauses in breathing or partial apnea during sleep. By definition, prolonged apnea or prolonged partial apnea is defined as apnea or partial apnea lasting longer than 10 seconds. Short pauses in breathing lasting less than 10 seconds are generally not considered harmful. However, if prolonged apnea or partial apnea occur more frequently than five times per hour of sleep, this can have serious health consequences.

[0007] Obstructive sleep apnea (OSA) is widespread in adults. The disorder can cause neuronal damage, or OSA itself can be a neurological disorder. Stimulation of the hypoglossal nerve, which serves as an effective treatment in many OSA patients, supports the argument that OSA is a neuronal / neuromuscular disorder. Accordingly, evidence of a disconnection between motor-associated cortical neuronal groups and peripheral superior cervical motor units has been found in OSA. Furthermore, there is evidence from functional MRI studies for a disconnection between sensorimotor and other cortical regions in OSA.

[0008] Sleepiness, a more common patient-reported effect in OSA, shows significant associations with functional connectivity within the sensorimotor network. A functional neuroimaging study in adults over 55 years of age demonstrated hypoperfusion in sensorimotor areas of patients with severe OSA. Furthermore, neuroimaging studies of interhemispheric interaction have found a strong association between sleepiness and the activity of the bilateral precentral gyrus, an important part of the sensorimotor network, after sleep deprivation.

[0009] Sleep apnea, or rather the severity of sleep apnea, is usually diagnosed in a sleep laboratory using cardiorespiratory polysomnography. The severity is currently divided into three severity groups.

[0010] For one or two consecutive nights, measurement sensors are attached to the patient suffering from obstructive sleep apnea. 18 different physiological signals are recorded over approximately eight hours: electroencephalogram (EEG, 4 signals), electrooculogram (EOG, 2 signals), electromyogram (EMG, 3 signals) on the chin and both lower legs, electrocardiogram (ECG, 1 signal), pulse rate and blood oxygen saturation measurements (pulse oximetry, 2 signals), respiratory flow measurement via the nose and mouth (2 signals), respiratory effort measurement on the abdomen and chest (2 signals), snore microphone on the neck (1 signal), and body position (1 signal).

[0011] During cardiorespiratory polysomnography (CRPSG), the severity of OSA is calculated using the RDI (Respiratory Disturbance Index), i.e., the number of apneas, hypopneas, and so-called RERAs (respiratory effort-related arousals) per hour of sleep. People with an RDI of > 15 / hour of sleep have clinically significant OSA and should be treated promptly according to current guidelines. People with an RDI of 5-15 / hour have mild OSA and do not necessarily require prompt treatment, while people with an RDI of < 5 / hour are healthy (do not suffer from OSA).

[0012] The Epworth Sleepiness Scale (ESS) is used for the subjective assessment of daytime sleepiness in patients with OSA (Johns 1991). It is an 8-item questionnaire for the subjective (patient-perceived) assessment of the tendency toward daytime sleepiness in monotonous everyday situations (no tendency = 0 points; maximum tendency = 3 points; i.e., the cumulative score can vary between 0 and 24 points). With an ESS total score > 10, the probability of significant, clinically relevant daytime sleepiness is greatly increased.

[0013] The measurement is then evaluated automatically or manually using the algorithms embedded in commercially available KRPSG software systems, which use eight of the 18 KRPSG signals mentioned above to calculate the RDI. For quality assurance, it is essential that a visual evaluation be performed by an expert (nurse, medical technician, physician), each with a varying level of training.

[0014] To calculate the ESS, a subjective assessment of daytime sleepiness using the ESS questionnaire is necessary.

[0015] This process is comparatively complex.

[0016] Description of the invention:

[0017] It is an object of the present invention to provide a method for recording and especially for evaluating measurement data that is as automated as possible, which makes it possible to make a statement on the basis of the data in a simple manner about a measure of the severity of the OSA as well as the extent of the associated subjectively assessed daytime sleepiness in the OSA patients, in particular to make a corresponding statement in a simple manner without the involvement of experts.

[0018] The problem is solved by a method for determining a measure of the degree of obstructive sleep apnea and / or its sequelae by evaluating measurement data according to claim 1. First, a measure of the degree of obstructive sleep apnea and / or its sequelae is determined. Two EEG measurement signals from an electroencephalograph are then provided at the electroencephalograph sites of a 10-20 international EEG system. The EEG measurement signals are divided into frequency bands, in particular filtered accordingly. At least one cross-frequency modulation index is determined from the data of at least two different frequency bands. Subsequently, the measure of the degree of obstructive sleep apnea and / or its sequelae is determined using the at least one cross-frequency modulation index.

[0019] This method thus involves the provision and evaluation of only two EEG signals. The claimed method generates data suitable for correlation with a measure of the severity of obstructive sleep apnea and / or its sequelae, allowing a statement to be made about the severity of obstructive sleep apnea and / or its sequelae. The provision and evaluation of the data can be carried out fully automatically.

[0020] One measure of the severity of obstructive sleep apnea is the Respiratory Disturbance Index. One consequence of obstructive sleep apnea is daytime sleepiness. The degree of daytime sleepiness is indicated, for example, using the Epworth Sleepiness Scale (ESS).

[0021] The 10-20 international EEG system is an internationally recognized method for describing the position of scalp electrodes during an EEG measurement, particularly a polysomnography sleep study.

[0022] It is particularly advantageous if the EEG signals are recorded at home while you sleep. However, it is also possible to record the data in a sleep laboratory or doctor's office.

[0023] The method is particularly easy to implement if, according to a preferred embodiment, the measure of the degree of obstructive sleep apnea and / or its consequences, in particular the Respiratory Disturbance Index and / or daytime sleepiness, is determined only on the basis of the two EEG measurement signals.

[0024] It has proven particularly advantageous to use electroencephalography sites C3 and C4 for recording EEG signals. Using the data obtained at these sites, the Respiratory Disturbance Index, or a measure of daytime sleepiness, can be determined with comparatively high accuracy.

[0025] It is advantageous that the measurement signals are divided into frequency bands separately for each measuring point in order to exclude measurement interference at one measuring point.

[0026] The invention is based on the following findings:

[0027] Cross-frequency coupling (CFC) is a fundamental feature of brain oscillatory activity and strongly correlates with brain function. CFC encompasses various patterns: phase synchronization; amplitude co-modulation; and phase-amplitude coupling (PAC). PAC was investigated in the present study because it represents neuronal coding and information transmission within local microscale and macroscale neuronal ensembles of the brain. Low-frequency oscillatory activity represents the regulation of information flow between brain areas by modulating the excitability of local brain ensembles. The phase of this low-frequency oscillatory activity influences both the rhythm of high-frequency activity and the firing rate of individual neurons.Therefore, phase-amplitude cross-frequency coupling (PACFC) appears to promote effective interaction between neurons with similar phase preferences and the synchronization of high-frequency bands during specific slower rhythm phases. Understanding neurophysiological connectivity patterns and oscillatory dynamics at sensorimotor cortical areas may help further elucidate central nervous system (CNS) pathophysiological processes in OSA. Given that OSA is clinically associated with cognitive, alertness, and wakefulness disturbances (particularly excessive daytime sleepiness), a better understanding and modeling of such connectivity may provide the basis for predicting relevant clinical phenomena.

[0028] This study characterized the functional network connectivity of the sensorimotor area in treatment-naive OSA patients. To this end, it was first tested whether sleep-stage-specific theta-gamma PACFC modulation differed between patients with and without significant OSA. This assessment was based on the results of previous studies that demonstrated a significant role of theta oscillation during different sleep stages. Furthermore, it was investigated whether these modulations were specific to a particular sleep stage. To assess whether a possible functional disconnection in the sensorimotor area was frequency-specific, a control experiment was conducted to compare delta-alpha PACFC modulation between patients with and without significant OSA.Finally, we investigated whether such separation was correlated with clinical parameters such as patient-reported sleepiness scores (Epworth Sleepiness Scale; ESS).

[0029] Previous studies have used cross-frequency coupling for sleep stage classification primarily in healthy adults, but also in OSA patients. However, none of these studies evaluated the significance of this classification in terms of clinical transferability to OSA patients. In this invention, the frequency-specific CFC-based modulation indices are not only used for sleep stage classification but further demonstrate that the same modulation indices could also predict clinical scores such as ESS. Therefore, these results should help unravel whether either a frequency-specific, sleep stage-specific, or global functional disconnection exists in the sensorimotor domain of OSA patients. Furthermore, this may provide an objective neurophysiological surrogate marker to quantify patient-reported subjective sleepiness as well as the severity of respiratory illness in OSA patients.

[0030] In a preferred embodiment of the method, the at least one cross-frequency modulation index is determined using phase-amplitude cross-frequency coupling. For this purpose, the measurement signals are preferably divided into at least two of the following frequency bands: low frequency band from 0.1 to 1 Hz, delta band (1 to 3 Hz), theta band (4 to 7 Hz), alpha band (8-13 Hz), beta band (14 to 30 Hz), and gamma band (31-100 Hz).

[0031] In particular, the two measurement signals are divided into the two frequency bands alpha band (8-13 Hz) and delta band (1 to 3 Hz), whereby the amplitude envelope of the measurement signals is determined from the alpha band and the phase from the delta band, preferably by means of a phase-amplitude cross-frequency coupling.

[0032] Additionally or alternatively, the measurement signals are divided into the two frequency bands theta band (4 to 7 Hz) and gamma band (31-100 Hz), whereby the amplitude envelope of the measurement signals is determined from the gamma band and the phase of the measurement signals is determined from the theta band, preferably by means of a phase-amplitude cross-frequency coupling.

[0033] The EEG measurement signals required for the method are preferably stored in a database or storage module so that they are available temporally and spatially independent of the acquisition of the EEG measurement signals. This enables, for example, the creation of a correlation database in which a measure of the degree of obstructive sleep apnea and / or its sequelae is correlated with the cross-frequency modulation index.

[0034] In a preferred embodiment, a correlation database is provided with correlation data between the at least one cross-frequency modulation index and the measure of the degree of obstructive sleep apnea and / or its sequela, wherein the measure of the degree of obstructive sleep apnea and / or its sequela is then determined from the at least one cross-frequency modulation index using the data of the correlation database.

[0035] The provision of a correlation database enables a particularly simple and accurate assignment of the cross-frequency modulation index to a measure of the degree of obstructive sleep apnea and / or its sequelae.

[0036] Preferably, the measure of the degree of obstructive sleep apnea and / or its consequences, in particular the respiratory disturbance index and / or daytime sleepiness, are determined by means of a support vector machine on the basis of the at least one cross-frequency modulation index.

[0037] In summary, phase-amplitude cross-frequency coupling indicates the modulation of the high-frequency power in the C3 / C4 EEG signal by the low-frequency phase in the same (C3 / C4) EEG signal. This cross-frequency coupling (CFC) modulation index (MI) is then used to identify the phase-amplitude mapping between phase-modulating frequency bands (e.g., delta) and amplitude-modulated frequency bands (e.g., gamma). To measure the cross-frequency coupling measure, only the signals recorded from the brain, namely the signals from the C3 and C4 electrodes, are used. After estimating the MI, the support vector machine learning algorithm is used to predict the RDI and the ESS (Epworth Sleepiness Scale).

[0038] In a preferred development of the described method, the measurement signals are recorded during sleep, and sleep is divided into sleep stages. The at least one cross-frequency modulation index is determined as a function of the sleep stages, so that an assignment of the cross-frequency modulation index and the measure of the degree of obstructive sleep apnea and / or its sequelae can be specified as a function of the sleep stages. This allows very precise statements to be made about the severity of sleep apnea and daytime sleepiness, since it has been found that the individual cross-frequency modulation indices differ in the individual sleep stages depending on the severity of the sleep apnea and / or daytime sleepiness.

[0039] It is particularly advantageous that all process steps are carried out automatically, whereby an automatic classification of the sleep stages is advantageously carried out when the measurement signals are recorded during sleep.

[0040] The present invention also relates to a device for carrying out a method for determining a measure of the degree of obstructive sleep apnea and / or its consequences, in particular a device for carrying out the described method, wherein the device comprises a headgear with two sensors for detecting the EEG signals, in particular at the locations C3 and C4.

[0041] Since only two measuring points are necessary for carrying out the method according to the invention, it is sufficient that the headgear comprises only two sensors for determining the EEG signals, in particular at the points C3 and C4.

[0042] Furthermore, a data storage unit and a data processing module can be provided. The data storage unit is provided to store the EEG signals and make them available for further analysis. The data processing module is provided to determine a cross-frequency modulation index from the data according to the present invention. The data processing module is further configured to determine a measure of the degree of obstructive sleep apnea and / or its sequelae from the cross-frequency modulation index.

[0043] In a preferred embodiment, the headgear is a hat, a cap (which may additionally have a chin guard), or a headband. The invention has the advantage that the determination of the RDI and the ESS is carried out using only two EEG electrodes (instead of the standard eight of the aforementioned 18 electrodes) in patients with OSA. The method is thus less complex and more cost-effective than known methods.

[0044] Due to the small number of different measurement signals, a corresponding device for carrying out the method can be designed to be minimally disruptive to the test subject. This is particularly advantageous for measurements taken during sleep, as the measuring device for capturing measurement signals does not interfere with sleep, or interferes with it significantly less than with previously known devices. Furthermore, with the help of the invention, both the RDI and the ESS are determined fully automatically, thus providing an objective method for determining / predicting daytime sleepiness.

[0045] Short description of the drawings:

[0046] Preferred embodiments of the invention are described in more detail with the aid of the accompanying drawings, in which:

[0047] Fig. 1 shows an overview of the data collection and data analysis process.

[0048] Fig. 2 the Ml differences in theta-gamma CFCs

[0049] Fig. 3 the Ml differences in delta-alpha CFCs

[0050] Fig. 4 the SVM classification of different sleep stages

[0051] Fig. 5 the SVM prediction of RDI and ESS

[0052] Fig. 6 the correlation of clinical parameters with CFC measurements

[0053] Fig. 7 the subsequent distribution of the groups studied and

[0054] Fig. 8 a measuring device for recording the EEG signals C3 C4

[0055] Fig. 9 shows a first alternative embodiment of a measuring device for detecting the EEG signals C3 C4 and

[0056] Fig. 10 shows a second alternative embodiment of a measuring device for detecting the EEG signals C3 C4.

[0057] Ways of carrying out the invention and industrial applicability: A total of 170 participants were included in a study conducted using the method according to the invention. The patients were divided into a main and validation dataset, with 86 participants in the main dataset: 22 women, age group: 27-84 years, 44 subjects with moderate or severe OSA, and 84 participants in the validation dataset: 28 women, age group: 35-75 years, 42 subjects with moderate or severe OSA. The data used for the analysis were retrospectively evaluated. Consecutive datasets of patients after applying the inclusion and exclusion criteria listed below were included for analysis. Therefore, a retrospective non-randomized case-control study design was used. All patients had initially visited the outpatient clinic of the Sleep Medicine Center of an academic medical center due to complaints of snoring and / or daytime sleepiness.All participants were initially diagnosed with OSA based on the PSG recordings used in this study. Therefore, all participants were treatment-naive and had no prior positive airway pressure therapy, upper airway surgery, or therapy with any mandibular advancement devices.

[0058] The inclusion and exclusion criteria for participants were based on conditions that could have influenced the development and severity of the observed OSA and / or EEG recordings. Data from adult patients (aged > 18 years) who reported snoring and / or daytime sleepiness to the outpatient clinic, had no prior treatment for sleep-disordered breathing, and had undergone nocturnal polysomnography at our Sleep Medicine Center were included. Participants with neurodegenerative (e.g., Parkinson's disease) or neuroinflammatory (e.g., multiple sclerosis) diseases, a history of stroke, New York Heart Association (NYHA) stage 3 or 4 heart failure, chronic obstructive pulmonary disease (COPD), and any psychiatric disorders were excluded from the study.In addition, individuals with regular use of sedatives, benzodiazepines, serotonin uptake inhibitors, or other psychotropic medications, malignant diseases of any kind, radiation therapy of an anatomical skull or neck region, surgery on intracranial structures, or surgery for the treatment of sleep-disordered breathing (either snoring or OSA) were further excluded from the study. Patient-reported outcomes for excessive daytime sleepiness (EDS) were collected using the Epworth Sleepiness Scale (ESS). Whole-night polysomnography (PSG) recordings from 170 thoroughly screened individuals who met study entry criteria were used for the analysis. All participants underwent overnight polysomnography (PSG) with recordings of the electroencephalogram, electrooculogram, submental and pretibial electromyogram, and electrocardiogram.Polysomnography (PSG) was recorded according to current AASM (American Academy of Sleep Medicine, Inc.) standards to determine the type and degree of sleep-disordered breathing. Nasal airflow was visualized by measuring impact pressure with a nasal sensor, which determined pressure fluctuations in the respiratory airflow. Thoracic and abdominal excursions, oxyhemoglobin saturation (pulse oximeter), and body position were recorded simultaneously. Snoring was recorded using a microphone placed in front of the larynx. PSG recordings were performed for all patients using a commercially available PSG measurement system. All EEG recordings were obtained from C3 and C4 electrodes at a sampling rate of 200 Hz.Patients were divided into the main and validation datasets, with 86 participants in the main dataset: 22 women, age range: 27–84 years, 44 subjects with moderate or severe OSA, and 84 in the validation dataset: 28 women, age range: 35–75 years, 42 subjects with moderate or severe OSA. The recording parameters (EEG bandpass filter (0.05–200) Hz, sampling rate, and EEG channels C2–M1 (left mastoid) and C4–M2 (right mastoid)) were identical in both datasets. All polysomnography (PSG) recordings were performed in a standardized setting between 10:00 p.m. and 6:00 a.m. for each patient.

[0059] Sleep stages were assessed visually (manually) according to the guidelines of the American Academy of Sleep Medicine, Inc. Sleep-related respiratory events were assessed visually (manually) according to the updated guidelines of the American Academy of Sleep Medicine. Apnea was determined when the maximum signal excursion decreased by > 90% from the pre-event baseline for > 10 seconds. Similarly, hypopnea was determined when the maximum signal excursion decreased by > 30% from the pre-event baseline for > 10 seconds in association with either > 3% arterial oxygen desaturation or cortical excitation. Further classification into obstructive, central, or mixed respiratory apnea events was based on the simultaneous assessment of nasal airflow and thoracic and abdominal excursion.

[0060] For data preprocessing, raw EEG data were low-pass filtered (fourth-order Butterworth filter; cutoff frequency: 100 Hz) to avoid aliasing, followed by high-pass filtering at 0.5 Hz. For artifact removal, the data were subjected to independent component analysis (FastICA) to remove artificial components related to muscle, eye blink, eye movement, and line noise artifacts. The preprocessed data were then divided into six different frequency bands: very low frequencies (VLF, 0.1–1 Hz), delta (1–3 Hz), theta (4–7 Hz), alpha (8–13 Hz), beta (14–30 Hz), and gamma (31–100 Hz) for both electrodes. An overview of the data acquisition and analysis process is shown in Fig. 1.

[0061] Phase-to-amplitude cross-frequency coupling (PACFC) indicates the modulation of high-frequency power by low-frequency phase. This CFC modulation index (MI) is then used to identify the phase-amplitude mapping between phase-modulating frequency bands (e.g., delta) and amplitude-modulated frequency bands (e.g., alpha). The following steps were performed to calculate the CFC MI. First, the obtained EEG signal was filtered into two frequency bins, namely delta and alpha. After filtering, the Hilbert transform was applied to both the filtered time series to obtain the phase of one time series and the amplitude envelope of the other. This combined time series then contains the information in each phase from delta oscillations to the amplitude of the alpha rhythm.The possible phase range from -180° to +180° was then divided into 20 units (N) of 18° each and the Kullback-Leibler distance (KL) was calculated using the following formula:.

[0062] Here, O is the KL distance of a discrete distribution P from a distribution Q. The KL distance has the property of always being greater than zero, i.e., DKL (P,Q) > 0, unless the P and Q distributions are equal, i.e., DKL (P,Q) = 0 when P = Q, and is similar to the definition of Shannon entropy, which is given as follows:

[0063] In terms of Shannon entropy, the KL distance can therefore be used to determine the deviation between the distribution of the data and the uniform distribution (L ) as follows:

[0064] D KLP, IT) = logiN) — H(P). Finally, the CFC - Ml was calculated for all units. The distribution of the mean amplitude is uniform across all units, indicating no correlation between phase and amplitude. The modulation index (Ml) could therefore be calculated as follows:

[0065] Thus, if the mean amplitude is evenly distributed across the phases, the Ml would be zero and would be maximal when a Dirac delta is obtained in the phase distribution. The coupling for the theta-gamma and delta-alpha frequency bands was estimated by correlation between the amplitude of the higher-frequency signals and the phase of the lower-frequency signals. The CFC was estimated using a window length of 5 seconds with an overlap of 50%.

[0066] To investigate the significance of these CFC modulation indices, a support vector machine (SVM) algorithm was used to classify different sleep stages based on CFC MI values ​​from both frequency bands. SVM is a powerful tool for nonlinear classification between two datasets, which searches for an optimally discriminating threshold between the two datasets by maximizing the margin between the closest points of the classes. Here, the polynomial function kernel was used for this projection due to its good performance, as well as grid search (min = 1; max = 10) to find the few optimal input parameters and gamma (0.25). The selection was verified by a 10-fold cross-validation, using 75% of the data for training and 25% for testing.To validate the effectiveness of these CFC modulation indices for clinical use, SVM analysis was further applied to predict the clinical scores (RDI and ESS) used in the diagnostic criteria for OSA patients. Here, a support vector regressor (SVR) analysis was performed, which represents a machine learning-based multiple regression method that could associate the observed and trained values ​​and represent the prediction accuracy. To obtain the prediction accuracy threshold, an approach based on statistical inference obtained from the Bayesian credible interval was developed. The 75% threshold could distinguish the posterior distribution from the 95% Bayesian credible interval (indicating the inclusion of 95% of the data points).Here, the posterior distribution and credible interval were obtained by considering all modulation indices from all sleep stages of both groups and the highest density interval at 95% (range: 0.32–0.89) of the distribution. Therefore, the prediction accuracy of over (75%) achieved after 10-fold cross-validation was considered a fairly significant result.

[0067] To ensure that the study results were not influenced by variables other than the independent variables, scientific controls were conducted. It was verified whether the estimated PACFC was independent of the arousals and periodic limb movements of these patients. To this end, the arousal index and the periodic leg movement index (PLM) were estimated for each patient, as well as the Pearson correlation between the PACFC in each sleep stage and these two indices. Since previous studies have shown a significant association between heart rate variability and clinical OSA scores, it was verified whether PACFC was influenced by the activity of the autonomic nervous system. To this end, heart rate variability (HRV) was estimated separately for each patient, and the Pearson correlation with the PACFC in each sleep stage was estimated.HRV was calculated using the standard deviation of normal-to-normal intervals; a technique described elsewhere. Furthermore, the power of the sample size used in the study was determined. A post-hoc Bayesian posterior distribution analysis was performed to estimate the Ml index of the N1 sleep phase between the two groups.

[0068] Of the 86 patients analyzed, 42 patients were diagnosed with a respiratory disturbance index (RDI) < 15 per hour (4 with RDI < 5 per hour and 38 with RDI between 5 and 15 per hour), and 44 patients were diagnosed with clinically significant OSA (30 patients with RDI between 15 and 30 per hour and 14 patients with RDI > 30 per hour). These two groups did not differ significantly in terms of age and gender (p > 0.05). The demographic details along with the clinical measures obtained are presented in Table 1. The statistical analysis performed for cross-frequency coupling (CFC) parameters and their linkage with clinical measures obtained from these two groups yielded significant results, as described below. Table 1. Demographic details of all participants included in the study. Here, RDI: respiratory disturbance index; ESS: Epworth sleepiness scale.

[0069] T-test

[0070] Dataset Group N Age (Years) ^ e ' . . ESS bad hour) p-values

[0071] F = 19 Age: 0.746

[0072] FEI < 15 42 55.67 ± 10.22 9.30 ± 3.26 10.90 ± 4.53

[0073] M = 23 Gender: 0.085

[0074] Main group F = 12 RDI: < 0.001

[0075] FEI > 15 44 56.52 ± 13.91 27.96 ± 12.47 11 .59 ± 4.45

[0076] M = 32 ESS: 0.480

[0077] F = 17 Age: 0.119

[0078] FEI < 15 42 52.79 ± 9.71 11.04 ± 2.97 9.86 ± 4.90

[0079] Validation- M = 25 Gender: 0.168 group F = 11 49.48 ± 19.67 10.14 ± 5.47 RDI: < 0.001

[0080] FEI > 15 42 56.0 ± 9.02

[0081] M = 31 ESS: 0.817

[0082] 5 The CFC modulation index (Ml) in the theta-gamma frequency bands was significantly reduced (p < 0.001) in all sleep stages in patients with clinically significant, i.e., moderate or severe OSA (RDI > 15 / h), as shown in Fig. 2A. The theta-gamma modulation index was higher during NREM stages N2 and N3 than in N1 and REM sleep stages for both groups. The difference in Ml values ​​between the

[0083] 10 both groups was highest during N1, was reduced during N2, but increased again during N3 and REM sleep phases.

[0084] A table presenting all these values ​​is provided as Supplementary Table 1.

[0085] Fig. 2 shows the Ml differences in theta-gamma CFCs, with the rain cloud diagram in Fig. 2a clearly showing that patients in the RDI-> 15 / h group in all sleep phases

[0086] 15 (NREM and REM) exhibit a significantly lower theta-gamma CFC modulation index than that of the RDI group < 15 / h, with exactly the same pattern being found in both the initial and validation patient groups (Fig. 2b).

[0087] However, the CFC-MI at the delta-alpha frequency bands was significantly reduced only during REM and N1 (p < 0.001 ), but not in N3 sleep stage in patients with clinically

[0088] 20 significant OSA compared to patients with mild or no OSA (RDI < 15 / h), as shown in Fig. 3A. Furthermore, the CFC Ml in NREM sleep stage N2 was higher in patients with significant OSA (i.e., RDI > 15 / h) than in patients without (RDI < 15 / h). (See Table 1). Fig. 3 shows the Ml differences in delta-alpha CFCs, with the rain cloud plot in Fig. 3a showing that patients in the RDI > 15 / h group have a significantly lower delta-alpha CFC modulation index than in REM and N1 sleep stages and a significantly higher Ml in N2 sleep stage than patients in the RDI < 15 / h group. The delta-alpha CFC modulation index in NREM-3 (N3) sleep stage is almost identical in the two patient groups. It is noteworthy that exactly the same pattern was found in both the initial and validation data sets (Fig. 3b).

[0089] Support vector machine (SVM) analysis showed significant classification of all four sleep stages and the waking stage using CFC modulation indices separately for the theta-gamma and delta-alpha frequency bands. Overall classification accuracy was higher than 80%, reaching up to 94% for the waking stage classification using theta-gamma CFC-MI, as shown in Fig. 4.

[0090] Fig. 4 shows the SVM classification of different sleep stages. The bar chart shows the support vector machine (SVM) classification accuracy of different sleep stages using theta-gamma and delta-alpha cross-frequency coupling modulation (CFC) indices. The group of 10 bars in each set represents the accuracy obtained for 10-fold cross-validation. A dotted line is shown with an accuracy of 75% to emphasize the significant classification level obtained for all CFC metrics used in the study. All accuracy values ​​are presented in Supplementary Table 1.

[0091] Furthermore, using SVM, the RDI and the Epworth Sleepiness Scale (ESS) in different sleep stages could be predicted with significant accuracy (more than 75%) using CFC-MI in both frequency band pairs. Theta-gamma CFC was able to significantly predict the RDI and ESS in NREM sleep stages (N2 and N3). Delta-alpha CFC in REM sleep stage was able to significantly predict RDI, and delta-alpha CFC in waking stages was able to significantly predict ESS. The details of all predictions are shown in Fig. 5.

[0092] Fig. 5 shows the prediction of RDI and ESS. The scatter plot shows the prediction accuracy of the support vector machine (SVM) of the respiratory distress index (RDI) and the Epworth Sleepiness Scale (ESS) using the modulation indices from theta-gamma and delta-alpha cross-frequency coupling (CFC). The group of 10 points in each set represents the accuracy obtained for 10-fold cross-validation. An accuracy of over 75% was obtained for significant; represented by a dotted line in the graph. Among the 84 patients analyzed from this dataset, 42 patients were diagnosed with a respiratory distress index (RDI) <15 per hour and 42 patients were diagnosed with clinically significant OSA (3 patients with RDI between 15 and 30 per hour and 39 patients with RDI >30 per hour). Also in this dataset, the two groups did not differ significantly in terms of age and gender (p > 0.05).The demographic details are presented in Table 1.

[0093] The statistical analysis performed for this dataset yielded quite similar results to the first (main) dataset and therefore confirmed most of the findings. CFC MI at the theta-gamma frequency bands was also reduced in clinically significant OSA patients (RDI > 15 / h), similar to the main findings, which showed higher modulation also during NREM N2 and N3 sleep stages (Fig. 2B). Likewise, CFC MI in delta-alpha frequency bands was also significantly reduced only during REM and N1, but not during N2 and N3 sleep stages in patients with clinically significant OSA, as found in the main dataset (Fig. 3B).

[0094] The SVM analysis for sleep stage classification using CFC modulation indices for theta-gamma and delta-alpha frequency bands showed replicable results using the validation dataset with a classification accuracy of over 80%, as shown in Fig. 4.

[0095] Similarly, the validation dataset was also able to replicate the prediction results for clinical parameters—RDI and Epworth Sleepiness Score (ESS)—with an accuracy of more than 75% using the same CFC modulation indices as in the main dataset, as shown in Fig. 5.

[0096] However, the correlation between the Epworth sleepiness score (ESS) and PACFC was not significant in any sleep stage (Fig. 6).

[0097] No significant correlation (all p > 0.05) was found between arousal and PLM indices and PACFC in any sleep stage (Fig. 6). 2) Furthermore, no significant correlation was found between heart rate variability and PACFC in any sleep stage, showing no influence of the renal autonomic system on PACFC (Fig. 6).

[0098] Fig. 6 shows the correlation of clinical parameters with CFC measurements. Fig. 6a shows the correlation coefficients between arousal and periodic leg movement (PLM) indices and phase-amplitude cross-frequency coupling (PACFC) for each sleep stage separately. For both delta-alpha and theta-gamma, PACFCs are separated by columns. Fig. 6b shows the correlation coefficients between heart rate variability (HRV) and PACFC in the different sleep stages, separated by columns. Fig. 6c shows the correlation coefficients between the Epworth sleepiness score (ESS) and PACFC in different sleep stages. The correlation for the main and validation groups is shown separately in each row. The r values ​​are shown for the correlation. All correlations were non-significant (p>0.05).

[0099] The Bayesian posterior distribution showed that the 95% high density interval (HDI) lies within the obtained effect in the analyzed data (Fig. 7), indicating a sufficient sample size for the primary outcome in this study.

[0100] Fig. 7 shows the posterior distribution of the studied groups. The graph on the right shows the distribution histogram of the effect size, indicating the 95% high-density interval (HDI) found in the analyzed data. This indicates a sufficient sample size of included subjects based on the primary outcome (i.e., phase-amplitude cross-frequency coupling for theta-gamma in N1 sleep stage). The graphs on the left show the probability distribution with superimposed posterior predictive distributions of the raw data for each data sample.

[0101] A significant reduction in the theta-gamma modulation index (Ml) was found in the central sensorimotor cortical regions in patients with moderate or severe OSA compared to patients with mild OSA or healthy individuals. The Ml reduction during sleep was frequency band-specific; it involved theta-gamma connectivity during all sleep stages, whereas delta-alpha connectivity was only present during REM and N1. Therefore, a global reduction in modulation (both theta-gamma and delta-alpha) was observed during REM and N1. Furthermore, the Ml differences between stages were so pronounced that sleep stage classification based on Ml values ​​was achieved in both datasets in both patient groups. Furthermore, theta-gamma Ml very reliably predicted both RDI and ESS during N2 and N3, and delta-alpha Ml very reliably predicted RDI during REM.

[0102] These novel findings, which demonstrate the functional separation between theta and gamma activity in the cortical sensorimotor area during all sleep stages in OSA patients, have pathophysiological and clinical implications that require further discussion.

[0103] Theta-gamma PACFC has been implicated in motor, sensory, and cognitive processes. In individuals with RDI < 15 / h, there is very strong coupling between theta and gamma oscillations during both N2 and N3 stages. In moderate and severe OSA, the modulation index decreases quite markedly during N3, whereas it decreases to a much lesser extent in N2. During brief waking periods between sleep episodes, Ml increased significantly in patients with moderate / severe (i.e., significant) OSA compared to patients with RDI < 15 / h. This may reflect a physiological (motor, respiratory, cognitive) compensatory role for cortical arousals and / or intermittent waking periods to promote a transient increase in central sensorimotor connectivity in patients with significant OSA.Synaptic net potentiation associated with waking states may form the basis for such an increase in connectivity.

[0104] The recorded neural activity of the sensorimotor cortex may either be primarily generated in this anatomical region or be an epiphenomenon of the activity of other subcortical / thalamic or brainstem neural master generators. These generators promote neuromodulation of cranial nerve pathways, resulting in the reduced upper airway muscle tone associated with the upper airway obstructions that occur during sleep-related airway events in OSA.

[0105] In patients with focal epileptic seizures, the intensity of theta-gamma phase amplitude coupling during sleep was highest during N3 and lowest during REM. In moderately and severely affected OSA patients, theta-gamma PACFC was highest during N2 (see Fig. 2). Coupling of fast and slow oscillations was greatly reduced during REM compared to N2 and N3 in all OSA patient groups; this reduction was more pronounced in patients with significant OSA. Strong CFC between high-frequency and slow-wave oscillations during slow-wave sleep was found in the hippocampus of anesthetized primates. EEG measures the summed postsynaptic potentials of synchronously active regions of the cortex and hippocampus spread throughout the brain, skull, and scalp.Although they often coincide, the firing of action potentials is not necessarily related to oscillations of postsynaptic potentials. Therefore, oscillations of postsynaptic potentials do not always lead to the firing of postsynaptic action potentials. It is difficult to distinguish cortical from hippocampal output in humans based solely on surface EEG recordings.

[0106] A significant increase in delta-alpha CFC Ml is observed during N2 in patients with significant OSA compared to patients with RDI < 15 / h. This finding may represent compensatory increased activity of the sensorimotor cortex during the respiratory event-rich N2 stage to exert better motor control of breathing in severely affected (RDI > 15 / h) OSA patients. Delta-band oscillation in spike and local field potential activity in the somatosensory whisker-barrel cortex of conscious mice is phase-locked to respiration. Therefore, respiratory activity directly modulates slow (1-4 Hz) rhythmic neuronal activity in the somatosensory whisker-barrel cortex and indirectly modulates gamma-band power through phase-amplitude coupling mechanisms in mice.Our results provide preliminary evidence for a physiological involvement of delta and gamma band oscillations in the control of breathing in humans, particularly in OSA, and should be further validated.

[0107] Notably, delta-alpha CFC MI remains fairly stable during N3, regardless of OSA severity. Therefore, delta-alpha coupling may be involved in brain connectivity processes that remain stable during N3, or it may also be a surrogate marker for the known respiratory stability during N3, when apneas and hypopneas occur much less frequently.

[0108] Given that both theta-gamma and delta-alpha modulation indices can reliably predict sleep stage classification according to AASM criteria, it can be assumed that very different sleep-stage-specific PACFC patterns exist with the aforementioned frequency bands. These distinct patterns are apparently quite robust, affecting at least the two oscillatory channels mentioned above (gamma-theta, delta-alpha), and may include additional oscillatory channels. Since a large portion of the tested datasets belong to patients with RDI > 15 / h, these sleep-stage-specific coupling patterns appear to remain quite robust regardless of the degree of accompanying sleep-related dyspnea.

[0109] The global (theta-gamma and delta-alpha) connectivity reduction, as represented by the Ml reduction, during REM in the central sensorimotor areas in OSA patients with RDI>15 / h compared to patients with RDI <15 / h may provide a surrogate marker for the reduced central motor performance during REM. This reduced motor performance likely affects many muscle groups and particularly affects the muscles that control upper airway patency, as they strongly correlate with RDI. The Ml difference is particularly pronounced in delta-alpha CFC-MI (Fig. 2). Differential modulation of global and local oscillations during REM sleep has been reported. Therefore, the multifrequency (global) Ml in the sensorimotor areas during REM may serve as a surrogate marker for OSA disease severity. In support of this argument, a further analysis (Fig.4) showed that delta-alpha Ml during REM very reliably predicted the average RDI in both tested datasets. Theta-gamma Ml during N2 and N3 and delta-alpha Ml during brief awakenings from sleep prove to be reliable surrogate markers for patient-reported excessive daytime sleepiness (EDS). These results also point to possible sleep-related oscillation- and stage-specific physiological mechanisms that promote attention and alertness in humans. Subjective ratings of sleepiness have shown pronounced associations with increased functional connectivity in widespread regions within the sensorimotor network.

[0110] The phase-amplitude cross-frequency coupling (PACFC) modulation index was the primary outcome and was tested as a predictor of clinical variables in this study. Regarding its significance, spatial working memory performance, multi-item working memory maintenance, changes in perceptual outcomes, learning, visual attention, and perception are some of the functional features that have been linked to PACFC modulation. Furthermore, it has also been shown to contribute to BOLD connectivity (dependent on blood oxygen levels) and to have links to brain alterations seen in several neurological disorders, such as epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, obsessive-compulsive disorder (OCD), and mild cognitive impairment (MCI).Although there is sufficient evidence that PACFC is potentially a promising approach to decipher brain function and some of its pathologies with a credible physiological mechanism (low-frequency phase reflects local neuronal excitability, whereas high-frequency power enhancements reflect either a general increase in population synaptic activity or the selective activation of a connected neuronal subnetwork), several unanswered questions remain regarding the origin, causality, and mechanism of these oscillations. The choice of the modulation index (Ml) in this study is based on the finding that, among some of the most widely used phase-amplitude coupling measures, Ml has been shown to be the most robust against confounding influences of moderators, including data length, signal-to-noise ratio, and sampling rate, as Nyquist frequencies are approached.

[0111] The above evidence may open new possibilities for intervention through pharmacological or transcranial magnetic stimulation (TMS) of the sensorimotor cortex in OSA patients. TMS during sleep has been applied to the corticomotor-somatotopic representation of the tongue; induced jerks have briefly improved airflow without causing arousals in OSA patients. However, the effect on other motor areas and the neurocognitive impact of TMS have not been comprehensively investigated in OSA. The finding that the above-mentioned CFC modulation indices in both frequency bands could significantly predict the RDI and Epworth Sleepiness Score (ESS) in OSA patients should be further validated in larger studies.

[0112] The finding that 1) theta-gamma CFC-MI significantly predicted RDI and ESS in NREM (N2, N3), 2) delta-alpha CFC-MI significantly predicted RDI in REM, and 3) delta-alpha CFC-MI significantly predicted ESS in waking states suggests that CFC-MI theta-gamma and delta-alpha metrics may represent quite distinct processes in human sleep physiology. Delta-alpha coupling appears to be significant 1) for the control of upper airway motor stability and breathing during REM sleep and 2) for the control of attentional and vigilance processes, as represented by ESS, during (cortical) arousal and brief periods of wakefulness between sleep stages.Theta-gamma phase amplitude coupling appears to be highly significant 1) for the control of upper airway stability and breathing during NREM N2 and N3 sleep and 2) for attention- and vigilance-related processes (represented by ESS) occurring during N2 and N3 sleep. These findings suggest that cortical central sensorimotor regions may indeed be a significant hub in the networks regulating sleep and / or sleep-related respiratory activity. Following validation in larger patient cohorts, the modulation index could eventually be integrated as an additional metric representing both the severity of dyspnea and daytime sleepiness in patients with OSA.

[0113] The replication of the findings in the validation dataset further supports the reproducibility and validity of the results and indicates their clinical significance as diagnostic surrogate markers. Furthermore, the scientific control results clearly showed no influence of arousal, periodic leg movement, or the autonomic nervous system on the PACFC measurement.

[0114] It is proposed that theta-gamma MI at sensorimotor cortical areas during N2 and N3 and delta-alpha CFC MI at sensorimotor cortical areas during REM can be used as a metric of respiratory distress during sleep in humans and thus as a measure of OSA severity. Therefore, further investigations of theta-gamma CFC during N2 and N3 and delta-alpha CFC during REM should be conducted. Calculating these MIs in additional cortical areas may provide additional insights into OSA pathogenesis and diagnosis. Neurophysiological and neuroimaging studies of thalamocortical connectivity based on the present findings may further elucidate mechanisms of excessive daytime sleepiness. Furthermore, it would be interesting to evaluate the effect of established evidence-based therapies for OSA, such as positive airway pressure (PAP) therapy, on PACFC.

[0115] The functional separation of the central cortical sensorimotor area between theta and gamma activity is observed in all sleep stages of OSA. A further significant delta-alpha sensorimotor area separation occurs during REM and N1 stages in OSA. Therefore, sensorimotor separation is widespread, shows frequency band- and sleep stage-specific patterns, and provides further evidence for the presence of central sensorimotor dysfunction in OSA patients. The theta-gamma modulation index during N2 and N3 reliably predicted patient-reported sleepiness. Therefore, modulation indices can be used as surrogate diagnostic predictive markers for dyspnea during sleep and for patient-reported excessive daytime sleepiness.

[0116] In summary, the described method is suitable for determining the severity of obstructive sleep apnea and the accompanying daytime sleepiness, whereby the method comprises the following steps:

[0117] - Recording two body function data, whereby the two body function data are EEG measurement signals of an electroencephalography at the electroencephalography sites C3 and C4,

[0118] - Dividing the measurement signals into frequency bands, whereby the division is carried out separately for each measuring point,

[0119] - Determination of cross-frequency modulation indices,

[0120] - Determination of the Respiratory Disturbance Index and daytime sleepiness using a support vector machine based on the cross-frequency modulation indices,

[0121] The Respiratory Disturbance Index and daytime sleepiness are a measure of the severity of obstructive sleep apnea and its accompanying symptoms.

[0122] Figures 8 to 10 show embodiments of a measuring device for detecting the measurement signals C3 and C4. These measuring devices are part of a device (not shown) for carrying out the described method. The measuring devices in Figures 8 and 9 are a headgear incorporating two sensors 10 for detecting the C3 and C4 measurement signals. The headgear is designed here as a cap 12 (Figure 8) or as a cap 112 with a chin part 114 (Figure 9). Alternatively, interconnected straps 212 can be provided as headgear, which are attached to the head and incorporating two sensors 10 for detecting the C3 and C4 measurement signals (see Figure 10).

Claims

Patent claims:

1. Procedure for determining a measure of the degree of obstructive sleep apnea and / or its consequences by means of the following steps: - Establishing a measure for the degree of obstructive sleep apnea and / or its consequences, - Provision of two EEG measurement signals from an electroencephalography unit at the electroencephalography sites of a 10-20 international EEG system are, - Dividing the EEG measurement signals into frequency bands, - Determining at least one cross-frequency modulation index using data from at least two different frequency bands, - Determining the measure of the degree of obstructive sleep apnea and / or its consequences using at least one cross-frequency modulation index.

2. Method according to claim 1, characterized in that the EEG measurement signals are determined during sleep in the laboratory or at home.

3. Method according to one of the preceding claims, characterized in that the measure for the degree of obstructive sleep apnea and / or its consequence, in particular the Respiratory Disturbance Index and / or daytime sleepiness, is determined only on the basis of the two EEG measurement signals.

4. Method according to one of the preceding claims, characterized in that the electroencephalography sites at which the measurement signals are recorded are sites C3 and C4.

5. Method according to claim, characterized in that the division of the measurement signals into frequency bands is carried out separately for each measurement point.

6. Method according to one of the preceding claims, characterized in that the at least one cross-frequency modulation index is determined by means of a phase-amplitude cross-frequency coupling.

7. Method according to one of the preceding claims, characterized in that the measurement signals are divided into at least two of the following frequency bands: Low frequency band from 0.1 to 1 Hz, delta band (1 to 3 Hz), theta band (4 to 7 Hz), alpha band (8 to 13 Hz), beta band (14 to 30 Hz), and gamma band (31 to 100 Hz). A method according to claim 6 or 7, characterized in that, to determine a phase-amplitude cross-frequency coupling, the measurement signals are divided into the two frequency bands alpha band (8 to 13 Hz) and delta band (1 to 3 Hz), wherein preferably the amplitude envelope is determined from the alpha band and the phase of the measurement signals is determined from the delta band by means of the phase-amplitude cross-frequency coupling.A method according to any one of claims 6 to 8, characterized in that, to determine a phase-amplitude cross-frequency coupling, the measurement signals are divided into the two frequency bands theta band (4 to 7 Hz) and gamma band (31 to 100 Hz), wherein preferably the amplitude envelope is determined from the gamma band and the phase of the measurement signals is determined from the theta band by means of the phase-amplitude cross-frequency coupling. A method according to any one of the preceding claims, characterized in that a correlation database containing correlation data between the at least one cross-frequency modulation index and the measure of the degree of obstructive sleep apnea and / or its consequences is provided, and the measure of the degree of obstructive sleep apnea and / or its consequences is determined from the at least one cross-frequency modulation index using the data from the correlation database.A method according to any of the preceding claims, characterized in that the measure of the degree of obstructive sleep apnea and / or its consequences, in particular the Respiratory Disturbance Index and / or daytime sleepiness, is determined by means of a Support Vector Machine based on at least one cross-frequency modulation index. A method according to any of the preceding claims, characterized in that the measurement signals are acquired during sleep and the sleep is divided into sleep stages, wherein the at least one cross-frequency modulation index is determined as a function of sleep stages. A method according to any of the preceding claims, characterized in that all method steps are performed automatically. Device for carrying out a method for determining a measure of the degree of obstructive sleep apnea and / or its consequences, in particular a device for carrying out a method according to one of the preceding claims, characterized in that the device comprises a head covering with only two sensors for determining the EEG signals, wherein the sensors are preferably arranged such that the measurement signals are detected at locations C3 and C4. Device according to claim 14, wherein the head covering is a headband, a cap, or a hat.

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