WO2018160065A1

WO2018160065A1 - Device for detecting post-traumatic stress disorder (ptsd) in a subject

Info

Publication number: WO2018160065A1
Application number: PCT/NL2018/050128
Authority: WO
Inventors: Lucia Maddalena TALAMINI
Original assignee: Universiteit Van Amsterdam
Priority date: 2017-02-28
Filing date: 2018-02-28
Publication date: 2018-09-07
Also published as: NL2018442B1

Abstract

The invention provides a device for detecting post-traumatic stress disorder (PTSD) in a subject, comprising a data processing assembly and a computer program product which, when running on said data processing assembly: - retrieves a first dataset representative of an electromagnetic brain-related signal from a frontal brain region of said subject during a non-rapid eye movement (NREM) stage sleep; - retrieves a second dataset representative of an electromagnetic brain-related signal from an occipital brain region of said subject during a rapid eye movement (REM) stage sleep; - calculates a first frequency spectrum from said first dataset and a second frequency spectrum from said second dataset; - evaluates a neuromarker from said first frequency spectrum and said second frequency spectrum, said neuromarker providing an indication for a presence of said PTSD in said subject.

Description

Device for detecting post-traumatic stress disorder (PTSD) in a subject Field of the invention

The invention relates to a device for evaluating brain signals. The invention further relates to a method for evaluating brain signals, and to a computer program product for evaluating brain signals. Background of the invention

Many publications speculate on a possibility of determining brain conditions using recordings of brain signals, for instance recorded using Electro Encephalograms or EEG's.

US2014316230 according to its abstract discloses "With explosive penetration of portable electronic devices (PEDs) recent focus into consumer EEG devices has been to bring advantages including localized wireless interfacing, portability, and a low-cost high-performance electronics platform to host the processing algorithms to bear. However, most development continues to focus on brain-controlled video games which are nearly identical to those created for earlier, more stationary consumer EEG devices and personal EEG is treated as of a novelty or toy. According to embodiments of the invention the inventors have established new technologies and solutions that address these limitations within the prior art and provide benefits including, but not limited to, global acquisition and storage of acquired EEG data and processed EEG data, development interfaces for expansion and re-analysis of acquired EEG data, integration to other non-EEG derived user data, and long-term user wearability." In the description, a possibility of providing a "biomarker" is suggested. Such a biomarker, according to the patent application, can be calculated from EEG signals.

WO2015039689 according to its abstract discloses "Method for determining a parameter which is indicative for whether a patient is delirious or not, or is at risk of becoming delirious or not, wherein the method comprises the steps of : - providing electroencephalography (EEG) data comprising recording signals from at least two electrodes located on different locations on the patient's scalp during a predetermined time period, for instance at least 10 seconds, wherein at least one of the signals is recorded from the frontal half on the scalp; - processing said EEG data for obtaining a deviation signal from the two recording signals from the electrodes; - analyzing said deviation signal in the frequency spectrum for establishing slowing of said deviation signal and defining the parameter as the degree of slowing of said deviation signal which in combination with the locations of the recordings on the patient's scalp is indicative whether said patient is delirious or not, or is at risk of becoming delirious or not."

WOODWARD S H ET AL: "PTSD-related hyperarousal assessed during sleep.", PHYSIOLOGY & BEHAVIOR, part 70, nr. 1-2, 1 July 2000 (2000-07-01), pp. 197- 203 and

L.M. TALAMINI, M. DE BOER, M.J. NIJDAM, R.A. JONGEDIJK, M. OLF, & W.F. HOFMAN: "Spatially specific changes in EEG spectral power in post traumatic stress disorder during REM and NREM sleep", SLEEP-WAKE RESEARCH IN THE NETHRELANDS, part 25, September 2016 (2016-09), pp 55-55, which abstract was published just before filing of the current application,

Generally relate to PTSD and EEG measurements.

SFMOR PETER ET AL: "Fluctuations between sleep and wakefulness: wakelike features indicated by increased EEG alpha power during different sleep stages in nightmare disorder.", BIOLOGICAL PSYCHOLOGY DEC 2013, part 94, nr. 3, December 2013 (2013-12), pp. 592-600, in its abstract states: "Although a growing body of research indicates that frequent nightmares are related to impaired sleep regulation, the pathophysiology of nightmare disorder is far from being fully understood. We examined the relative spectral power values for NREM and REM sleep separately in 19 individuals with nightmare disorder and 21 healthy controls, based on polysomnographic recordings of the second nights' laboratory sleep. Nightmare subjects compared to controls exhibited increased relative high alpha (10- 14.5 Hz) and fronto-central increases in high delta (3-4 Hz) power during REM sleep, and a trend of increased fronto-central low alpha (7.75-9 Hz) power in NREM sleep. These differences were independent of the confounding effects of waking emotional distress. High REM alpha and low NREM alpha powers were strongly related in nightmare but not in control subjects. The topographical distribution and spectral components of REM alpha activity suggest that nightmare disordered subjects are characterized by wakelike electroencephalographic features during REM sleep." Summary of the invention

It is an aspect of the invention to provide a relatively simple neuromarker that allows a clear and reliable indication of a brain condition. In particular the invention allows a reliable indication of post-traumatic stress disorder (PTSD) in a subject.

The current invention provides a device for detecting post-traumatic stress disorder (PTSD) in a subject, comprising a data processing assembly and a computer program product which, when running on said data processing assembly:

- retrieves a first dataset representative of an electromagnetic brain-related signal from a frontal brain region of said subject during a non-rapid eye movement (NREM) stage sleep;

- retrieves a second dataset representative of an electromagnetic brain-related signal from an occipital brain region of said subject during a rapid eye movement (REM) stage sleep;

- calculates a first frequency spectrum from said first dataset and a second frequency spectrum from said second dataset;

- evaluates a neuromarker from said first frequency spectrum and said second frequency spectrum, said neuromarker providing an indication for a presence of said PTSD in said subject.

Furthermore, the invention provides a method comprising:

- retrieving a first dataset representative of an electromagnetic brain-related signal from a frontal brain region of said subject during a non-rapid eye movement (NREM) stage sleep;

- retrieving a second dataset representative of an electromagnetic brain-related signal from an occipital brain region of said subject during a rapid eye movement

(REM) stage of said sleep;

- calculating a first frequency spectrum from said first dataset and a second frequency spectrum from said second dataset;

- evaluating a neuromarker from said first frequency spectrum and said second frequency spectrum.

The current device was surprisingly found to provide a very strong en selective indicator for post-traumatic stress disorder.

The electromagnetic brain-related signal provides a time series of brain activity. In an embodiment, frequency ranges are integrated over a predefined frequency range. The integrated frequency range can be normalized against a predefined frequency range. This can be found to define a relative power spectrum.

In general, the power spectrum of a time series x(t), for instance brain-related signals like an Electroencephalogram (EEG) describes the distribution of power into frequency components composing that signal. According to Fourier analysis any physical signal can be decomposed into a number of discrete frequencies, or a spectrum of frequencies over a continuous range. The statistical average of a certain signal or sort of signal (including noise) as analysed in terms of its frequency content, is called its spectaun.

When the energy of the signal is concentrated around a finite time interval, especially if its total energy is finite, one may compute the energy spectral density. More commonly used is the power spectral density (or simply power spectrum), which applies to signals existing over all time, or over a time period large enough (especially in relation to the duration of a measurement) that it could as well have been over an infinite time interval. The power spectral density (PSD) then refers to the spectral energy distribution that would be found per unit time, since the total energy of such a signal over all time would generally be infinite. Summation or integration of the spectral components yields the total power (for a physical process) or variance (in a statistical process).

The subject in most cases is a mammal, in particular a human. When communication with the subject is difficult or even impossible, the device or method allow obtaining an evaluation of the mental or psychological state of the subject.

The frequency spectrum can be calculated using known methods. In particular for digital time series, often Fast Fourier transform (FFT) is used.

In an embodiment, the computer program product evaluates said neuromarker from said first frequency spectrum including a frequency range of slow oscillations, and said second frequency spectrum including a frequency range of slow oscillation, said neuromarker providing an indication for a presence of said PTSD in said subject.

In an embodiment, the computer program product normalises said first frequency spectrum for evaluation of said neuromarker. In particular the computer program product normalises said frequency range of slow oscillations of said first frequency spectrum against said full spectrum range. In an embodiment, the computer program product normalises said second frequency spectrum for evaluation of said neuromarker. In particular the computer program product normalises said frequency range of slow oscillations of said second frequency spectrum against said full spectrum range.

In an embodiment, the device comprises a first receiver for providing said first brain-related signal and a second receiver for providing said second brain-related signal.

In an embodiment, the device further comprises a headset, placeable on said subjects head, comprising said first and second receiver and for holding said first and second receiver at said respective frontal and occipital brain regions.

In an embodiment, said first and second receiver comprise a first and second electrode for conductively coupling to a subjects scalp respectively at functionally said frontal brain region and said occipital brain region for retrieving the brain signals.

In an embodiment, the brain signals comprise EEG signals from said receivers that are conductively coupled to a subjects scalp.

In an embodiment, the evaluation of said neuromarker comprises evaluating a ratio between said first frequency spectaun and said second frequency spectaim.

In an embodiment, the first electromagnetic brain-related signal comprises an EEG signal representative of a EEG signal originating from a right-frontal electrode during sleep. In particular, the brain-related signal originates from the F4 position.

In an embodiment, the second electromagnetic brain-related signal comprises an EEG signal representative of a EEG signal originating from an occipital electrode during sleep. In particular, the brain-related signal originates from the 02 position.

In an embodiment, the computer program product calculates at least one selected from said normalised first power spectrum, said second power spectrum and a combination thereof using an EEG signal representative of a EEG signal of a cortical origin. In an embodiment, the a slow oscillation range power is used. In an embodiment, a frequency range of 0.5-1.5 Hz is used.

In an embodiment, the computer program product normalises said power spectra or frequency ranges against a substantial past of said recorded frequency range. In an embodiment, the computer program product normalises against a 0.5-50 Hz range.

In an embodiment, the computer program product applies fast Fourier transformation for calculating said frequency spectra. In an embodiment, the computer program product retrieves said first and second datasets from one sleep session of said subject.

In an embodiment, the first and second datasets are retrieved functionally simultaneously.

In an embodiment of said method, said method is for detecting post-traumatic stress disorder (PTSD) in a subject, further comprising said neuromarker providing an indication for a presence of said PTSD in said subject.

The invention further pertains to a device for a mental or psychological status in a subject, comprising a data processing assembly and a computer program product which, when running on said data processing assembly:

- retrieves a first dataset representative of a brain-related signal of said subject during a non-rapid eye movement (NREM) stage sleep;

- retrieves a second dataset representative of a brain-related signal of said subject during a rapid eye movement (REM) stage sleep;

- evaluates a neuromarker from said first frequency spectrum and said second frequency spectrum, said neuromarker providing an indication for said status in said subject.

- retrieves a first dataset representative of a brain-related signal from a frontal brain region of said subject;

- retrieves a second dataset representative of a brain-related signal from an occipital brain region of said subject;

These embodiments may be combined. A brain-related signal in this respect is a signal that is representative for brain activity. Often, this relates to electromagnetic activity. This can for instance be determined in an EEG.

In an embodiment, the first and second datasets are retrieved during one sleep session of the subject, in particular functionally simultaneously.

The term "substantially" herein, such as in "substantially consists", will be understood by the person skilled in the art. The term "substantially" may also include embodiments with "entirely", "completely", "all", etc. Hence, in embodiments the adjective substantially may also be removed. Where applicable, the term "substantially" may also relate to 90% or higher, such as 95% or higher, especially 99% or higher, even more especially 99.5% or higher, including 100%. The term "comprise" includes also embodiments wherein the term "comprises" means "consists of.

The term "functionally" will be understood by, and be clear to, a person skilled in the art. The term "substantially" as well as "functionally" may also include embodiments with "entirely", "completely", "all", etc. Hence, in embodiments the adjective functionally may also be removed. When used, for instance in "functionally parallel", a skilled person will understand that the adjective "functionally" includes the term substantially as explained above. Functionally in particular is to be understood to include a configuration of features that allows these features to function as if the adjective "functionally" was not present. The term "functionally" is intended to cover variations in the feature to which it refers, and which variations are such that in the functional use of the feature, possibly in combination with other features it relates to in the invention, that combination of features is able to operate or function. For instance, if an antenna is functionally coupled or functionally connected to a communication device, received electromagnetic signals that are receives by the antenna can be used by the communication device. The word "functionally" as for instance used in "functionally parallel" is used to cover exactly parallel, but also the embodiments that are covered by the word "substantially" explained above. For instance, "functionally parallel" relates to embodiments that in operation function as if the parts are for instance parallel. This covers embodiments for which it is clear to a skilled person that it operates within its intended field of use as if it were parallel. Furthermore, the terms first, second, third and the like when used in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the tenns so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other sequences than described or illustrated herein.

The devices or apparatus herein are amongst others described during operation. As will be clear to the person skilled in the art, the invention is not limited to methods of operation or devices in operation.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. Use of the verb "to comprise" and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. The article "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the device or apparatus claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

The invention further applies to an apparatus or device comprising one or more of the characterising features described in the description and/or shown in the attached drawings. The invention further pertains to a method or process comprising one or more of the characterising features described in the description and/or shown in the attached drawings.

The various aspects discussed in this patent can be combined in order to provide additional advantages. Furthermore, some of the features can form the basis for one or more divisional applications. Brief description of the drawings

Embodiments of the invention and experimental results will now be described, by way of example only, with reference to the accompanying schematic drawings in which corresponding reference symbols indicate corresponding parts, and in which:

Figure 1 schematically depicts an embodiment of a test setup;

Figure 2 shows a flowchart of the processing procedure for obtaining a neuromarker;

Figures 3A and 3B provides example EEG recordings during sleep;

Figure 4 shows, in a table, sociodemographic details of participants in the PTSD group and in the Control Group;

Figure 5 shows, in a table, sleep macrostructure in PTSD patients and trauma- controls (mean, SD);

Figure 6 shows a table with mean relative power in PTSD patients and trauma- controls, parsed out by sleep state, frequency band and electrode;

Figure 7 shows, in a table, results of repeated measures ANOVA's per sleep state and frequency band;

Figure 8 shows, in a bar-chart, power spectral deviation for PTSD patients with respect to control subjects in REM sleep for various electrodes, and

Figure 9 shows, in a bar-chart, power spectral deviation for PTSD patients with respect to control subjects in NREM sleep for various electrodes;

The drawings are not necessarily on scale

Description of preferred embodiments

In this description of preferred embodiments, first an embodiment of an experimental setup will be discussed, and the processing of data. Next, an experiment will be discussed.

Figure 1 schematically depicts an experimental setup showing a (sleeping) subject 1 having an electrode providing a brain signal from the occipital region 4 and an electrode providing a brain signal from the frontal region 5. The electrode signals are provided to a data processor 2 for processing the electrode signals and evaluating the neuromarker. Next, the evaluated neuromarker or its conclusion is displayed on a display device 3. Alternatively, the EEG is recorded, stored and evaluated at a later stage in time, or even at a remote location. In figure 2, a flowchart is provided that schematically shows steps including steps performed by a computer program.

The brain signals, here the EEG signals from electrodes that are conductively coupled to a subjects scalp, are received by an EEG signal processing device 2.

For each of the EEG sequences, various sleep stages are identified using sleep stages identifier 6.

The selected signals are subsequently converted into a frequency spectaim using a frequency spectaim converter 7 for each of the brain signals 4,5. The frequency spectrum can be calculated using an FFT procedure. Alternatively, wavelet analyses may be used, or another similar technology known to a skilled person. Usually, the EEG signals are digitized and these digital signals are processed into spectra.

Next, the selected frequency ranges are selected in a frequency processor 8, and the selected frequency ranges may be normalized, indicated by the coupling between the sleep stage identifier 6 and the frequency filter 8.

Next, a ratio is calculated in ratio calculator 9.

In an embodiment, the frequency content of the EEG was analysed using fast Fourier transform-based spectral analysis (for instance, using 4 seconds time windows with 50 % overlap, 0.25 Hz bin size; Hamming window), on each electrode (F3, F4, C4 & 02) for NREM sleep and REM sleep separately, for each of the following bands: slow oscillations (0.5-1.5 Hz), delta (1.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), sigma (11-16 Hz), beta (12-30 Hz) and gamma (30-50 Hz).

In figures 3A and 3B, an example of a recorded EEG is shown that comprises a sleep sequence. In figure 3 A, an EEG of the F4 position during NREM sleep is shown, and in figure 3B, an EEG of the 02 position during REM sleep is shown. These EEG traces show 30 second-epochs in each case referenced against the average of the left and right mastoid electrodes.

Polysomnography and general procedure

All data acquisition, including sleep recording, was performed at the clinical facility of Centrum '45. Subjects slept at the clinic twice in the context of a broad diagnostic assessment, before the onset of treatment. The data for the current study was recorded on one of the two nights, with the order of the nights counterbalanced over subjects and disease status (PTSD, trauma-control). Subjective sleep quality did not differ significantly between the two nights, either in participants with PTSD or in trauma-controls (p's>0.1). All reported clinical and sleep diagnostics were obtained within six to eight weeks of the sleep recording. In figure 4, a table is shown with sociodemographic details of participants in the PTSD group and in the Control Group. Note, in this table CAPS=clinical-administered PTSD scale, IES-R=Impact of Event Scale - Revisited, HADS= Hospital Anxiety and Depression scale.

Polysomnographic data was recorded for 13 participants with and 14 without PTSD. Subjects were given the opportunity to sleep undisturbed for 9 hours during a lights-off period starting between 11 and 12 PM, depending on habitual sleep times. Polysomnography, using ambulatory 16-channel Porti amplifiers (TMS-i) and Galaxy sleep analysis software (PHI-international), consisted of an EEG recording (F3, F4, C4 & 02, referenced to average mastoids), two EOG electrodes monitoring eye- movements, and two for submental EMG. Further sensors were ECG monitoring heart rate, plethysmography monitoring blood oxygenation, tibial EMG to detect leg movements, probes measuring nasal airflow, and piezo respiratory bands for thoracic and abdominal respiratory effort to monitor breathing and sleep apnea. Sample rate for all signals was 512Hz.

Data Analysis

Sleep stages were scored visually according to AASM criteria (see Iber et al., 2007). For each recording, we calculated total sleep time, sleep latency, REM latency, time awake after sleep onset, and sleep efficiency. We also determined the amounts of light sleep (N1+N2), SWS (N3) and REM sleep in minutes, and as percentage of total sleep time.

In an embodiment, the Fast Fourier Transformation (FFT) was done with Analysis Manager in Rembrandt (50% overlapping Hamming windows) for the 14 subjects in the PTSD group and for the 13 control subjects. The FFT was computed with a resolution of 0.25 Hz. For all calculations only the frequency bins from 0.5 Hz to 50 Hz were included, leaving the super slow oscillations (0-0.5 Hz) and frequencies beyond 50 Hz out of the analysis. Frequency bands were defined as follows: On each electrode (F3, F4, C4 & 02) for NREM sleep and REM sleep separately, the following bands were defined: Slow oscillations (0.5-1.5 Hz), Delta (1.5-4Hz), Theta (4-8 Hz), Alpha (8-12Hz), Sigma (12-16 Hz), Low Beta (16-20Hz), High Beta (20-30Hz), Beta (16-30Hz) and Gamma (30-50Hz).

Relative power values were calculated for the NREM and REM sleep stages on each electrode separately. To calculate the relative power, the total absolute power per sleep stage was calculated by summing up the absolute power values of all frequencies in the 0.5-50Hz range for each sleep stage. The absolute power per frequency band for both sleep stages was calculated separately by summing up the absolute power values of the frequencies within that specific frequency range. The total power per frequency band for each sleep stage was then divided by the total absolute power of that sleep stage to calculate the relative power per frequency band per sleep stage.

Apneas and hypopneas, oxygen desaturations, periodic leg movements and R- peaks in the ECG were automatically scored (Galaxy, PHI-international) and manually checked. From these measures an apnea index, oxygen saturation index, periodic leg movement index and heart rate were calculated (details in Supplementary materials).

Sleep macrostructure and non-EEG physiological variables were statistically analysed using independent samples t-tests (two-tailed) or Mann-Whitney U tests. Relative spectral power was log-transformed to achieve a Gaussian distribution and analysed through repeated measures ANOVA with factors Diagnosis (PTSD, trauma- control), Sleep State (NREM, REM) and Electrode (F3, F4, C4, 04). The model contained all main and interaction effects of the factor Diagnosis. Frequency bands were analysed separately to avoid sphericity violations. Correlation analyses were performed using Pearson or Spearman correlation coefficients. Finally, effect sizes were calculated as Glass' delta (Larry V. Hedges & Ingram Olkin (1985). Statistical Methods for Meta- Analysis. Orlando: Academic Press. ISBN 0-12-336380-2.).

Subjective sleep quality and sleep disorders

As expected, PTSD patients rated their sleep quality as extremely poor (t=-4.9, p>0.000) and scored very high on insomnia (insomnia: t=9.3, p=0.001) and nightmares (W=142.5, p<0.000) compared to trauma-controls. Considering diagnostic threshold criteria, 13 PTSD patients out of 16 met criteria for insomnia, 11 for nightmare disorder and 1 for circadian rhythm sleep disorder. In the control sample, the number of participants crossing a diagnostic threshold ranged between 0 and 3 across all scales. Finally, PTSD subjects' daily functioning complaints associated to sleep problems were much higher than trauma-controls' (W=105, p<0.000).

Considering non-EEG physiological measures, heart rate and respiratory variables did not differ significantly between participant groups (p's>0.1). However, the amount of leg movements, indexed by the Limb movement index, was significantly increased in PTSD subjects (mean 58,1, SD 42,9) with respect to trauma-controls (mean 33,8, SD 13,6; W=158.0, p=0.039). Also, one PTSD participant was diagnosed with sleep apnea. This, however clearly cannot differentiate PTSD subjects from subjects without PTSD, as increased leg movements also occur with other disorders (e.g. restless legs syndrome and periodic leg movement disorder).

Sleep macrostnicture

Sleep macrostructural variables, presented in the table in figure 5, also differed between groups. Subjects with PTSD displayed a tendency toward longer sleep latency (W=164.5, p=0.077), significantly more awakenings during sleep, both in terms of the absolute number (t=2.4, p=0.025) and frequency of awakenings (awakenings/TST: t=3.0, p=0.005), increased total time awake after sleep onset (t=2.3, p=0.037) and reduced sleep efficiency (t=-2.5, p=0.025). Furthermore, the PTSD group showed marginally significant changes in sleep stage composition compared to trauma- controls: Nl percentage was somewhat increased (t=2.0, p=0.056), while there was a decrease in N3 percentage (t=2.3, p=0.033) and time spent in N3 (t=-2.0, p=0.057). Finally, REM latency in the PTSD group was significantly increased (t=2.2, p=0.043). For other variables, no significant differences were found (p's>0. 1). The effect size for each of these measures is moderate to small, furthermore similar deficits can occur with other disorders. Therefore, these measures cannot accurately differentiate PTSD subjects from subjects without PTSD.

Spectral Analysis

Mean relative power values for each group, per sleep stage, frequency band and electrode, are presented in the table in Figure 6. In figures 8 and 9, the respective power spectral deviation are shown in a bar graph for the various frequency ranges and EEG locations. The various frequency ranges are here binned and plotted, normalized to the total spectral contents, where difference values were calculated by subtracting the average relative power value of the control group from the average relative power value of the PTSD group. Figures 8 and 9 thus show power deviations from control in the PTSD group. As can be seen, there is a selective loss of slow oscillation power in PTSD NREM sleep with a power increase across higher frequency bands (Fig. 9). The pattern is apparent across all derivations, but is most pronounced on frontal electrodes and especially in the right hemisphere. PTSD REM sleep (Fig. 8) shows a more or less opposite pattern of alterations, with increased slow oscillation power and power loss in higher frequency bands. This pattern is most pronounced in the occipital area.

To provide statistical support for the differential spectral abnormalities in PTSD NREM and REM sleep, repeated measures ANOVA, with factors Diagnosis (PTSD, trauma-control), Sleep State (NREM, REM) and Electrode (F3, F4, C4, 04) was performed for each frequency band. The Diagnosis by Sleep State interaction was statistically significant for all frequency bands (all p's<0.01), with statistical results being strongest in the lowest frequency band (SO power: F=15.2, dfl,25, ρ=0.00ί) and somewhat weaker in the highest frequency bands (gamma power: F=8.2, dfl,25, p=0.008). Further analyses were, thus, conducted for the two sleep states separately, through repeated measures ANOVA with factors Diagnosis (PTSD, trauma-control) and Electrode (F3, F4, C4, 04). The results of these analyses are shown in the table in figure 7,Note: ns = non-significant or p>0.05.

For NREM sleep, a significant effect of Diagnosis was found for all frequency bands (p's<0.05), reflecting that in PTSD power is significantly decreased in the SO range and significantly increased in all other bands. The anterior-posterior gradient in the power change was tested through the Diagnosis*Electrode interaction, assessing the contract between anterior F4 and posterior 02. The contrast was statistically significant for the delta, alpha, sigma and beta bands (p<0.05) and reached trend-level significance (p<0.1) for all other bands (SO, theta, beta and gamma), confirming larger changes in anterior than posterior region.

For REM sleep, the main effect of Diagnosis was significant for the SO, delta and theta bands and reached trend-level significance in all remaining bands (alpha, sigma, beta, gamma). Thus, the SO power increases and delta and theta decreases in PTSD REM sleep appear statistically robust, while the decreases in the higher frequency bands are less so. The posterior-anterior gradient in this effect was again assessed through the Diagnosis*Electrode 02 to F4 contrast. The contrast was only significant for the SO band, suggesting that only the SO power increase is significantly localized to posterior brain areas.

Finally, we turned our attention to the spatial locations showing the largest power shifts in each sleep state; that is, right-frontal F4 for NREM sleep and occipital 02 for REM sleep, to assess whether group differences would be statistically robust at the single electrode level. For NREM-F4, the difference between PTSD and trauma- control subjects was highly significant at each frequency band (SO: t=-3.0, p=0.006; delta: t=3.2, p=0.005; theta: t=2.8, p=0.01; alpha: t=3.4, p=0.003; sigma: t=3.3, p=0.004; beta: t=3.3, p=0.003; gamma: t=2.8, p=0.01). Group differences for REM-02 were slightly less robust, but still reached statistical significance for most frequency bands (SO: t=2.8, p=0.014; delta: t=-2.7, p=0.014; theta: t=-2.6, p=0.018; alpha: t=- 2.2, p=0.04, sigma: t=-2.1, p=0.04, beta: t=-1.9, p=0.072; gamma: t=-2.0, p=0.061).

Correlation of power changes in PTSD with experienced sleep problems

To investigate the relation of abnormalities in PTSD-oscillatory sleep dynamics with experienced sleep problems, we considered the largest power changes in the investigated space-frequency domain, that is, SO power, the most strongly affected band across the combined sleep states, on right-frontal F4 for NREM sleep and on occipital 02 for REM sleep. Each variable was correlated with the two most common and characteristic sleep problems in PTSD: insomnia and nightmares.

Reduced right-frontal SO power in NREM sleep was related to increased insomnia (r=-0.46, p=0.017), but was not related at all to nightmare severity (p>0.1). Conversely, occipital SO power in REM sleep showed an extremely large, highly significant positive correlation with nightmare severity (r=0.84, p=0.003), and also a significant correlation with insomnia (r=0.46, p=0.016).

We next calculated a 'PTSD spectral sleep index' (PSSI) that would reflect both the NREM and REM sleep spectral abnomialities. The index was calculated as the ratio between right-frontal NREM SO power and occipital REM sleep SO power. The clinical relevance of this index was assessed through the effect size of disease status (PTSD vs trauma-control). We observed an effect size of striking 3.4, which is considered very large. Importantly, a biomarker should correlate strongly with diagnostic measures obtained with standardized diagnostic instruments. Accordingly, the PSSI shows a large, highly significant correlation with subjects' CAPS scores (r=- 0.60, p=0.001).

It will also be clear that the above description and drawings are included to illustrate some embodiments of the invention, and not to limit the scope of protection. Starting from this disclosure, many more embodiments will be evident to a skilled person. These embodiments are within the scope of protection and the essence of this invention and are obvious combinations of prior art techniques and the disclosure of this patent.

Claims

1. A device for detecting post-traumatic stress disorder (PTSD) in a subject,

comprising a data processing assembly and a computer program product which, when running on said data processing assembly:

- retrieves a second dataset representative of an electromagnetic brain-related signal from an occipital brain region of said subject during a rapid eye movement

(REM) stage sleep;

2. The device of claim 1, wherein said device comprises a first receiver for providing said first brain-related signal and a second receiver for providing said second brain- related signal.

3. The device of claim 2, further comprising a headset, placeable on said subjects head, comprising said first and second receiver and for holding said first and second receives at said respective frontal and occipital brain regions.

4. The device of any one of claims 2-3, wherein the said first and second receiver comprise a respective first and second electrode for conductively coupling to a subjects scalp respectively at functionally said frontal brain region and said occipital brain region for retrieving the brain signals.

5. The device of any one of claims 2-4, wherein said brain signals comprise EEG signals from said receivers that are conductively coupled to a subjects scalp.

6. The device according to any one of the preceding claims, wherein said computer program product evaluates said neuromarker from said first frequency spectrum including a frequency range of slow oscillations, and said second frequency spectnini including a frequency range of slow oscillation, said neuromarker providing an indication for a presence of said PTSD in said subject.

7. The device according to any one of the preceding claims, wherein said computer program product normalises said first frequency spectnini for evaluation of said neuromarker, in particular normalises said frequency range of slow oscillations of said first frequency spectrum against said full spectnini range.

8. The device according to any one of the preceding claims, wherein said computer program product normalises said second frequency spectnini for evaluation of said neuromarker, in particular normalises said frequency range of slow oscillations of said second frequency spectrum against said full spectnini range.

9. The device according to any one of the preceding claims, wherein said evaluation of said neuromarker comprises evaluating a ratio between said first frequency spectrum and said second frequency spectrum.

10. The device according to any one of the preceding claims, wherein said first

electromagnetic brain-related signal comprises an EEG signal representative of an EEG signal originating from a right-frontal electrode during sleep, in particular from the F4 position.

1 1. The device according to any one of the preceding claims, wherein said second electromagnetic brain-related signal comprises an EEG signal representative of an EEG signal originating from an occipital electrode during sleep, in particular from the 02 position.

12. The device according to any one of the preceding claims, wherein said computer program product calculates at least one selected from said normalised first power spectrum, said second power spectnini and a combination thereof using an EEG signal representative of a EEG signal of a cortical origin, in particular of a slow oscillation range power, more in particular 0.5-1.5 Hz.

13. The device of claim 12, wherein said computer program product normalises said power spectra against a substantial part of said recorded frequency range, in particular normalises against a 0.5-50 Hz range.

14. The device according to any one of the preceding claims, wherein said computer program product applies fast Fourier transformation for calculating said frequency spectra.

15. The device according to any one of the preceding claims, wherein said computer program product retrieves said first and second datasets from one sleep session of said subject.

16. A method comprising:

- retrieving a second dataset representative of an electromagnetic brain-related signal from an occipital brain region of said subject during a rapid eye movement (REM) stage sleep;

- evaluating a neuromarker from said first frequency spectrum and said second frequency spectaun.

17. The method of claim 16 for detecting post-traumatic stress disorder (PTSD) in a subject, further comprising said neuromarker providing an indication for a presence of said PTSD in said subject.

18. The method according to claim 16 or 17, wherein said first and second datasets are retrieved from one sleep session of said subject, in particular functionally simultaneously.

19. The method according to claim 18, wherein evaluating a neuromarker from said first frequency spectrum including a frequency range of slow oscillations, and said second frequency spectaim including a frequency range of slow oscillation, said neuromarker providing an indication for a presence of said PTSD in said subject.

-o-o-o-o-o-