WO2018160065A1 - Dispositif de détection de trouble de stress post-traumatique (tspt) chez un sujet - Google Patents

Dispositif de détection de trouble de stress post-traumatique (tspt) chez un sujet Download PDF

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Publication number
WO2018160065A1
WO2018160065A1 PCT/NL2018/050128 NL2018050128W WO2018160065A1 WO 2018160065 A1 WO2018160065 A1 WO 2018160065A1 NL 2018050128 W NL2018050128 W NL 2018050128W WO 2018160065 A1 WO2018160065 A1 WO 2018160065A1
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WIPO (PCT)
Prior art keywords
brain
sleep
frequency
subject
frequency spectrum
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PCT/NL2018/050128
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English (en)
Inventor
Lucia Maddalena TALAMINI
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Universiteit Van Amsterdam
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Publication of WO2018160065A1 publication Critical patent/WO2018160065A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms

Definitions

  • 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
  • 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.”
  • a biomarker is suggested. Such a biomarker, according to the patent application, can be calculated from EEG signals.
  • WO2015039689 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.”
  • EEG electroencephalography
  • SFMOR PETER ET AL "Fluctuations between sleep and wakefulness: wakelike features indicated by increased EEG alpha power during different sleep stages in nightmare disorder.”
  • the invention allows a reliable indication of post-traumatic stress disorder (PTSD) in a subject.
  • PTSD post-traumatic stress disorder
  • 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:
  • PTSD post-traumatic stress disorder
  • NREM non-rapid eye movement
  • the invention provides a method comprising:
  • NREM non-rapid eye movement
  • 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.
  • 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.
  • 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.
  • EEG Electroencephalogram
  • 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.
  • the energy spectral density 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.
  • 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.
  • FFT Fast Fourier transform
  • 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.
  • 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.
  • the device comprises a first receiver for providing said first brain-related signal and a second receiver for providing said second brain-related signal.
  • 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.
  • 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.
  • the brain signals comprise EEG signals from said receivers that are conductively coupled to a subjects scalp.
  • the evaluation of said neuromarker comprises evaluating a ratio between said first frequency spectaun and said second frequency spectaim.
  • the first electromagnetic brain-related signal comprises an EEG signal representative of a EEG signal originating from a right-frontal electrode during sleep.
  • the brain-related signal originates from the F4 position.
  • the second electromagnetic brain-related signal comprises an EEG signal representative of a EEG signal originating from an occipital electrode during sleep.
  • the brain-related signal originates from the 02 position.
  • 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.
  • the a slow oscillation range power is used.
  • a frequency range of 0.5-1.5 Hz is used.
  • 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.
  • 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.
  • the first and second datasets are retrieved functionally simultaneously.
  • 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.
  • PTSD post-traumatic stress disorder
  • 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:
  • NREM non-rapid eye movement
  • REM rapid eye movement
  • 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:
  • 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.
  • the first and second datasets are retrieved during one sleep session of the subject, in particular functionally simultaneously.
  • 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.
  • 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” 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.
  • 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.
  • 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
  • Figure 9 shows, in a bar-chart, power spectral deviation for PTSD patients with respect to control subjects in NREM sleep for various electrodes
  • 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.
  • the evaluated neuromarker or its conclusion is displayed on a display device 3.
  • the EEG is recorded, stored and evaluated at a later stage in time, or even at a remote location.
  • 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.
  • sleep stages identifier 6 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.
  • the EEG signals are digitized and these digital signals are processed into spectra.
  • 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.
  • ratio calculator 9 Next, a ratio is calculated in ratio calculator 9.
  • 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).
  • fast Fourier transform-based spectral analysis for instance, using 4 seconds time windows with 50 % overlap, 0.25 Hz bin size; Hamming window
  • FIGS 3A and 3B an example of a recorded EEG is shown that comprises a sleep sequence.
  • FIG 3 A an EEG of the F4 position during NREM sleep is shown
  • 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.
  • 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.
  • ECG monitoring heart rate plethysmography monitoring blood oxygenation
  • tibial EMG to detect leg movements
  • probes measuring nasal airflow probes measuring nasal airflow
  • piezo respiratory bands for thoracic and abdominal respiratory effort to monitor breathing and sleep apnea. Sample rate for all signals was 512Hz.
  • 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.
  • the Fast Fourier Transformation 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.
  • 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).
  • diagnostic threshold criteria 13 PTSD patients out of 16 met criteria for insomnia, 11 for nightmare disorder and 1 for circadian rhythm sleep disorder.
  • the number of participants crossing a diagnostic threshold ranged between 0 and 3 across all scales.
  • 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.
  • Diagnosis 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.
  • PSSI 'PTSD spectral sleep index'

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Abstract

L'invention concerne un dispositif de détection de trouble de stress post-traumatique (TSPT) chez un sujet, comprenant un ensemble de traitement de données et un produit programme informatique qui, lors de l'exécution sur ledit ensemble de traitement de données : - récupère un premier ensemble de données représentatif d'un signal électromagnétique lié au cerveau provenant d'une région de cerveau avant dudit sujet pendant un sommeil sans phase de mouvements oculaires rapides (NREM) (appelé également sommeil lent); - récupère un second ensemble de données représentatif d'un signal électromagnétique lié au cerveau provenant d'une région du cerveau occipital dudit sujet pendant un sommeil à mouvement oculaire rapide (REM) (appelé également sommeil paradoxal); - calcule un premier spectre de fréquences à partir dudit premier ensemble de données et un second spectre de fréquences à partir dudit second ensemble de données; - évalue un neuromarqueur à partir dudit premier spectre de fréquences et dudit second spectre de fréquences, ledit neuromarqueur fournissant une indication pour une présence dudit TSPT dans ledit sujet.
PCT/NL2018/050128 2017-02-28 2018-02-28 Dispositif de détection de trouble de stress post-traumatique (tspt) chez un sujet WO2018160065A1 (fr)

Applications Claiming Priority (2)

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NL2018442 2017-02-28
NL2018442A NL2018442B1 (en) 2017-02-28 2017-02-28 Device for detecting post-traumatic stress disorder (PTSD) in a subject

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021205648A1 (fr) * 2020-04-10 2021-10-14 国立大学法人東海国立大学機構 Procédé objectif d'évaluation du sommeil pour un patient souffrant d'un trouble mental

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WO2015039689A1 (fr) 2013-09-19 2015-03-26 Umc Utrecht Holding B.V. Procédé et système permettant de déterminer un paramètre indiquant si un patient est délirant

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US20140316230A1 (en) 2013-04-22 2014-10-23 Personal Neuro Devices Inc. Methods and devices for brain activity monitoring supporting mental state development and training
WO2015039689A1 (fr) 2013-09-19 2015-03-26 Umc Utrecht Holding B.V. Procédé et système permettant de déterminer un paramètre indiquant si un patient est délirant

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Title
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, vol. 25, September 2016 (2016-09-01), Enschede, pages 55 - 55, XP002774944, ISBN: 978-94-028-0361-7 *
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, 25 September 2016 (2016-09-25), pages 55 - 55, XP002774944
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021205648A1 (fr) * 2020-04-10 2021-10-14 国立大学法人東海国立大学機構 Procédé objectif d'évaluation du sommeil pour un patient souffrant d'un trouble mental
WO2021206046A1 (fr) * 2020-04-10 2021-10-14 国立大学法人東海国立大学機構 Procédé d'évaluation objective du sommeil d'un patient souffrant d'un trouble mental

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