WO2024061487A1 - Procédé d'analyse et appareil de dynamique intracrânienne - Google Patents

Procédé d'analyse et appareil de dynamique intracrânienne Download PDF

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WO2024061487A1
WO2024061487A1 PCT/EP2023/066161 EP2023066161W WO2024061487A1 WO 2024061487 A1 WO2024061487 A1 WO 2024061487A1 EP 2023066161 W EP2023066161 W EP 2023066161W WO 2024061487 A1 WO2024061487 A1 WO 2024061487A1
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brain
dynamics
pulses
intracranial
band
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Teemu MYLLYLÄ
Hany FERDINANDO
Vesa Korhonen
Vesa KIVINIEMI
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Oulun Yliopisto
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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
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
    • A61B5/14553—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases specially adapted for cerebral tissue
    • 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
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
    • A61B5/4064—Evaluating the brain
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6814—Head
    • 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
    • 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
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/03—Detecting, measuring or recording fluid pressure within the body other than blood pressure, e.g. cerebral pressure; Measuring pressure in body tissues or organs
    • A61B5/031—Intracranial pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/683—Means for maintaining contact with the body
    • A61B5/6831—Straps, bands or harnesses

Definitions

  • the invention relates to an analyzing method for and apparatus of intracranial dynamics.
  • the glymphatic brain clearance system is responsible for convecting both central nervous system metabolites and waste in both brain and spinal cord.
  • the glymphatic system maintains the brain homeostasis especially during night sleep as it balances metabolite concentrations and removes wastes that accumulate over the daily functions. Its proper activity and deviation from a normal function are known to relate to both brain health and disease conditions, respectively. Larger changes in the glymphatic system can lead to major cognitive changes.
  • Reduced glymphatic activity have been associated with neurodegeneration, epilepsy, narcolepsy, invasive brain tumors, ischemic strokes and several other major brain diseases. Furthermore, such as, mood, stress and sleep problems may affect function of the glymphatic system. It means the glymphatic system and brain health are related. However, no wearable methods exist to monitor and analyze the glymphatic activity. Hence, the measurements and analyses call for improvement Brief ⁇ description The present invention seeks to provide an improvement in the measurements.
  • the invention is defined by the independent claims. Embodiments are defined in the dependent claims. If one or more of the embodiments is considered not to fall under the scope of the independent claims, such an embodiment is or such embodiments are still useful for understanding features of the invention.
  • Figure 1 illustrates an example of apparatus for measuring intracranial dynamics relating to a neuronal and hydrodynamic state
  • Figure 2 illustrates an example of an optic measurement arrangement
  • Figure 3 illustrates an example of possible approximated lines for the G-index as a function of time (age)
  • Figure 4 illustrates an example of the G-index as a function of age and/or neurodegeneration based on an analysis derived from the approximated lines of Fig.3
  • Figure 5 illustrates an example of possible approximated lines of the G- index as a function of time (day)
  • Figures 6 and 7 illustrate examples of Gaussian decomposition of brain pulsation of different brains
  • Figure 8 illustrates an example of pulse decomposition analysis
  • Figure 9 illustrates an example of a flow chart of analysis based on a distance between pulses
  • Figures 10A to 10D illustrate examples of variation around a mean
  • Figures 11A and 11B illustrate examples of water-hemo
  • the abbreviation HbR refers to haemoglobin with reduced oxygen, the haemoglobin being measured in blood.
  • the abbreviation HbT refers to a total oxygen of the blood or total haemoglobin A person skilled in the art is familiar with these terms, per ⁇ se.
  • the term “very low frequency (VLF) band” refers to a frequency band about 0.008 Hz to about 0.1 Hz.
  • the term “respiratory band” refers to a frequency band for recoding breathing.
  • the respiratory band is about 0.1 Hz to about 0.6 Hz.
  • the term “cardiac band” refers to a frequency band for recording heart rate.
  • the cardiac band is about 0.6 Hz to about 5 Hz with harmonics of the principal cardiac frequency.
  • the given frequency ranges refer to typical physiological frequency ranges in human.
  • the glymphatic system including brain fluids are responsible for brain cleaning from metabolic waste and it should work properly to maintain the brain health. Its activity and condition are related to the brain health and brain condition and thus also to neurodegenerative diseases (NDDs).
  • NDDs neurodegenerative diseases
  • the NDDs are mainly found in the elderly which implies that the brain function is affected by NDD and vice versa. Larger changes in the glymphatic activity of the brain are responsible for cognitive changes. It means the glymphatic activity of the brain of particularly the elderly with and without NDD should differ from each other. Normal aging without any evidence of mild cognitive impairment (MCI) or dementia changes cognitive capacities as well and it typically slowly starts after age of about 40 as the brain starts to lose volume, weight and tissue elasticity.
  • MCI mild cognitive impairment
  • dementia changes cognitive capacities as well and it typically slowly starts after age of about 40 as the brain starts to lose volume, weight and tissue elasticity.
  • Prefrontal cortex is one of the brain areas affected by aging and is responsible for memory storage. Two main other areas are posterior associational neocortex and medial temporal lobe. It means two different age groups should have different glymphatic activities of the brain which may also appear as different brain health. Sleep disorders causing mood disorders and stress are also associated with the glymphatic system. It means the glymphatic activity reflects, in addition to brain health, also mental health. The glymphatic system activity reflecting the clearance mechanism, which can be monitored using the apparatus described in this document, uses neuro-hydrodynamical or neurofluid (i.e.
  • the glymphatic system clears metabolic waste and balances reduced metabolites of the brain especially during sleep, and an improper glymphatic convection may decrease wellbeing of mammalians such as human beings.
  • glymphatic convection has even been connected to several major brain disorders including Alzheimer's disease, fronto- temporal dementia, vascular dementia, normal pressure hydrocephalus, multiple sclerosis, epilepsy, trauma, stroke, tumors, hydrocephalus, Chiari malformation, syringomyelia, pseudotumor cerebri, cerebral vasospasm, glaucoma, cerebral aneurysms or the like for example.
  • cerebral disorders including Alzheimer's disease, fronto- temporal dementia, vascular dementia, normal pressure hydrocephalus, multiple sclerosis, epilepsy, trauma, stroke, tumors, hydrocephalus, Chiari malformation, syringomyelia, pseudotumor cerebri, cerebral vasospasm, glaucoma, cerebral aneurysms or the like for example.
  • the dynamics of the brain and neurofluids within the cranium can be measured and monitored using the apparatus described in this document, determination and/or diagnosis of any potential disease based on the measurement results expressly
  • the measured dynamics may be based on the heartbeat and/or the respiration and/or low frequency vasomotor waves (Mayer & Traube-Hering waves).
  • An optoelectronic measurement system of this document can measure and monitor volume dynamics of the brain and brain fluids within the cranium.
  • the fluids within the cranium include interstitial fluid, CSF (CerebroSpinal Fluid) and blood.
  • the measurement of the permeable movement of these fluids between blood, perivascular glymphatic CSF and brain tissue interstitial fluid may be performed with the full band direct-current (DC) electroencephalography (EEG) also involving electrophysiological activity from about 0 Hz to about 1000 Hz ”
  • DC direct-current
  • EEG electroencephalography
  • the optoelectronic measurement system and EEG may jointly be used to quantify hydrodynamics
  • the study of glymphatic system and neurofluid dynamics is a fairly new branch within neurophysiology and the recorded when processed as described in this document is used to form a parameter or index value that describes the function of the brain that can be called in more detail glymphatic activity, brain clearance activity, brain fluid dynamics, neurofluid dynamics or neurohydro dynamics of the brain.
  • NIR near-infrared range
  • brain tissue dynamics and/or fluid dynamics can be measured.
  • blood/haemoglobin dynamics may be measured.
  • the brain tissue water /or neurofluid dynamics can be seen as pulsatile volume/or concentration/ or flow changes, which is caused mainly by three leading physiological pulsation sources: cardiorespiratory and vasomotor pulsations.
  • Each of the pulsation source has a different, non-overlapping frequency range that can be used to identify the pulsation source. This further can be used to identify the measured brain fluid compartments and importantly to identify failures of the glymphatic clearance mechanisms.
  • movements or vibration of head can be used as a pulsation source and the movements can be measured using an acceleration sensor, for example.
  • NIR near-infrared
  • DC-EEG ElectroEncephaloGram
  • Interaction between the gathered signals, in particular their specific phase differences, and amplitude changes reflect activation of the glymphatic system and blood-brain barrier (BBB) permeability, and CSF flow dynamics between CSF and brain tissue. Moreover the direction of information flow between the signals can be used to quantify awareness connection to neurofluid changes.
  • An apparatus of this document can be used to measure and analyze the signals from the brain and their interactions. The apparatus of this document may be wearable, and it can be used for long-term monitoring of the glymphatic activity and examining the well-being of the brain.
  • Glymphatic brain fluid convection and clearance precedes several chronic brain diseases such as Alzheimer’s disease, chronic traumatic encephalopathy, and in tumors and focal epilepsies scar formation prevents the normal CSF convection.
  • CSF cerebral ischemic occlusion CSF
  • the glymphatic system is similar in mammals including humans and mice as it has been shown to be similar with magnetic resonance imaging with invasive gadolinium agent (MRI Gd3+) intrathecal injections and contrast media imaging lasting 24-48 h.
  • MRI Gd3+ invasive gadolinium agent
  • What is presented in this document enables easy monitoring of sleep quality and can quantify the glymphatic increase related brain clearance increase in sleep and importantly monitor the subject also in upright position in daily activity. Further, it provides a method to study and diagnose how, such as, physiological exercise and different treatments affect glymphatic system and brain clearance and wellbeing of the mammal such as a human being.
  • the monitoring may be performed during awake in a short measurement time up to 10 minutes, or during overnight sleep, for example, which can easily be done with the wearable support structure 102 having at least sensing device 100. Additionally, the measurement results may be used in conjunction of diagnoses of brain diseases such as Alzheimer’s disease, stroke, Parkinson, epilepsy, tumors, etc. but that is the work of the medical personnel.
  • Fig.1 illustrates an example of a measurement apparatus for measuring the intracranial dynamics i.e dynamics of the neurological central nervous system and/or intracranial neuronal and fluid dynamics, and glymphatic activity, the apparatus comprising at least one sensing device 100.
  • the apparatus for measuring the intracranial dynamics may comprise a support structure 102 to which the least one sensing device 100 is attached.
  • the support structure 102 may be a band round the head or a cap on the head, for example, without limiting to these. Because the support structure 102 with the at least one sensing device 100 is easy to wear on the head, the apparatus is literally wearable.
  • the at least one sensing device 100 transmits in a wired or wireless manner information of the intracranial dynamics to a data processing unit 150 which may be located at a distance from a mammal 10 such as a human being that has the at least one sensing device 100 attached to the head.
  • At least one sensing device 100 is an optic measurement arrangement 120, an example of which is illustrated in Fig. 2.
  • the optic measurement arrangement 120 may comprise at least one optic radiation source 122 and at least one optic radiation detector 124.
  • Each of the at least one optic radiation source 122 directs optic radiation toward the brain through the cranium, and the at least one optic detector 124 receives the optic radiation reflected and/or scattered therefrom.
  • the at least one optic radiation source 122 may be in contact with the skin of the cranium.
  • the at least one optic detector 124 may be in contact with the skin of the cranium. Alternatively, there may be a non-zero distance between the at least one optic radiation source 122 and the skin of the cranium. Correspondingly, there may be a non-zero distance between the at least one optic detector 122 and the skin of the cranium.
  • Oxy-haemoglobin and deoxy -hemologlobin may be detected.
  • An fNIRS (functional Near InfraRed Spectroscopy) signal cardiac activity related pulsation recorded from the brain is similar to the PPG (PhotoPlethysmoGram) signal recorded from the skin or finger, with a steep upslope and slow decaying downslope.
  • PPG PhotoPlethysmoGram
  • the data processing unit 150 can apply one or more analyses to electrical signals that it receives from the optic measurement apparatus 120, the electrical signals carrying information on the dynamics of the neurological central nervous system in forms of pulses of the brain pulsation.
  • the analysis may be based on time domain analysis and/or frequency domain analysis.
  • the time domain-based analyses are pulse shape analysis (or decomposition analysis), water-hemodynamic coupling and water-EEG coupling (interconnections) analysis and sample entropy, for example. In time domain- based analyses distances between hemodynamic, water and EEG pulses (neural activations) may be determined at each cardiac pulse, for example.
  • intracranial water or brain water refers also to cerebrospinal fluid (CSF).
  • CSF cerebrospinal fluid
  • differences between these couplings can be analysed between different measurement locations in the brain, in particular correlations between the hemisheres. For instance, interhemispheric balance analysis within cardiac band, 0.6- 5 Hz, on HbO-H2O and HbR-H2O concentrations measured in resting state from prefontal core area delivers good separation between stroke patients from controls.
  • hemo-water dynamics in very low frequency 0.008-0.1 Hz
  • respiratory 0.1-0.6 Hz
  • cardiovascular 0.6-5 Hz
  • the lags form a series, which are subject to statistical measures as features to differentiate AD patients from control based on a t-test at 0.05 significant difference.
  • SD and IQR of the series in cardiac band provides the best separation delivered by HbR-H2O pair, e.g. characterised by IQR with p-value between 0,0002 and 0.0018.
  • the frequency domain-based analyses are fractional amplitude of physiological fluctuation (fAPF), power spectral density of high and low band, spectral entropy, cardiorespiratory envelope modulation (CREM), Additionally, the following analyses may be done: tissue oxygenation, phase locking value, phase transfer entropy and EEG signal analysis.
  • the phase transfer entropy forms dPTE (normalized values) and PTE values.
  • the following pairs may be evaluated: HbO-HbR, HbO-H2O, HbR-H2O, HbT- H2O, and EEG-H2O.
  • simultaneous multi-directional analyses between the dcEEG, H20, HbO, HbR can provide more accurate information on the causal relations between the water permeability of BBB and neuronal interactions.
  • dPTE normalized values
  • a transfer from both directions gives the same p-value.
  • fNIRS and EEG signal analysis may be done together and a combined result may be formed.
  • PSD power spectral density
  • VLF 0.008-0.1 Hz
  • respiratory 0.1-0.6 Hz
  • cardiac 0.6-5 Hz
  • the fAPF feature for each band is calculated by dividing PSD from corresponding band with that from the whole band.
  • fAPF feature for HbO, HbR, and HbT is calculated by dividing PSD from corresponding band with that from the whole band.
  • spectral entropy (SE) features are also calculated from all concentration, HbO, HbR, and HbT.
  • SE features from all concentrations in VLF band provide good separation between AD patients and controls.
  • SE features from VLF band provide very low p-value, indicating their significant difference between patients and controls.
  • the fNIRS measuring cerebral pulses can be analysed by suitable statistical methods that distinguish subjects from each other when the subjects have different glymphatic activity.
  • each of the fNIRS pulses can also be decomposed into several characteristic pulses, such that the sum of the characteristic pulse reconstructs the original one.
  • a log-normal pulse may be selected.
  • the log-normal pulse consists of three parameters, amplitude, position, and width.
  • a single fNIRS pulse can be decomposed into characteristic log-normal pulses a number of which may be adaptive.
  • the pulse decomposition analysis can be used to classify subjects based on age group, obesity, and hypertension, vessel and brain stiffness, for example.
  • the characteristic pulses may also be referred to as standard pulses.
  • concentration changes of oxy- and deoxy- haemoglobin , HbO and HbR respectively, and water may be calculated using the modified Beer-Lambert law (MBLL), which is a known method, per ⁇ se.
  • MBLL Beer-Lambert law
  • Total haemoglobin (HbT) as a sum of HbO and HbR may also be formed. They represent concentration changes under the fNIRS sensor.
  • the G-index may be formed.
  • Another example of the at least one sensing device 100 is an electrode of an electroencephalographic (EEG) electrode arrangement, which comprises the support 102 and at least two electrodes.
  • EEG electroencephalographic
  • the EEG measurement can be used in addition to the optical measurement.
  • the at least one sensing device 100 is in an electric contact with skin of a cranium of the mammal 10 an example of which is a human being.
  • the sensing devices of the electroencephalographic electrode arrangement receive and sense direct-current (DC) electroencephalographic signals from the brain of the mammal 10.
  • DC direct-current
  • the at least one optic radiation source 122 may be pigtailed such that the optic radiation is guided from the at least one optic source 122 to the skin of the cranium through an optic fiber.
  • the at least one optic detector 124 may be pigtailed such that the optic radiation is guided from the skin of the cranium through an optic fiber to the at least one optic detector 124.
  • the optic fiber end(s) may be in contact with the skin of the cranium or be in a non- zero distance from the skin. The optic fibers are not separately illustrated in Figures.
  • all the sensing devices 100 may be optic detectors.
  • the sensing devices 100 may comprise a combination of at least two of different kinds of sensing devices 100.
  • a combination may include at least one electroencephalographic sensing device 100 and at least one optic sensing device 100.
  • a data processing unit 150 receives the electric signals from the at least one sensing device 100.
  • the electric signal may be filtered, and their baseline may be corrected and/or stabilized.
  • a person skilled in the art is familiar with the existing filtering and baseline correction/stabilization methods, per ⁇ se.
  • the data processing arrangement 150 then processes the electric signals and forms a G-index, which represents the glymphatic activity of the brain referred to earlier in this document.
  • the G-index represents the glymphatic activity of the brain referred to earlier in this document.
  • the G-index is based on one analysis or a combination of various analyses of the brain signal perfomed by the data processing arrangement 150.
  • the G-index can be calculated from the fNIRS measurement alone or as a combination of the EEG measurement and the fNIRS measurement.
  • the calculations may utilize a single- and/or multimodal approach, the multimodal approach including a plurality of analyses of the brain signals of either the fNIRS measurement or a combination of the fNIRS measurement and the EEG measurement.
  • One of the multimodal analyses is coupling between different G-index values of different analysis, because any two analyses relate to different physiological phenomenon in principle.
  • the rate of change and the trend can vary depending on the subject(s) or condition; thus, several models are proposed.
  • the approximated lines can be used to predict G-index during G-index model development process.
  • Parameter k in a linear function (– kx) or an exponent function (e -kx ) modelling the G-index can be adjusted for a suitable match with the measured results, for example, where x is time such as age or related to age.
  • the matching may be performed using a smallest square error, for example.
  • the slope is negative as a function of age, particularly for adults.
  • Both single- and multimodality analysis models can be utilized.
  • the brain signals can be measured from subject of brain conditions and subjects each having a healthy brain.
  • the experiments can utilize brain signals measured from both Alzheimer’s Disease (AD) patients and a control group (healthy persons of the same age range), aiming to choose suitable indices for the G-index.
  • the control group may be used as a reference although any group with or without brain conditions may be selected as a reference.
  • General guidelines are required for deciding which indices are good candidates. The following guidelines may be used to evaluate whether the proposed methods are promising for a further processing: 1. They should be able to separate patients from age-matched controls 2. They should be able to separate healthy controls with same age but different living styles and e.g., when having differences in sleep quality, stress, smoking, eating/diet etc. 3. They should provide good separation among subjects with different ages.
  • indices from different methods may be combined to estimate the G-index. Combining different indices may be done by assigning a certain weight value for the corresponding indices. For example, the G-index may equal to 100 or some other desired value may represent the best brain health or most desired glymphatic activity, while zero or some other desired value may be the worst, see Figs 3 and 4.
  • the G-index is inversely proportional to the age.
  • the data processing unit 150 processes the electrical signals it receives.
  • the electrical signals carry information on the brain and particularly glymphatic activity in their pulsation.
  • Each of the pulses can be processed using decomposition, statistical methods, and a power distribution.
  • the pulses can alternatively or additionally be decomposed into a group of pulses and form the G- index based on one or more properties of the decomposed pulses.
  • the processing in the data processing unit 150 may utilize one or more moments of the statistical analysis of the decomposed pulses or the non- decomposed pulse.
  • the statistical analysis by be based on mean, variance, skewness, and kurtosis.
  • the data processing unit 150 may then form the G-index based on them.
  • the non-decomposed pulse may be so called raw pulse or it may be filtered for reducing noise, interference and/or artefacts.
  • the mathematics of the analysis methods is known, per ⁇ se. However, they are now specially applied to the electric signals received from the optical measurement of the brain. They may also be applied to the EEG measurements.
  • the pulsations of the brain have been modelled for various groups of people that have different brain function i.e. different G-indices.
  • Different groups can have different age range and within age groups people may be divided into groups that have healthy brain and one or more brain conditions such as Alzheimer and/or mental health issues, for example.
  • the conditions may be sub- divided based on severity of the condition and so on.
  • MODELLING Phase ⁇ 1 It is necessary to conduct feature analysis to see which features have good contribution using, e.g., principal component analysis (PCA). It also serves as a dimensionality reduction to speed up calculation. Then, a simple machine learning classifier, such as kNN (k-Nearest Neighbour) or multi-class support vector machine can be used as the learning algorithm, for example.
  • Data collection may be performed as follows: • Data is collected from healthy subjects who are 20 years old or older.
  • Measurement time is 10 minutes per session for the sake of analysis in VLF band (0.008-0.1 Hz) • Subjects are measured for several sessions on different week because it can be hypothesised that G-index should be dynamic but within certain ranges • Subjects are measured on resting state in sitting position • Subjects should restrict themselves from certain medications and food that can alter the measurement Data processing: • All signals may pass standard signal processing procedure, e.g., reducing artefact, selecting the band of interest, etc. • Features are extracted using the promising methods.
  • Evaluation criteria Sets of models, which result in high accuracy or similar high performance with other appropriate performance metrics such as F1- score, sensitivity, specificity or balanced accuracy, are useful for further exploration.
  • the set of features for further analysis may be selected according to the models with high achieved performance with the developed model or by suitable feature importance analysis. During feature extraction, many features, which are more or less useful, are obtained. Of them suitable ones are selected to support the goal properly.
  • Phase ⁇ 2 Based on the G-index model phase 1, a regression model can be developed in phase 2. The aim of this phase is to get real value for each subject, instead of classifying subjects to corresponding age group.
  • the model development starts from the simplest regression, i.e., linear regression.
  • Data collection There is no data collection in this phase because all required signals have been collected during the phase 1.
  • Data processing may go as follows, for example: • Sets of features with selected in the phase 1 are used in this phase • For each group, the following “predicted” G-index values are assigned. An idea may be to have a high G-index for young subjects. Proper values for each group are not known yet, so the “predicted” values are used temporarily.
  • the isolated test dataset consists of subjects whose measurement data is unseen by the trained model • Apply “predicted” G-index also to the other approximation lines, see Fig.3, and other type of regression algorithm. If necessary, the parameters in the approximation line can be changed to get reliable and credible results Evaluation criteria: • The result should be similar to that of Fig.4 • Mean squared error (MSE) or other suitable performance metric may be used for validation with good performance. • The variation among different measurements on the same subject should be within about mean ⁇ 2 times standard deviation, for example. Expected results: Rough idea about the approximation line for a G-index curve and the parameters. Phase ⁇ 3 In phase 3, subjects from children and teenagers may also be involved.
  • MSE Mean squared error
  • the process is the same as the G-index model phase 2, and may be conducted for example as follows Data collection: • Subjects: o Teenager: 13-19 years old o Children: 7-12 years old o Small children: 2-6 years old • Data collection is conducted with the same protocol Data processing: • Combine signals from these subjects with the previous data collection in phase 1. • Use the same procedure in phase 2. • Assign the following “predicted” G-index from linear approximation: o 2-6 years old : 100 o 7-12 years old : 90 o 13-19 years old : 80 • Apply “predicted” G-index using the other approximation lines, see Fig.3.
  • Phase 4 In phase 4, patients with certain NDDs are involved and the processes follow phases 1 and 2. This phase requires more data collection from patients with standardized NDD assessment. At this point, we ignore the severity of NDD and the age. It means as long as patients have a low G-index, it is fine. The G-index from healthy subjects should have similar performance as in phase 3.
  • the phase 4 may be conducted for example as follows. Data collection: • Data is collected from patients with NDDs. • Patients go through examination to determine how severe their health condition.
  • phase 5 may be conducted for example as follows.
  • Data collection There is no data collection in this phase because it is done in the previous phase
  • Data processing • Due to variation of severity level, data from patients is divided into three groups • The “predicted” G-index values are 10, 5, 0 based on the linear approximation line. • Repeat procedure in phase 1, but only for data from the NDD, for each of the NDDs. Evaluation criteria: See phase 1. If the performance is promising, procedure in phase 2 can be executed. Expected results: The approximation line for the G-index curve and the parameters with gradual changes to indicate the severity level of NDDs.
  • the G-index model development involves the following algorithms: • Classification: o kNN using various distance measure, e.g., Euclidean, Mahalanobis, Chebyshev, city block or the like for example o supervised learning algorithm such as the support vector machine (SVM, using various kernel, e.g., linear, polynomial, radial basis function, and sigmoid) o Tree and random forest • Regression: o Regression o Support vector regression o Bayesian network One or more of these models are available for the data processing unit 150 as a reference for formation of the G-index as function of age or some other parameter in order to describe a person’s glymphatic activity of the brain or the brain health with respect to the reference.
  • SVM support vector machine
  • kernel e.g., linear, polynomial, radial basis function, and sigmoid
  • Regression o Regression o Support vector regression o Bayesian network
  • the reference may be a G-index of a normal, healthy brain, for example.
  • the reference may be a G-index of a group of people with a brain condition or a deviance from a normal brain function caused by lifestyle, medication, sickness, obesity, stress, mental health, pregnancy, injury, nutrition, or the like, for example.
  • Processing ⁇ measured ⁇ signals the dynamics of the neurological central nervous system may be measured in a frequency band about 0 Hz to about 0.5 Hz. In an embodiment, the dynamics may be measured in a frequency band about 0 Hz to about 2.5 Hz. In an embodiment, the dynamics may be measured in a frequency band about 0 Hz to about 4 Hz.
  • the dynamics may be measured in a frequency band about 0 Hz to about 5 Hz.
  • Dynamics of glymphatic system refers to the pulsatile activity related to its clearance function.
  • Decomposition Examples of analysis based on decomposition are illustrated in Figs 6, 7 and 9.
  • a waveform of the received pulse may be decomposed based on an integral transform.
  • the integral transform may be the Fourier transform or the Laplace transform, which the person skilled in the art is familiar with.
  • the data processing unit 150 can decompose at least one of the pulses of the of the dynamics of the neurological central nervous system into a plurality of characteristic pulses for processing at least one relation between the characteristic pulses such as a temporal distance between the characteristic pulses.
  • a pulse may be converted into a pulse that looks like a Gaussian pulse.
  • a natural logarithm without limiting to this can be used to perform a log transform.
  • Scaling of the variable may be needed because negative values are not allowed (logarithm does not have a real value for negative numbers). Scaling if needed may be performed in conjunction with filtering, for example.
  • a transform an example of which may be a Gaussian decomposition
  • 3 to 5 characteristic individual log-normal pulses or waves may be extracted, for example.
  • the number of the characteristic pulses may be adjustable for the measurement. There are 5 characteristic log-normal pulses as shown in Figs 6 and 7.
  • the Gaussian decomposition may be expressed mathematically as where N is the number of the decompositions i.e characteristic pulses, n is the index of the summation, A is amplitude, c is a center point and ⁇ is the standard deviation.
  • each optically measured waveform or pulses may be represented by fifteen parameters when using 5 or other adjustable number of characteristic pulses of the decomposition, the values of parameters being estimated by a nonlinear curve fitting with constraints, for example.
  • An alternative to the Gaussian transform, per ⁇ se, may be utilized.
  • a reciprocal transform may also be used to make a pulse look like a Gaussian pulse.
  • An exponent transform may also be used to make a pulse look like a Gaussian pulse.
  • PDA in the fNIRS (functional Near InfraRed Spectral) signal analysis requires high enough sampling frequency to get good time resolution for the center position and pulse width parameters.
  • a sampling frequency should be more than about 100 Hz, for example.
  • a sampling frequency of about 500 Hz or higher may also be used, for example.
  • the PDA may be applied with the following algorithm to get five log-normal pulse with fifteen parameters of the Gaussian transform, which is here as an example 1.
  • the algorithm guesses fifteen random numbers as starting points of the optimization algorithm (different starting point can result in different estimated parameters) 2.
  • a minimization or optimization function with constraints seeks for suitable parameters which follow the constraints 3.
  • the estimated parameters are evaluated based on the error and correlation to the original pulse 4.
  • the Gaussian transform may be used to provide five pulses a combination of which resembles the original pulse measured from the brain.
  • the first and third pulses represent systolic and diastolic pulse pressure, while the second and fourth ones are their reflections due bifurcation.
  • the distance between the first and second pulses can be useful to estimate this brain stiffness.
  • This assessment based on the optical measurement of the brain may be conducted in a segmented signal, e.g., the last about 2 minutes, for example, to get the results periodically.
  • a pulse selection may be applied to remove unqualified pulses. Based on the pulse rate variability (like HRV in ECG), pulses with length outside the boundary of mean ⁇ 3 times standard deviation may be discarded. Additional works such as correlating the pulse with a reference pulse can be used in order to collect a reference pulse, which may vary among the age group.
  • NDDs neurodegenerative diseases
  • a distance between the characteristic pulses can be used to quantify the degree of NDD severity.
  • some other reasons not necessarily pathological such as age, nutrition and/or lifestyle, for example, may affect the tissue stiffness.
  • the decompositions 1 st pulse, 2 nd pulse, 3 rd pulse, 4 th pulse and 5 th pulse have different heights and widths in Figures 6 and 7.
  • the pulses 4 th pulse and 5 th pulse at the trailing edge are lower in a stiff brain than in an elastic brain, for example. Because there is a wide variety of possibilities how the waveform relates to the brain stiffness it is possible, in an embodiment, that the recognition of the brain stiffness from the waveform is based on artificial intelligence, neural network and/or machine learning methods, for example. Alternatively or additionally, the brain stiffness may be determined based on image processing of a curve of the waveform.
  • UTILIZATION ⁇ OF ⁇ STATISTICAL ⁇ MOMENTS Deviation ⁇ A variation around a mean (or center), i.e. standard deviation (SD), of blood oxygen level dependent (BOLD) may be used to separate patients with brain conditions such as Alzheimer’s disease (AD) from a control group of people having healthy brain.
  • Figs 8A to 8D illustrate examples of variation around the mean.
  • the SD of the people with the brain condition (AD patients) is always larger than that of control group of healthy brain in a very low frequency band (VLF, 0.008-0.1 Hz), respiratory (0.1-0.6 Hz), and cardiac (0.6-5 Hz) bands. Similar procedures apply to oxy-haemoglobin HbO (see Fig.
  • Fig. 10A deoxy-hemoblobin HbR (see Fig. 10B), and water H2O (see Fig.10C) measurements.
  • This is a method to separate patients of a brain condition such as AD from a reference based on a control group with the healthy brain using oxy-haemoglobin HbO and total-haemoglobin HbT in various bands.
  • Fig. 10D presents an example that it is possible to use also only single wavelength (830 nm in this example) of a light source in order to realize a successful analysis. All measurements 10A to 10D show clearly that AD patients have larger variation around the mean than a control group with no AD do. Variation around the mean may be performed as follows 1.
  • HbO, HbR, HbT, and water (H2O) concentration and also their first difference, dHbO, dHbR, dHbT, and dH2O may be used 2.
  • the bands of interest may be VLF, respiratory, and cardiac bands 3.
  • Cross correlation may be applied to each pair for segmented signal using pre-defined window, and a minimum width is a longest period in each band, while a maximum is approximately 1.5 times of the minimum.
  • VLF VLF: 120, 140, 160, 180 seconds, for example b.
  • Respiratory 10, 12, 14, 16 seconds, for example c.
  • Cardiac 2, 2.4, 2.6, 3 seconds, for example 5.
  • the window may be shifted such that it has overlap of about 25%, 50%, and 75% 6.
  • the lag values at the best correlation coefficient from each window may form a time-series data for further analysis 7. This time-series may be subjected to statistical analysis, such as mean, SD, median, IQR (interquartile range), skewness, and kurtosis.
  • the method of computing a ratio between a part of a band and the whole band is known, per ⁇ se, for example in the functional magnetic resonance imaging community but a person skilled in the art is not familiar with it use or applicability in the optical measurements of the brain.
  • the fAPF fractional amplitude of physiological fluctuation
  • VLF very low frequency
  • the VLF band reflects vasomotor response, which relates to constriction and dilation of blood vessel.
  • PDSVLF is the very low frequency band
  • PDSresp is the respiratory band
  • PDScard is the cardiac band
  • PDSulband is the fulband about 0.008 Hz to about 5 Hz.
  • An algorithm of the fAPF measurement may be as follows 1. Calculate power spectral density (PSD) of HbO, HbR, and HbT from VLF band, respiratory band, cardiac band, and the full band used in the measurement. 2. Calculate area under the curve (AUC) of the power spectral density using trapezoid integration, for example 3. Divide the power spectral density of VLF, respiratory, and cardiac bands by the power spectral density of the full band to get the fAPF from corresponding band.
  • PSD power spectral density
  • AUC area under the curve
  • the fAPF of HbT in cardiac band can be used to separate patients with a brain condition from a control group with the healthy brain.
  • the brain condition may mean a brain disease like AD, a mental health issue, lifestyle effect in the brain and/or injury, for example.
  • PSD ⁇ ratio ⁇ of ⁇ high ⁇ to ⁇ low ⁇ band An example of a flow chart of the PSD ratio of high and low band is illustrated in Fig. 15. Another use of the power spectral density relates to characterization of broadband random signals.
  • a ratio between the PSDs of high and low frequency bands is a suitable way to normalize this value.
  • This method was inspired by HRV analysis in frequency domain. Mathematically this may be expressed as follows: There is an example of the high and low PSDs for VLF below. The high and low of the respiratory band and cardiac band are defined in a corresponding manner.
  • the PSD ratio of high to low frequency band may be as follows 1. Calculate threshold to separate low and high band within the VLF band, respiratory band, and cardiac bands a. Log-scale may be used instead of a linear scale b.
  • Centre point may be calculated as the mean of log(low_limit) and log(high_limit).
  • d Convert this log-scale to linear scale using 10 raised to power of centre value.
  • AUC of low and high frequency bands may be computed using trapezoid integration, for example, to get PSD_low_band and PSD_high_band respectively. 5. Divide the PSD of the high frequency band by the PSD of the low frequency band to get the feature.
  • Sample ⁇ entropy An example of a flow chart of the sample entropy is illustrated in Fig 14. Entropy measures randomness of a time series data without prior knowledge about the source of the data generator. Consequently, it can be used to estimate regularity of the data. Unfortunately, entropy estimation requires the whole time series, which is not normally available. However, an estimate of this kind of partial signal may be formed using an algorithm called approximate entropy, a person skilled in the art being familiar with the algorithm, ⁇ per ⁇ se.
  • sample entropy may additionally be used to improve approximate entropy algorithm.
  • Sample entropy may be as follows 1. Sample entropy may be applied together with a Chebychev distance measure to HbO, HbR, and HbT in different bands 2. Compare sample entropy values of different age groups may be compared. Experiments on HbO, HbR, and HbT using sample entropy with Chebychev distance measure are promising. The sample entropy scores are gradually increasing and proportionally to the age of the group, except for HbT in age less than 30 years old. It might indicate that the older the person, the more random the fluctuation of Hb, HbR, and HbT is.
  • Spectral ⁇ entropy An example of a flow chart of the spectral entropy is illustrated in Fig. 15.
  • Spectral entropy is a measure of the spectral power distribution of a signal based on the Shannon entropy. Thus, it applies the Shannon entropy to the signal’s normalised power distribution.
  • the spectral entropy analysis on MREG, EEG, and fNIRS signal revealed electrophysiological and hemodynamic changes in drug- resistant epilepsy.
  • the spectral entropy calculation starts from normalised power spectral density (PSD). There are many methods to get the PSD, e.g., FFT (Fast Fourier Transform) and a periodogram.
  • FFT Fast Fourier Transform
  • the spectral entropy is simply applying Shannon entropy to probability distribution of the normalised PSD.
  • the spectral entropy can be applied to a raw fNIRS signal and concentration changes.
  • Spectral entropy may be as follows 1. Use signal sampled at 10 Hz instead of 800 Hz 2.
  • a band pass filter (BPF) may be applied to select the interested band: VLF, respiratory, or cardiac 3.
  • Spectral entropy may be calculated using pentropy function in Matlab or the like 4. Compare spectral entropy values may be compared between patients with brain issues and a control group with a healthy brain using t-test at 0.05 significant level, for example.
  • the brain issue may mean a brain disease like AD, a mental health issue, lifestyle effect in the brain and/or injury, for example.
  • Cardiorespiratory ⁇ envelope ⁇ modulation ⁇ (CREM) An example of a flow chart of the CREM is illustrated in Fig.16. Analysis on blood oxygen level dependent (BOLD) signal shows that respiratory band modulate signals in cardiac band. The same method may be applied to HbO, HbR, and HbT.
  • An algorithm the CREM may be as follows: 1. Apply BPF (Band Pass Filtering) to select respiratory and cardiac band for the analysis 2. Estimate upper envelope of the cardic signal 3. Apply FFT (Fast Fourier Transform) to the respiratory signal and the upper envelope of the cardiac signal 4.
  • BPF Blood oxygen level dependent
  • FFT Fast Fourier Transform
  • Phase ⁇ Transfer ⁇ Entropy An example of a flow chart of the phase transfer entropy is illustrated in Fig 17. It uses a mean of phase transfer entropy to get a differential phase transfer entropy dPTE (normalised) and PTE values.
  • phase transfer entropy can be expressed as:
  • the non-integer numbers after the symbol “à” refer to a p-value which indicates a probability of error.
  • the result is significant.
  • An acceleration signal recorded by accelerometer is sensitive to vibration; thus, the raw signal may look noisy. For this reason, a low-pass filter may be used to remove unnecessary spike due to this property. Then, the signal may be utilized.
  • Body movement can be estimated by placing a 3D accelerometer on the body, especially on the waist. It may be placed on the wrist and even finger.
  • the axis parallel to the left and right movement can be the dominant signal, while the other serve as secondary ones.
  • Another secondary signal is single magnitude area of the 3D acceleration signal.
  • the forehead is about perpendicular to the vertical axis. The situation is slightly different from supine position with the head to left or right. The angle difference should be enough to separate laying on the side from supine.
  • a machine learning algorithm may be used. Features may be extracted based on the body position and its transition. Since the body position is tracked during sleeping, the signal is processed based on a pre-defined window with certain overlap, e.g., about 25%.
  • the window width may be determined carefully.
  • the width should not be too narrow such that the acceleration signal is too short to capture the movement or too wide such that there are several important movements in the same window.
  • the data processing unit 150 may determine the G-index also based on the position or positions of the subject.
  • the reference for the measurement can be acquired by measuring a control group in one or more defined positions.
  • a set of raw signals from a glymphometer may comprise four fNIRS signals from different wavelengths, EEG, acceleration signals, and other possible modalities, for example. Due to hardware complexity and other reasons, the signals may contain unwanted spikes that should be removed, otherwise the concentration calculation will be affected.
  • Figs 18A to 20 illustrate examples of how to pre-process signals before pulse analysis.
  • Fig. 18A illustrates an example of a raw signal for the G-index measurement.
  • Fig.18B illustrates an example of pre-processed fNIRS signal, where an unwanted spikes of the raw signals have been replaced by vertical lines using a derivative or difference of the spikes. The slope of the spikes is either positive or negative.
  • the gaussian-like pulse then may denote a location of the unwanted spike.
  • a gap between the vertical lines have been replaced by a linear line between the extreme points of the gap for making the signal curve continuous. That is, each unwanted spike is replaced with a linear line based on a linear line equation between two points.
  • the signal may be smoothed by a moving average filter.
  • Figs 19A to 19C illustrate examples of a baseline straightening signal normalizing.
  • Band-pass filter e.g., about 0.5 Hz to about 3 Hz, may be applied to clean_the raw_signal, that is, to remove unnecessary frequency outside the cardiac band. Then baseline wandering is estimated using an upper envelope and a lower envelope of the raw signal. To eliminate or reduce the baseline wandering, estimated_baseline_wandering based on the lower envelope is subtracted from the raw signal. The upper envelope may be estimated from signal_without_baseline_wandering. Dividing signal_without_baseline_wandering may be divided with the upper envelope to normalise it to a range [0,1], for example. Each pulse of the normalized signal may be separated by slicing the signal at points where the signal is zero.
  • Fig.21 illustrates an example of the data processing unit 150 which is shown in Fig.1.
  • the data processing unit 150 comprises one or more processors 2100 and one or more memories 2102 including computer program code.
  • the term “computer” includes a computational device that performs logical and arithmetic operations.
  • the data processing unit 150 is a computer.
  • a “computer” may comprise an electronic computational device, such as an integrated circuit, a microprocessor, a mobile computing device, a laptop computer, a tablet computer, a personal computer, or a mainframe computer.
  • a “computer” may comprise a central processing unit, an ALU (arithmetic logic unit), a memory unit, and a control unit that controls actions of other components of the computer so that steps of a computer program are executed in a desired sequence.
  • a “computer” may also include at least one peripheral unit that may include an auxiliary memory (such as a disk drive orflash memory), and/or may include data processing circuitry.
  • a user interface 152 which is shown in Fig. 1, means an input/output device and/or unit. Non-limiting examples of a user interface include a touch screen, other electronic display screen, keyboard, mouse, microphone, handheld electronic game controller, digital stylus, display screen, speaker, and/or projector for projecting a visual display.
  • Fig. 22 is a flow chart of the analysis method.
  • step 2200 an optical measurement of intracranial dynamics including the intracranial neuronal and fluids dynamics, brain tissue pulsation and/or glymphatic activity through the cranium by an optic measurement arrangement 120.
  • step 2202 at least one of the following analyses is applied, by the data processing unit 150, to electrical signals, the electrical signals being received from the optic measurement arrangement 120 and carrying information on said intracranial dynamics through the cranium in forms of pulses: decomposing 2202A at least one of the pulses of the of said intracranial dynamics into a plurality of characteristic pulses for processing with at least one relation between the characteristic pulses, at least one moment of statistical analysis of the pulses of said intracranial dynamics is determined in step 2202B, a water-hemodynamic coupling is determined in step 2202C from the pulses of said intracranial dynamics based on a definition that a sum of volumes of the brain, celebrospinal fluid and intracranial blood is constant, a ratio between a power spectral density of a fraction of a whole measured frequency band and a power spectral density of the whole measured frequency band is determined in step 2202D, entropy relating to the electrical signals is estimated in step 2202E.
  • a G-index is determined based on said analysis of the electrical signals, which describes a state of brain function, and a reference, which is based on a corresponding analysis of at least one control group with known and/or estimated intracranial dynamics , the G-index representing a relative intracranial dynamics of the brain with respect to the reference.
  • the method shown in Fig. 22 may be implemented as a logic circuit solution or computer program.
  • the computer program may be placed on a computer program distribution means for the distribution thereof.
  • the computer program distribution means is readable by a data processing device, and it encodes the computer program commands, carries out the measurement and analysis and optionally controls the processes on the basis of the measurement.
  • the computer program may be distributed using a distribution medium which may be any medium readable by the controller.
  • the medium may be a program storage medium, a memory, a software distribution package, or a compressed software package.
  • the distribution may be performed using at least one of the following: a near field communication signal, a short distance signal, and a telecommunications signal.

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Abstract

L'invention concerne un procédé de mesure de la dynamique intracrânienne, par réalisation (2200) d'une mesure optoélectronique de la dynamique intracrânienne neuronale et fluidique, de la pulsation du tissu cérébral et de l'activité glymphatique à travers le crâne par un agencement de mesure optoélectronique (120). Ensuite, l'unité de traitement de données (150) applique au moins l'une des analyses suivantes à des signaux électriques reçus à partir de l'agencement de mesure optoélectronique (120) et transporte des informations sur ladite dynamique. Au moins l'une des impulsions de la dynamique est décomposée pour un traitement avec une relation entre les impulsions caractéristiques. Un moment d'analyse statistique des impulsions de ladite dynamique est déterminé. Un couplage de fluide cérébral, en particulier eau-hémodynamique, est déterminé à partir des impulsions de ladite dynamique sur la base d'une définition selon laquelle une somme de volumes du tissu cérébral, du liquide céphalorachidien (CSF) et du sang intracrânien est constante. Un rapport entre une densité spectrale de puissance d'une fraction d'une bande de fréquence entière mesurée et une densité spectrale de puissance de la bande de fréquence entière mesurée est déterminé. L'entropie relative aux signaux électriques est déterminée. Un indice G basé sur ladite analyse des signaux électriques et une référence, qui est basée sur une analyse d'un groupe témoin avec une dynamique intracrânienne connue et/ou estimée, est déterminé, l'indice G représentant une dynamique relative du cerveau.
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