• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    Non-invasive method to determine intracranial pressure through the bioelectric activity of the brain. The present invention relates to a non-invasive method for determining intracranial pressure changes from data from an electroencephalogram obtained from a patient comprising: a) determine the value of the variables of the spectral analysis and of the electroencephalogram networks, b) determine the endogenous variable X of a transfer function whose exogenous variables are the values of the variables obtained in stage a), where changes in the endogenous variable X are indicative of changes in the value of intracranial pressure; The present invention also relates to a device for carrying out the method of the present invention.
    公开号:ES2696904A1
    申请号:ES201730943
    申请日:2017-07-18
    公开日:2019-01-18
    发明作者:Rabbione Guillermo Jose Ortega；Garcia Ancor Sanz；Gomez Jesus Pastor；Zelaya Lorena Carolina Vega；Lopez Maria Del Carmen Torrecilla；Gonzalez Maria Gema Vega；Chicharro Fernando Monasterio；De Sola Rafael Garcia；Rivas Paloma Pulido；Dias Cristina Virginia Torres
    申请人:Fundacion para la Investigacion Biomedica del Hospital Universitario de la Princesa；
    IPC主号:
    专利说明:

    [0001]
    [0002] NON-INVASIVE METHOD TO DETERMINE INTRACRANIAL PRESSURE THROUGH THE BIOELECTRIC ACTIVITY OF THE BRAIN
    [0003]
    [0004] Field of the invention
    [0005]
    [0006] The present invention falls within the general field of biomedicine, and in particular it relates to a method and non-invasive system for determining changes in intracranial pressure from the data from an electroencephalogram obtained from a patient.
    [0007]
    [0008] State of the art
    [0009]
    [0010] Continuous multimodal monitoring of neurocritical patients is an extended modality nowadays in most intensive care units (ICU). Both neurosurgeons and intensivists have a large arsenal of techniques that allow them to continuously monitor critical variables in patients with a multitude of pathologies. Particularly interesting are the cases of traumatic brain injuries (TBI) and subarachnoid hemorrhages (SAH). In these patients, monitoring of intracranial pressure (ICP), cerebral perfusion pressure (CPP), tissue oxygen pressure (PTO) and electrical activity of the brain by means of electroencephalography (EEG), among others, is very useful. many other variables. The value of its use for the identification, prevention and treatment of secondary lesions that may worsen the patient's primary pathology is nowadays indisputable [Le Roux, P., Menon, DK, Citerio, G., Vespa, P., Bader, MK, Brophy, GM, ... & Badjatia, N. (2014). Consensus summary statement of the international multidisciplinary consensus conference on multimodality monitoring in neurocritical care. Neurocritical care, 21 (2), 1-26]. Multimodal monitoring is of great help in those cases where the clinical examination of the patient may be impossible due to the effects of sedoanalgesia or in those cases where the patient is in an altered state of consciousness such as during the coma. Furthermore, the information provided by the monitoring equipment usually precedes the clinical information, so that changes in the monitored variables help to detect changes in the underlying physiological processes and therefore to predict any change in the clinical condition.
    [0011]
    [0012] One of the variables that it is certainly necessary to monitor in every neurocritical patient is the PIC, whose measurement is highly invasive since it is performed by means of a sensor that measures the pressure of the cerebrospinal fluid either intraparenchymally or intraventricularly, and therefore requires surgery for its placement.
    [0013] There is thus a need to provide a non-invasive alternative to determine the risk associated with an increase in intracranial pressure by a non-invasive method.
    [0014]
    [0015] Description of the invention
    [0016]
    [0017] The present invention solves the problems described in the state of the art since it refers to a non-invasive method and system for determining the intracranial pressure in a subject.
    [0018]
    [0019] Thus, in a first aspect, the present invention relates to a non-invasive method for determining intracranial pressure changes from data from an electroencephalogram obtained from a patient (hereinafter, method of the present invention) comprising :
    [0020]
    [0021] a) determine the value of the variables of the spectral analysis and of the electroencephalogram networks,
    [0022]
    [0023] b) determine the endogenous variable X of a transfer function whose exogenous variables are the values of the variables obtained in stage a),
    [0024]
    [0025] where the changes in the endogenous variable X are indicative of changes in the value of intracranial pressure.
    [0026]
    [0027] In a particular embodiment of the present invention, the variables of the electroencephalogram spectral analysis determined in step a), are selected from: bands lower than delta (<1Hz), delta (1-4Hz), theta (4-7Hz) , alpha (8-12Hz), beta (12-30Hz), gamma (30-100Hz), bands above gamma (> 100Hz), spectral entropy of all frequencies. In a more particular embodiment, in step a) of the method of the present invention, at least two of the variables of the spectral analysis are determined
    [0028] In a particular embodiment of the present invention, the analysis variables of the electroencephalogram networks determined in step a), are selected from among the variables obtained from the synchronization between the different electrodes of the electroencephalogram: density of bonds, grouping coefficient average, average path length and degree of each electrode of the encephalogram or node. In a more particular embodiment, in step a) of the method of the present invention, at least two of the network analysis variables are determined.
    [0029]
    [0030] In the present invention, channel and node refer to the same, that is, to the data obtained from an encephalogram, but from different perspectives, thus, by channel it refers to the signal received through each electrode of the electroencephalogram . When the Analysis of networks, instead of channel is called node and refers to each of the points that form the network.
    [0031]
    [0032] In a more particular embodiment of the present invention, the value of the spectral and network analysis variables of step a) is determined by the following steps:
    [0033] I. calculation of the power spectrum in neurophysiological records from each electrode or channel, by means of the Fourier transform;
    [0034]
    [0035] II. calculation of the relative power of each of the frequency bands characteristic of the electroencephalogram;
    [0036]
    [0037] III. calculation of the entropy value and spectral entropy;
    [0038]
    [0039] IV. calculation of synchronization measures between all pairs of neurophysiological records using Pearson correlation, Mutual Information and Phase Synchronization;
    [0040]
    [0041] V. calculation of the link density value, average clustering coefficient, average path length and degree of each node;
    [0042]
    [0043] SAW. estimation of all previous measurements in consecutive temporary windows;
    [0044] In a particular embodiment, the method of the present invention optionally comprises the determination of variables associated with the variability of the heart rate of an electrocardiogram obtained from a patient.
    [0045]
    [0046] In another particular embodiment, the method of the present invention optionally comprises the determination of the variables associated with sedation of a patient.
    [0047]
    [0048] In a second aspect, the present invention relates to a device for the determination of intracranial pressure in a patient (hereinafter, device of the present invention) by the method of the present invention, which comprises means for the determination of the value of the variables of the spectral analysis and networks of an electroencephalogram; and means for calculating the transfer function from said variables.
    [0049]
    [0050] More particularly, the device of the present invention further comprises a microprocessor.
    [0051]
    [0052] More particularly, the device of the present invention comprises means for the registration of the electroencephalographic and the electrocardiographic signal.
    [0053] More particularly, the device of the present invention comprises a supervised learning algorithm.
    [0054]
    [0055] Description of the figures
    [0056]
    [0057] Figure 1: shows the continuous monitoring of 3 consecutive days of different measurements of the EEG, PIC, and heart rate, in each of the temporary windows of 5 seconds. The measurements derived from the EEG are: the density of links, "density", the relative spectral power in the Delta, Theta, Alpha bands and the spectral entropy. It is also possible to observe the measurements of the PIC and its differential, "dif-PIC", as well as the heart rate, estimated by means of an electrode in charge of measuring cardiac activity (derivation V3).
    [0058] Figure 2: shows the estimates of Granger's causality (CG) among all the variables throughout a day of continuous monitoring. When the value of F, determined by Equation (2), for this pair of variables, is not significant (P <0.05), a zero has been placed in that time window. Otherwise, the corresponding value has been plotted. The color scale of these values is the one to the right of the figure, with the highest values being those corresponding to red. In order to make the graph clearer, all values of F greater than 5 have been equalized to 5. Since the non-significant values of F have been zeroed, they remain blank in Figure 2.
    [0059]
    [0060] Figure 3: shows the delays in the cross-correlation between the different variables. It can be observed that there is no correlation with delays (or advances) between variables derived from the EEG, but it does exist between the variables of the EEG and the PIC (and its differential). In most cases, it is clear that there is an advance (blue color) in the maximum of the correlation, implying that the variables derived from the EEG are delayed with respect to the PIC. According to the color scale of the graph, on average this delay is between 5 and 10 steps. Taking into account that these steps are actually temporary windows of 5 seconds, this would correspond to delays of 25 and 50 seconds.
    [0061]
    [0062] Figure 4: shows the percentages of Granger's causality (CG) for the patient sample.
    [0063]
    [0064] Figure 5: shows a particular embodiment of the device of the present invention (7), comprising means for determining the value of the variables of the spectral analysis (8) and networks (9) of an electroencephalogram; and means for calculating the transfer function (10) from said variables, and as optional components: a microprocessor (11), means for recording the electroencephalographic signal (12) and the electrocardiographic signal (13), a supervised learning algorithm (14) and a screen for viewing the data (15).
    [0065]
    [0066] Detailed description of the invention
    [0067]
    [0068] We analyzed the records of 18 (8 women) patients admitted to the ICU of the Hospital de la Princesa during the period October 2015 to March 2017. All patients were monitored continuously with EEG scalp and PIC. The analysis of the data was done retrospectively and always with the informed consent of the patients or their relatives. The research study was approved by the Ethics Committee of the Hospital de la Princesa. The inclusion criteria were the following: patients of both sexes, older than 18 years, presenting TBI or HSA, Glasgow less than 9, monitoring of the PIC. Exclusion criteria included: patients with a stay of less than a week, impossibility of continuous EEG registration. The continuous monitoring of EEG has been by means of 19 scalp electrodes, mounted in a standard configuration 10-20 of location of the electrodes. The records have been sampled at a frequency of 500 Hz and a mono-polar assembly has always been used, referenced to the mean line of electrodes, that is, (Fz Cz Pz) / 3. The records have been acquired continuously for a period (on average) of 5.2 ± 2.3 days for each patient. In order to eliminate pieces of records that have artifacts due to manipulation of the patient, interference with other equipment, etc. A video camera was installed to continuously monitor the patient.
    [0069] Each EEG record has been divided into 5-second windows that, because they are sampled at 500 Hz, correspond to 2500-data windows in each of the 19 channels. We have verified that in these windows of 5 seconds the registers are acceptably stationary ( weak stationarity) and therefore we have calculated several measurements, both spectral and network. In particular we have calculated the relative spectral power, that is, the power in each band with respect to the total power, for each of the Delta, Theta, Alpha, Beta and Gamma bands, as well as the density of links that the network has. of interactions [Sanz-Garcia, A., Vega-Zelaya, L., Pastor, J., Sola, RG, & Ortega, GJ (2017). Towards Operational Definition of Postictal Stage: Spectral Entropy as a Marker of Seizure Ending. Entropy, 19 (2), 81.]. In order to calculate this last measure, we have calculated in the first instance an estimator of the level of synchronization existing between the activity recorded between each pair of electrodes. We have used the following statistics as synchronization estimators: the Pearson correlation, the phase synchronization, mutual information and coherence [Mezeiova K., Paluu M. (2012). Comparison of coherence and phase synchronization of the human sleep electroencephalogram. Clin Neurophysiol 123, 1821-1830]. We have obtained estimates of all these measurements at 5-second intervals, that is, twelve values per minute, in non-overlapping windows.
    [0070]
    [0071] On the other hand, the PIC has also been continuously registered [Kirkman, MA, & Smith, M. (2013). Intracranial pressure monitoring, cerebral perfusion pressure estimation, and ICP / CPP-guided therapy: a standard of care or optional extra after brain injury .British journal of anesthesia, aet418; Kristiansson, H., Nissborg, E., Bartek, J., Andresen, M., Reinstrup, P., & Romner, B. (2013) Measuring Elevated Intracranial Pressure through Noninvasive Methods: A Review of the Literature. J Neurosurg Anesthesiol, 25 (4): 372-85.] In those patients in whom, due to their pathology and particular condition, has been indicated by neurosurgeons and intensivists. In these cases, we have obtained continuous records of intracranial pressure, measured by means of a fiber optic sensor Camino [Martínez-Mañas, RM, Santamarta, D., de Campos, JM, & Ferrer, E. (2000). Pathway intracranial pressure monitor: prospective study of accuracy and complications. Journal of Neurology, Neurosurgery & Psychiatry, 69 (1), 82-86.] That, by means of a transducer that is at the tip of an optical fiber and is inserted in the parenchyma, allowing to obtain intracranial pressure values thanks to changes in the intensity of light reflected in a mirror that is moved by the PIC. The PIC data has been obtained and stored through a program specifically designed for this purpose, NeuroPic. The sampling time of the PIC is approximately 2.9 seconds between successive values, although variable. In order to eliminate this variation in the sampling frequency and obtain "average" PIC data at instants coinciding with those of the EEG temporary windows, we have re-sampled this signal with a sampling time of 5 seconds between consecutive data. In this way we have PIC values, for every minute, at 0 seconds, 5 seconds, 10 seconds, etc. until obtaining 12 PIC values for each minute. This temporal "discretization" coincides with that performed for the values of the temporary windows of the measurements calculated from the EEG records, as explained in the previous paragraph. It should be mentioned that the PIC sensor usually presents a "drift" of its initial calibration, that is, the "zero" of initial pressure, calibrated with respect to atmospheric pressure just before insertion in the parenchyma, appears with values other than zero at take off. In order to correct this deviation we have removed the calculated slope between the initial zero and the final deviation value to the time series of the PIC. In general, this value does not exceed ± 2 mmHg.
    [0072] We have also paired the records of the measurements derived from the EEG and those of the PIC to obtain a new multivariate series with the measurements derived from the EEG, spectral and network, and the average values of PIC in those intervals. This is an important step of "integration" of the underlying dynamics of both types of records. We have also used the electrocardiography (ECG) record obtained through the V3 derivation and from there we have calculated the heart rate by measuring the distance between T waves, this is T-T. Although the EEG and PIC registers are continuous, in some situations one or both registries suffer cuts, such as during the transfer of the patient to other hospital services.
    [0073]
    [0074] Finally, in order to quantify the dependence that some time series can have with respect to others, we have used Granger's Causation (CG) [Granger, CW (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrics: Journal of the Econometric Society, 424-438.], Estimated as follows: given two time series of Ndat each; X = Xk, k = 1; Ndat and Y = YK, K = 1 Ndat, Granger's causality basically examines whether the future values of one variable can be predicted by another. Numerically this can be evaluated by means of autoregressive models of order L that adjust each one of the series in such a way that:
    [0075]
    [0076]
    [0077]
    [0078]
    [0079] If the second prediction is better than the first, it can be assured that the past values of y act on the present values of x . The way to quantify the "best" in a statistical sense is by means of a comparison between £ x and £ and, for example, using the statistic:
    [0080]
    [0081] Fy ^ x ln var £ x )
    [0082] var (£ y ) (2)
    [0083]
    [0084] In such a way that Fy ^ x is non-negative and the larger is Fy ^ x the better is the fit in the combined model and therefore implying a causality of y on x . The statistical significance of this equation can be evaluated by means of a Fisher test.
    [0085]
    [0086] Where RSSx and RSSy are the residual sums of the squares of the models x and y , respectively. In our case, of course, x and y will be replaced by the PIC (or PIC-diff) variables and the measurements from the EEG.
    [0087]
    [0088] To confirm the relationship between ICP and electroencephalographic activity, we calculated different measures of dependence and / or correlation between pairs of variables. With respect to the PIC we use this variable as well as its differential, that is, its first derivative, since in this way any tendency that exists in the time series is eliminated, making it therefore more stationary.
    [0089]
    [0090] We use CG to study a possible dependence of one time series with respect to another. For this we study the CG for all the pairs that can be formed between the following variables: Delta, Theta, Alpha, density of bonds, Spectral Entropy, PIC and the differential of the PIC. We have used Equation (1) for each of the time series in each pair of variables and we have calculated the statistic determined by Equation (2). The statistical significance of this estimator has been calculated by means of a Fisher test, given by Equation (3). The continuous monitoring of the EEG and the PIC allows us to have very long records and therefore to study the dynamics of the existing interactions between the variables. In order to quantify this, we have divided the total record into temporary windows of 30 minutes. In each of these temporary windows we have calculated the CG for each pair of variables. Taking into account that the data, both of the PIC and the EEG measurements are sampled every 5 seconds (see explanation in the methodology section), we will have that the CG evaluations will be done over a length of 30x12 = 360 values ( 12 values per minute) in 30 minutes.
    [0091]
    [0092] As shown in Figure 2, there is a clear dependence of the PIC variable (and of the PIC-diff, that is, its differential), with respect to the measurements derived from the EEG, spectral entropy and the Delta bands, Theta and Alpha. It can also be seen that there is no such dependency in the opposite direction, as well as with respect to the network variable such as link density (density). Although the dependence of the EEG variables on the PIC is throughout the record, in some areas it is more intense (for example, around 23 o'clock on the 24th) and less intense and even nil in other regions, such as for example about 9 o'clock on the 25th.
    [0093] Alternatively we calculate the degree of correlation between all the pairs of variables in order to have an independent measurement of the degree of dependence that exists between the variables studied. For this we calculate the cross-correlation between all pairs of variables, in the same way that we have studied CG. In order to study if there is an advance or delay in the correlation, which would imply a causality effect, we have calculated the cross-correlation between all the pairs of variables with delays up to a maximum of 30, that is, 30 trips for one side and the other, between the two variables studied.
    [0094]
    [0095] From the data shown in Figure 3 it can be seen that there is no correlation with delays (or advances) between variables derived from the EEG, but it does exist between the variables of the EEG and the PIC (and its differential). In most cases, it is clear that there is an advance (blue color) in the maximum of the correlation, implying that the variables derived from the EEG are delayed with respect to the PIC. According to the color scale of the graph, on average this delay is between 5 and 10 steps. Taking into account that these steps are actually temporary windows of 5 seconds, this would correspond to delays of 25 and 50 seconds.
    [0096]
    [0097] Interestingly, there is also a certain correlation between the density of links and the PIC, although there is no clear pattern to identify which variable leads to which.
    [0098]
    [0099] According to the previous results, the dependence between the EEG and the PIC measurements was confirmed for 18 patients admitted to the ICU of the Hospital de la Princesa. In these cases we have found that in the majority of patients there was a strong dependence between the EEG variables -the bands and the spectral entropy- on the ICP. In the same way that we did the calculations in Figure 2, we calculated the CG over the time patients were monitored during their stay in the ICU, both PIC and EEG. Table 1 shows an example for one of the patients. We have calculated the "percentage" of time in which CG is significant (P> 0.05) during the entire monitoring. This calculation has been made for different values of L, that is, of the length of the auto-regressive in equation (1) that in some way is related to the "delay" that exists between both variables.
    [0100]
    [0101]
    [0102] Table 1: CG percentages significant for different variables of a patient.
    [0103]
    [0104] This same procedure has been carried out in the group of 18 patients that we have studied. Figure 4 shows the CG percentages along all the records for each of the patients, and for three delay values, Lag = 5, 10 and 15.
    [0105]
    [0106] The first impression of the figure is that except in a couple of cases (patients 13 and 22), the trend that has been observed in the example of Table 1, is repeated, that is, the percentages of GC between the spectral variables of the EEGs are much larger than inverse ones, which, in most cases, disappear. In second instance, it is striking the fact that the percentages vary, in the same patient, according to the delay that exists between both time series. For example, in patient 11, the percentage of causality between the EEG variables on the PIC is much higher when the delay is 15, compared to a delay of 5 or 10. This fact would imply that the delay or duration of the effect of causality of one variable over another, is patient-dependent.
    [0107]
    [0108] Finally, an automatic classification method, Support Vector Machines (SVM), was used to determine a model that would allow to infer the increase of the PIC. The SVM is a non-linear classification algorithm [Chang CC, Lin CJ (2011). LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol TIST, 2:27.]. The SVM was used as follows: all the temporary windows of all the patients were grouped to build a general model, two thirds of the windows chosen at random were used to train the algorithm, each window had a calculated PIC value assigned to it. the invasive method, in addition to all the variables derived from the EEG; One third of the windows chosen at random were used to determine the efficiency of the model. Through a matrix of confusion, the percentage of prediction of the model, which reached a 91% prediction, decreasing to 89% subtracting chance.
    [0109]
    [0110] Our work of simultaneous record analysis of EEG and PIC in patients hospitalized in the ICU of the Hospital de la Princesa shows the existence of a direct relationship between the dynamics of the PIC and certain variables calculated from the EEG records.
    [0111]
    [0112] The data was calculated from a device (7) comprising means for determining the value of the variables of the spectral analysis (8) and networks (9) of an electroencephalogram; and means for calculating the transfer function (10) from said variables, optionally containing a microprocessor (11), means for recording the electroencephalographic signal (12) and the electrocardiographic signal (13), a supervised learning algorithm (14) and a screen to view the data (15).
    权利要求:
    Claims (10)
    [1]
    1. Non-invasive method to determine changes in intracranial pressure from data from an electroencephalogram obtained from a patient comprising:
    a) determine the value of the variables of the spectral analysis and of the electroencephalogram networks,
    b) determine the endogenous variable X of a transfer function whose exogenous variables are the values of the variables obtained in stage a),
    where changes in the endogenous variable X are indicative of changes in the value of intracranial pressure.
    [2]
    2. A non-invasive method for determining the intracranial pressure according to claim 1, wherein the variables of the electroencephalogram spectral analysis determined in step a) are selected from: bands below delta (<1 Hz), delta (1-4Hz) ), theta (4-7Hz), alpha (8-12Hz), beta (12-30Hz), gamma (30-100Hz), bands above gamma (> 100Hz), spectral entropy of all frequencies.
    [3]
    3. Non-invasive method for determining the intracranial pressure according to any of claims 1-2, wherein the variables of the analysis of electroencephalogram networks determined in step a) are selected from among the variables obtained from the synchronization between the different electrodes of the electroencephalogram : link density, average clustering coefficient, average path length and degree of each node.
    [4]
    4. Non-invasive method for determining the intracranial pressure according to any of claims 1-3, wherein the value of the spectral and network analysis variables of step a) is determined by the following steps:
    I. calculation of the power spectrum in the neurophysiological records from each electrode, by means of the Fourier transform;
    II. calculation of the relative power of each of the frequency bands characteristic of the electroencephalogram;
    III. calculation of the entropy value and spectral entropy;
    IV. calculation of synchronization measures between all pairs of neurophysiological records using Pearson correlation, Mutual Information and Phase Synchronization;
    V. calculation of the link density value, average clustering coefficient, average path length and degree of each node;
    SAW. estimation of all previous measurements in consecutive temporary windows;
    [5]
    5. Non-invasive method according to any of claims 1-4, comprising the determination of variables associated with the variability of the heart rate of an electrocardiogram obtained from a patient.
    [6]
    6. Non-invasive method according to any of claims 1-5, comprising the determination of the variables associated with the sedation of a patient.
    [7]
    Device (7) for the determination of intracranial pressure in a patient by the method according to any of claims 1-6, comprising means for determining the value of the spectral analysis variables (8) and networks (9) ) of an electroencephalogram; and means for calculating the transfer function (10) from said variables.
    [8]
    Device (7) according to claim 7, comprising a microprocessor (11).
    [9]
    Device (7) according to any of claims 7-8, comprising means for recording the electroencephalographic signal (12) and the electrocardiographic signal (13).
    [10]
    10. Device (7) according to any of claims 7-9, comprising a supervised learning algorithm (14).
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    同族专利:
    公开号 | 公开日
    EP3656296A4|2021-04-21|
    ES2696904B2|2020-02-10|
    US20200113461A1|2020-04-16|
    WO2019016420A1|2019-01-24|
    EP3656296A1|2020-05-27|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US8277385B2|2009-02-04|2012-10-02|Advanced Brain Monitoring, Inc.|Method and apparatus for non-invasive assessment of hemodynamic and functional state of the brain|US11109771B2|2020-01-03|2021-09-07|Vivonics, Inc.|System and method for non-invasively determining an indication and/or an assessment of intracranial pressure|
    CN113100785A|2021-04-14|2021-07-13|江南大学|Electroencephalogram signal clustering method based on convex cooperation game|
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    申请号 | 申请日 | 专利标题
    ES201730943A|ES2696904B2|2017-07-18|2017-07-18|NON INVASIVE METHOD FOR DETERMINING INTRACRANIAL PRESSURE THROUGH THE BIOELECTRIC ACTIVITY OF THE BRAIN|ES201730943A| ES2696904B2|2017-07-18|2017-07-18|NON INVASIVE METHOD FOR DETERMINING INTRACRANIAL PRESSURE THROUGH THE BIOELECTRIC ACTIVITY OF THE BRAIN|
    PCT/ES2018/070334| WO2019016420A1|2017-07-18|2018-04-30|Non-invasive method for determining intracranial pressure using the bioelectrical activity of the brain|
    EP18834509.4A| EP3656296A4|2017-07-18|2018-04-30|Non-invasive method for determining intracranial pressure using the bioelectrical activity of the brain|
    US16/626,272| US20200113461A1|2017-07-18|2018-04-30|Non-invasive method for determining intracranial pressure using the bioelectrical activity of the brain|
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