Resumen
The present invention describes a method for determining in real time the degree of activation of the trigeminovascular system. In particular, the invention can be applied in the field of medical devices capable of determining the activation index of the trigeminovascular system, mainly on the basis of the use of biomedical signals of hemodynamic character. The method establishes objective criteria for determining the degree of activation and is described as the result of the application of modelling and data fusion techniques. The method is also based on another type of signals, such as ambient signals, in order to improve statistically in real time the degree of activation determined.
Reivindicaciones
1. A method for determining the degree of activation of the trigeminovascular system based on the monitoring of biometric variables comprising the execution of the following steps: a. Monitoring of biometric and environmental variables and subjective degree of activation of the trigeminovascular system for training models, structured in the following sub-steps: i. Pre-processing of the signals by means of statistical techniques based on the knowledge of the history of each signal, the following values thereof and the distribution thereof (average and standard deviation, among others); ii. Objectification of the subjective measurement of the degree of activation of the trigeminovascular system by means of normalisation of levels and bilateral Gaussian fit with reference to the maximum level recorded; b. Amplification of the set of significant variables for training the models through the following sub-steps: i. Generation of secondary signals ii. Generation and selection of features of the signals acquired and of the secondary signals; c. Estimation of the degree of activation of the trigeminovascular system starting from the monitored variables, the secondary variables and the features generated according to the following sub-steps: i. Generation of groups of variables, combinations of, at least, two of them; ii. Training of the models, one for each group of input variables and with reference to the objective degree of activation; iii. Selection of the models according to the input variables used and the degree of similarity of the signal that they produce (ŷ) with the degree of objectivity (y) expressed in the following formula: fit = 100 1 − | | y − y ^ | | | | y − average y | | <img class="EMIRef" id="560798817-ib0006" /> iv. Starting from the available variables and the model chosen, a first estimation of the degree of activation of the trigeminovascular system is obtained as an output thereof; d. Reduction of the estimation error of the degree of activation of the trigeminovascular system by means of the use of a second set of variables, which can use expert knowledge strategies, such as data mining and/or fuzzy logic, for the correction and fit of the estimated degree of activation of the trigeminovascular system. 2. The method according to claim 1 for monitoring hemodynamic biometric variables, occipital electroencephalogram, climatological signals from the surroundings and environmental signals for the sending thereof to a cloud storage platform to be processed. 3. The method according to the preceding claims, wherein the data processing is structured in the following sub-steps: a. Synchronisation of the different signals with time marks of all the data; b. Elimination of out-of-range data and filtering of the signals; c. Application of techniques for automatic signal regeneration based on the statistical behaviour of the signal; d. Decimating the signals to reduce the amount of input data for the models of claim 1; e. Objectification of the degree of activation of the trigeminovascular system by means of a non-limited level scale and a continuous Gaussian fit mechanism with discreet subjective values of the degree of activation of the trigeminovascular system. 4. The method according to the preceding claims,characterised by the generation of secondary signals and characteristic features of the signals that is developed in the following sub-steps: a. Calculation of the heart rate (HR) by counting the number of events of the ECG signal in time windows of 20 seconds with 10 seconds of overlap by defining wait times between peaks and level decision criteria for the failure to detect false positives; b. Calculation of the pulse transit time (PTT) for determining the arterial pressure by means of regression functions calculated with the peaks detected from the PPG and ECG signal; c. Calculation of the qEEG signal through the calculation of the energy of the band pass filtering without overlap of the EEG signal. 5. The method according to the preceding claims,characterised by a System for Selecting Models Depending on the Sensors (SMDS<2> ) consisting of the precedence of models for estimating the degree of activation of the trigeminovascular system which can be performed by means of a mechanism based on statistical confidence. 6. The method according to the preceding claims,characterised by the linear combination of the set of models set out in Claim 5. 7. The method according to the preceding claims, wherein the reduction of the estimation error in Claim 1 is developed in three sub-steps; a. Detection and elimination of events by defining a threshold for which the events that do not determine a degree of activation of the trigeminovascular system with an index greater than 50% with respect to the maximum will be eliminated; b. Detection and elimination of events based on time by defining a temporal threshold of 60 minutes, wherein the events that exceed the level threshold but have a duration less than the time threshold will be eliminated; while the events that are at a shorter distance than this threshold of another event will be considered the same; c. Application of expert knowledge techniques, such as fuzzy logic algorithms, in order to grant degree of confidence to the activation events of the trigeminovascular system able to re-feed the signal to the monitoring signal. 8. The method according to claim 1, wherein the monitoring of biometric variables obtained by means of sensors will be developed in the following sub-steps: a. The detection of the state will be able to be carried out by means of a decision taken on the statistics of the data recorded in previous moments; b. If a sensor is not available, the models that include variables dependent on it will not be chosen. 9. The method according to claim 1 based on mobile equipment that communicate the information to the monitoring devices.
