METHOD FOR DETERMINING THE DEGREE OF ACTIVATION OF THE TRIGEMINOVASCULAR SYSTEM

Abstract

The present invention describes a method for determining in real time the level of activation of the trigeminovascular system. In particular, said 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.

Claims

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: monitoring of biometric and environmental variables and subjective degree of activation of the trigeminovascular system for training models, structured in the following sub-steps: 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); 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; amplification of the set of significant variables for training the models through the following sub-steps: generation of secondary signals generation and selection of features of the signals acquired and of the secondary signals; 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: generation of groups of variables, combinations of, at least, two them; training of the models, one for each group of input variables with reference to the objective degree of activation; selection of the models according to the input variables used and the degree of similarity of the signal that they produce () with the degree of objectivity (y) expressed in the following formula: $\mathrm{fit}=100.Math.\left(1-\frac{||y-\hat{y}||}{||y-\mathrm{average}\left(y\right)||}\right)$ 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; reduction of the estimation error of the degree of activation of 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, where there is a monitoring of 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; where the data processing is structured in the following sub-steps: synchronisation of the different signals with time marks of all the data; elimination of out-of-range data and filtering of the signals; application of techniques for automatic signal regeneration based on the statistical behaviour of the signal; decimating the signals to reduce the amount of input data for the models; 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; and that is characterised by the generation of secondary signals and characteristic features of the signals that is developed in the following sub-steps: 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; 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; calculation of the gEEG signal through the calculation of the energy of the band pass filtering without overlap of the EEG signal.

2-4. (canceled)

5. The method according to claim 1, characterised by a System for Selecting Models Depending on the Sensors (SMDS.sup.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 claim 1, characterised by the linear combination of the set of models depending on the sensors.

7. The method according to claim 1, wherein the reduction of the estimation error is developed in three sub-steps: 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; detection and elimination of events based on time by defining 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; 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: the detection of the state is carried out by means of a decision taken on the statistics of the data recorded in previous moments; and if a sensor is not available, the models that include variables dependent on it are not chosen.

9. The method according to claim 1 based on mobile equipment that communicate the information to the monitoring devices.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The following drawings illustrate the preceding description:

[0079] FIG. 1 shows the diagram of the creation of models not in real time. This diagram is executed in the training step and gives rise to the models (f) that will predict the migraines of each patient. The models are created in the module (3) through the biometric variables (b[1] and c[1]) and the synthesised pain signal (a). Different models are created for each different combination of variables. The models are validated in the module (4); the best models (f) will be chosen from these models in hierarchical order in the module (4). In turn, the module (3) also trains prediction models based on environmental variables (b[2] and c[2]); moreover, for each different combination of these variables different models are created and the best ones are chosen.

[0080] FIG. 2 shows the diagram of modelling and prediction in real time of the migraine prediction method through the hemodynamic variables of the patient and the cerebral electrical activity (d1), and with support from local and overall environmental variables (d5). Said variables are pre-processed and synchronised in the pre-processing module (1). In this prediction step (in a loop) the suitable models are chosen for the patient and depending on the variables available in the module (5) and the prediction (p[1]) is performed through the linear combination of models of biometric variables in the module (6). The prediction arrives at the decision module (7) where the false events are removed and, with help from the prediction of the environmental variables (p[2]) and the result from Machine Learning techniques (g), it is decided if a migraine (i) has been detected or not. The decision arrives at the actuator (8), which warns the patient in order to move up the ingestion of the medicinal product and prevent the pain.

EMBODIMENT OF THE INVENTION

[0081] The monitoring implies the recording of a sufficient number of migraines for the training step. In order to train the modelling algorithms, it is considered that the training can be sufficient as of 10 migraine episodes (the monitoring time generally oscillates between 4 and 6 weeks). One possible way of proceeding to acquire data is described below in an indicative and non-exhaustive manner.

[0082] The monitoring of the biometric variables (d1) is carried out with commercial outpatient monitoring devices. The ECG sensor can have as many derivations as desired, but having only three electrodes is enough to extract the HR (hs1); in this case, they will be placed on the precordial horizontal plane in derivations V3, V4 and V5. The EDA sensor (h1) placed in the arm acts to measure the relative variations in perspiration; like the superficial temperature sensor (h2), placed as close as possible to the armpit. One way of acquiring the SpO2 (h3) and the PPG (h4) is by means of the use of an oximetry clip placed on a finger. The EEG electrodes will be placed on the occipital area, in the reference points OZ, O1 and O2 (according to the international system 10-20). The PTT will be calculated through the ECG and PPG signals and by applying one of the bibliographical methods (Yoon Y, Cho JungH, Yoon Gilwon, Non-constrained Blood Pressure Monitoring Using ECG and PPG for Personal Healthcare. 2009; 33(4):261-266). The HR can be calculated in intervals of 20 seconds with a 10 second overlap. The qEEG is the energy of the Alpha, Beta, Gamma, Delta and Theta bands in 20 second intervals without overlap.

[0083] At the same time that the biometric variables (d1) are recorded, the local and overall environmental variables (d5) are recorded. The overall environmental variables are taken from the geographic area corresponding to the location of the patient, and a national weather service can be used. The local environmental variables are monitored through a weather station that is always near the patient. The good synchronisation of all the data must be taken into account; to do so, a smartphone can be used to capture all the weather data. The subjective sensation of the pain (d2) is recorded through a mobile application. The patient indicates the beginning and end of the pain, as well as the subjective evolution thereof. With the same mobile application, the activity of the patient (d3) is recorded, and furthermore, there is knowledge of some of their clinical data (d4) relevant for the study, such as weight, age, gender or diseases related to migraines. All the data (d1-d5) collected during the training step is pre-processed (1 and 1-2) and used to train migraine models in (3).

[0084] Both the training of the models, and the validation (4), are performed not in real time in high-capacity computing equipment. The result of the training step is a set of models that is different for each possible combination of variables. (f). The models are trained by means of supervised techniques, where the inputs are the processed variables, and the output to be fitted is the subjective pain of the patient pre-processed in (2). Models will be trained for all the possible combinations of input variables. In validation, the best ones will be ranked and chosen to be used in the prediction step in real time.

[0085] In the prediction step in real time, the system for selecting models depending on the sensors (5) is used to select the variables of interest of each patient and the hierarchical set of models (f-2) depending on the sensors available. To do so, the state of the sensors must be known at all times, and if there are any that are not available, the models will be changed. Once the chosen models are had, these are applied one by one to the input variables, giving rise to a set of predictions; the final prediction (p) is calculated in (6) like the linear combination of said predictions. The horizon in which the prediction is performed will depend on the quality of the model obtained, for example 30 minutes. The module for correction and fit of the prediction (7) removes the false prediction points according to criteria of duration and degree of detection; thus, possible false alarms are able to be eliminated. Furthermore, decision criteria (g) are applied in order to weight the answer. The prediction obtained from the biometric variables is weighted by the decision criteria. These weightings are the result of the fuzzy logic algorithms that, based on the knowledge of the environmental variables and of the activity of the patient, regulate the prediction, for example lessening or increasing the levels thereof. Finally, the prediction (i) is transmitted to the patient through the actuator module (8, the mobile phone) so that it can move up the ingestion of the medicinal product against the migraine pain, before it begins.

[0086] The retraining of the models can be carried out automatically in a transition period in which it is still in the real-time prediction step, but the set of models of the patient starts to be updated. The retraining will be necessary when the patient finds that the predictions are no longer correct or a clinical evaluation considers it appropriate. In real time, the patients will not mark the evolution of their pain (d2) for which reason the only record of errors in the prediction that can be had will be the one of the false positives (migraines that were not detected).

[0087] The hardware of the monitoring devices must have enough computational capacity to be able to sample the variables at the sampling rate required and send the data wirelessly. The capacity to stop monitoring when they are aware of the state of the prediction must also be supported by the hardware and the firmware of the devices.

[0088] The present invention has application in the field of medical devices for the early alert of migraine pain. The network of electronic outpatient health monitoring devices approved for medical use is increasingly widespread and established; furthermore, the portability thereof and the duration of the batteries thereof keeps increasing. The machine-man interface for alerting patients is performed through a smartphone terminal, which is common today. For all of this, the present invention can have an immediate use in monitoring migraine patients in order to predict their attacks.

[0089] This invention enables those suffering from migraines to perform the earlier ingestion of the medication against migraine pain so that it has a complete effect and they are able to thus prevent the painful phase of the migraine. The use of this method increases the quality of life of the patients, as well as reduces the direct and indirect costs that the disease causes worldwide.