WO2023111960A1 - Method and portable device for monitoring and identifying cardiac events - Google Patents

Abstract

The invention relates to a system for compiling biopotential signals and processing them to monitor and identify cardiac alerts, said system comprising a device for acquiring biopotential signals (DASB) (10) with at least one biopotential sensor that collects biopotential cardiac signals (ECG), which delivers them to a wireless module (11) that adapts them, encodes them and sends them to a mobile device (20), which sends them to a first computing server (21) where they are processed. Next, said signals are sent at the same time to a second (22) and third (23) computing server; the second server houses a medical platform for consulting data and recording alarms; the third server processes the biopotential signals by means of neural networks, where a first network determines the state of the user, and in the case of detecting a cardiopathy, a second network compares the biopotential signal to preestablished cardiopathic signals, delivering a series of possible scenarios.

Description

METHOD AND PORTABLE DEVICE FOR THE MONITORING AND PREDICTION OF CARDIAC EVENTS technical field

The present invention falls into the field of medical devices, presenting a biopotential signal collection and processing system for cardiac monitoring, prognosis and alerts, as well as methods or procedures for acquiring data by means of a biometric data acquisition device for later sending. to remote server or "cloud" biometric data (representing an electrocardiogram or ECG) methods for detection of heart disease are also presented; Thus, the present invention presents an alternative for people with a heart problem and/or anyone who wants to monitor their heart, from whom the necessary data for the generation of an electrocardiogram (ECG) is collected by means of one or more acquisition devices. of cardiac data with an electronic connection to a portable device such as a cell phone, "Tablet" or computer, where the collected data is grouped, sent to the cloud and analyzed by means of a neural network algorithm, with the aim of to monitor the user's heart, anticipate a cardiac event, issue an alert, if the ECG analysis shows any anomaly, among other functions or actions that could save the user's life.

Neural networks are made up of two or more neurons (X1, X2, … Xn), which try to imitate the functioning of the human brain; a neuron in the context of the present invention, is an abstract object that receives various input data (numeric), to which a weight is assigned (W1, W2, … Wn) performs a weighted sum Ui=∑j(WijYj ), this sum is normalized through a non-linear mathematical function such as ReLu, sigmoid, among others; the result after normalization is taken as the output; neural networks are normally composed of one or more layers (Y1, Y2, … Yj); in this context, a layer refers to a grouped number of neurons where they all receive the same inputs (in a fully connected network), but where each neuron has different weights; thus the first layer is called the input layer, the intermediate layers are called hidden layers and the last layer is called the output layer; these layers of neurons can be sequential, that is, the outputs of one layer of neurons are received as inputs in the next layer, where each layer can have a different number of neurons; neural networks normally comprise three types of databases, the first type of database is training, this has the objective of adjusting the weights (W) of each neuron; The second type of database is the validation database, which is used to validate the weights (W) as well as to eliminate the overfitting problem. The third database is the test database, this is very important since it allows us to measure the error and thus know how well trained the neural network is.

In the particular case of the present invention, the neural network is initially trained under an electrocardiogram data set, this set can be a public database, user data over time, heart disease database obtained from different sources, having as objective the training of the neural network to recognize patterns in an ECG in order to detect or predict heart disease; For this, the neural network used by the present invention has been trained under the theory of inverse propagation, that is: the values of the input layer of the neural network are assigned weights with values established at random, said data from the The input layer is a series of data describing an ECG that has a clinical picture as its attribute (ie, it is known in advance if the ECG comprises a heart disease, the type of heart disease, or if it is a "normal" ECG); at the end, the output value of the neural network is compared with the real output (the known clinical picture) by means of an error function, and based on this, the weights are changed; this is iterated all the time, being the way in which the neural network itself “learns” or in other words adapts to the user data over time, also leaving the door open to admit new data about it into the database. heart disease; The last layer of the neural network of the present invention is made up of a single neuron, which gives a value which, when compared with values obtained in laboratory tests, can determine if the ECG examined reflects some heart disease or if the state of the user's heart is “normal”; In the first case, an alarm system is activated, otherwise it maintains the "normal" label on the user's portable device.

BACKGROUND

We used to read in science fiction books various gadgets that could acquire biometric signals with which to save the lives of the characters; or even in the movies or television we longed for trips into space with space suits that remotely monitored our heart rate, breathing, organic functions and others, it seemed at that time in the distant future; now we marvel at affordable wristwatches that can measure our heart rate, count the steps we take per day, calculate calories when exercising, among other functions. The data can even be exported to a computer for future analysis with a specialized computer program or "app" or "application"; in the scope of the invention in discourse, we can mention some examples of medical devices that acquire data to be analyzed with the aim of alerting or assisting medical personnel in the treatment of heart diseases; Of these we can mention the following:

WO20211866 to Louise Rydén; which uses a plurality of sensors arranged in a garment that collect data to generate an ECG, the sensors communicate wirelessly with a first master controller, which collects the information from the sensors, saves, processes and sends it to a computer control, which is configured to detect abnormalities in the ECG, if an abnormality is detected, the control computer issues an alarm by means of an indicator.

Document US20200214618 by Rik Vullings describes and illustrates a device that obtains ECG signals from a fetus, the device has a plurality of sensors that are arranged on the mother's abdomen, the devices send the information to a master control which collects and stores the information coming from the sensors, it also processes it by first generating an ECG vector, said ECG vector of the unborn is processed by some already trained machine learning process that allows the baby's ECG to be classified, the data is also used as input for the machine learning process.

US20200205745 by Ali Khosousi et al. which illustrates and describes a method for detecting abnormalities in biophysical signals; For this, a computer acquires biophysical signals in one or several channels; said biophysical signals are pre-processed to generate data sets, each data set contains a complete cardiac cycle; the data sets are processed, yielding as a result a value that indicates the presence of some heart disease, the data sets are processed using deep neural networks, which are trained with data from biophysical signals acquired from patients with specific heart diseases and labeled with the presence or absence of heart disease; the result can be thrown in the form of a report or displayed on the computer screen.

US7194298 to Louis Massicotte et al. where a method to determine a trend in an ECG signal is described, where the method is learning based on the information that it is gradually acquiring from the user; the method consists of extracting the base points of said ECG signal, each base point is compared with predetermined thresholds by means of a slow adaptive learning system that uses the user's own data, said thresholds have both vertical and horizontal limits on the graph of the ECG, and these limits are modified over time; if the base points fall within the threshold, then an analysis is run to look at unlikely points, if the points are determinable then a trend can be determined in monitoring the ECG signal based on those new determinable points.

From the study of the previous documents, the need for a cardiac monitoring system that is not intrusive with the patient, of a convenient size, easy to manufacture, low cost, with few maintenance requirements, long-lasting battery, reliable, emerges. , that if necessary, several devices arranged on the user can be used to obtain different channels or leads to obtain an ECG and based on this, it can be determined if the user is in optimal conditions or if he can or is suffering from an ECG. cardiac event, which allows both the user and the medical team to be alerted; objects that the present invention intends to cover broadly.

Brief description of the invention

The biopotential signal collection and processing system for cardiac monitoring, prognosis and alerts described below consists of a biopotential data acquisition device, equipped with a wireless module in electronic connection with a battery and at least one sensor. of biopotential; the aforementioned sensor collects cardiac biopotential signals (ECG), which it delivers to the wireless module, which adapts, encodes and sends them to a mobile device, which in turn sends them to a first computer server (server) where the cardiac biopotential signals are decoded, an ECG is reconstructed, labeled and stored, then said biopotential signals are sent to a second compute server and a third compute server at the same time; the second computer server hosts a medical platform that allows data consultation as well as the recording of alarms; the third computing server processes the biopotential signals by means of neural networks, a first neural network determines if the user's state is "normal" or if he has some heart disease; In case of detecting heart disease, a second neural network compares the biopotential signal against pre-established heart disease signals that can be detected by cardiac leads, providing a series of possible scenarios. Once the statistical mode of these scenarios has been analyzed, the probability of their happening or not is determined; if the user's cardiac status is not in order, an alert is sent to the user and/or authorized interested party; if the user's cardiac status is in order, it notifies the user and/or authorized interested party. In both cases, the labeled ECG is saved to be used in the learning module of the neural network.

[The ] shows an electrical block diagram of the biopotential signal acquisition device.

[The , 3, 4, 5, 6, 7, 8, 9, 10] show a block diagram of the operation, function and signals of the biopotential signal acquisition device; which for ease of reading is illustrated on nine pages.

[The ] shows an operation flow chart of the biopotential signal collection and processing system for cardiac monitoring, prognosis and alerts

[The ] shows a block diagram illustrating cardiac biopotential signal processing.

[The ] shows a diagram illustrating the three layers of a neural network.

[The ] shows a diagram of the biopotential signal acquisition device.

[The ] illustrates a diagram with a user wearing a biopotential signal acquisition device.

[The ] illustrates a diagram with a user carrying a plurality of biopotential signal acquisition devices.

[Figs. 17a, 17b, 17c] illustrate different screens of the mobile application being implemented on the mobile device or computer.

[The ] shows a diagram of an input neural layer with weights and activation function.

[The ] shows a diagram of the middle layer of a neural network.

[The ] shows a diagram of the intermediate and output layer of the first neural network.

[The ] shows a diagram of the intermediate and output layer of the second neural network.

Detailed description of the invention

Alterations to the structure described herein may be foreseen by those knowledgeable in the matter. However, it is to be understood that the present description relates to preferred embodiments of the invention, which is for illustrative purposes only, and is not to be construed as a limitation of the invention.

When referring to any structural feature, mechanical link or component, the terms: mechanically fastened, mechanically secured, mechanically grasped, mechanically fastened, mechanically fixed or mechanically fixed or mechanically attached or mechanically fastened, shall be understood that any means known in the art can be used to join mechanically for example: screws and nuts with and without washers, rivets, screws in threaded holes, welding of any type (arc, tig, mig, etc.), friction welding, ultrasonic welding, glues, binders, overmolding, inserts, pressing, drawing, interference joining, snaps, tabs and straps, wedges, pins, grub screws, slots and safety washers, the above being able to be used separately or together, among other acquaintances in the middle.

Other aspects, modalities, and advantages of those exemplary aspects and modalities are discussed in detail below. The description provides illustrative examples of various aspects and embodiments of the present invention, and is intended to provide an overview or framework for understanding the nature and character of the claimed aspects and embodiments. The accompanying figures are included to provide additional illustration and understanding of the different aspects and modalities, and are incorporated into and constitute a part of this specification. The figures, together with the description, serve to explain the aspects and modalities described and claimed.

The following definitions are provided for the purpose of allowing a better understanding of the invention:

Mechanically coupled – Refers to the transmission of mechanical energy from one element to another by means of gears, friction pulleys, belt pulleys, magnetic means, mechanical transmissions, shafts, mechanical couplings, dynamic couplings, universal joints, Hook joints, homokinetic joints, cardans, wet media, among others.

Electronically coupled, in electronic connection – Refers to the wired or wireless connection between electronic elements vgr.: sensor, "driver", actuator, microcontroller, microprocessor, antenna, peripheral circuit, etc. with the desire to receive / send a signal, either analog or digital.

In electrical connection, electrically coupled – Refers to the connection or supply of power or electrical energy from a power source to electrical or electronic elements that need to be energized in order to operate.

Approximately - The use of this term provides some additional range with respect to the numerical value to which it is being applied. Said additional range is ± 10%. By way of example, but not limitation, if it says "approximately 40 cm", the exact range that is described and/or claimed is between 36 cm to 44 cm.

Plurality - Two or more elements of the same species; crowd, large number of some things, or the greatest number of them.

ECG – Electrocardiogram.

Server – It is a short term used in computer science jargon that refers to or is understood as a computer server.

Data module to be processed- database where the ECG signals to be processed by the neural networks are supported, where said signals have been duly packaged, digitized, conditioned, reconstructed and labeled.

Reference Data Module - database containing digitized, conditioned, reconstructed, and labeled packaged ECG signals known to be related to a known heart disease, and "normal" ECG data from the user in both resting state and ordinary cardiac activity, a state of average cardiac activity, a state of high cardiac activity or a state of extraordinary cardiac activity.

Machine learning module for the calibration of the classification module - database where digitized, conditioned, reconstructed and labeled ECG data are supported that have an affinity with some heart disease, whether they come from an external database or from the heart diseases detected by the second neural network, they also contain the ECG data of the user's state "Normal" or "Extraordinary" both in a resting state or ordinary cardiac activity, a state of medium cardiac activity, a state of high cardiac activity or a state of extraordinary cardiac activity; which are used to customarily recalibrate the "W" weights of each neural network for continuous updating and improvement.

User's “Normal” Status- It means that your ECG is within the parameters considered “normal”.

"Extraordinary" state of the user- It means that his ECG is out of parameters and that he may have some heart disease.

Model adjusted by the user's reference- Refers to updating the databases of the neural network modules with data acquired from the user over time, which allows adjusting the "W" weights of each neural network of customary way.

Medical platform- Application "app" or software that allows the backup, visualization and consultation of ECG signals digitized, conditioned, reconstructed and labeled from the DASB 10; It is also responsible for issuing alarm indications both on the medical platform screen itself and for sending the alarm indication through the server 11 to the mobile device or computer 20.

Figures 1 through 22 are used interchangeably in the following description:

As illustrated in Figure 1, the biopotential signal acquisition device or DASB 10, is made up of the wireless module 11, which comprises a microcontroller and an ultra-low consumption 2.4 GHz antenna, likewise, the DASB 10 It comprises a first 2.4 V voltage regulator 12, as well as a 3 V battery 13 that feeds the microcontroller, antenna, and a second regulator 15; The wireless module 11 receives the signal from a digital block AFE (14) -Analog Front End-, which in a preferred mode, is provided with an analog-digital converter (or ADC for its acronym in English) as well as at least one biopotential sensor which acquires the biopotential signals through at least one electrode 25 in electrical connection with a low-pass filter 28; in such a way that the ADC adapts the signal coming from at least one biopotential sensor so that it can be subsequently processed by the wireless module11; In a preferred embodiment, the battery 13 is electrically coupled to the first 2.4 V voltage regulator 12, which supplies the wireless module 11, the button 16, and the digital block of the AFE 14. Parallel to the process described above, a second 1.8 V voltage regulator 15 feeds a first shunt capacitor module, the digital module AFE30 and an oscillator 17 in electrical connection with the digital module AFE. 14; The 1.8 V voltage regulator 15 is controlled by a digital signal emitted by the wireless module 11. In this preferred mode, the first 2.4 V voltage regulator 12 feeds a first shunt capacitor bank 29, the wireless module 11 and to button 16; as is evident in an alternative embodiment, the wireless module 11 may be electrically coupled to the first 2.4 V voltage regulator 12 or to the battery 13 omitting the use of the first voltage regulator 12; in any case of the alternate mode, the wireless module 11 is electronically coupled to the AFE 14, the second 1.8 V voltage regulator 15, the button 16 and the light emitting diode 18 (or LED), and where appropriate to an ADC; In an alternate embodiment, a DASB 10 may have a plurality of AFEs 14; In an alternative modality, the signal from at least one AFE 14 is received directly by the wireless module 11, which is an analog signal, so the wireless module 11 itself must have an internal ADC with which to convert or " translate” the analog signal to digital; In an alternative modality, the signal from the AFE 14 reaches an ADC which converts or "translates" the analog signal obtained from the user, to a digital signal which is received and subsequently processed by the wireless module 11.

A low consumption antenna is embedded in the wireless module 11 which is used to send and receive information. In a preferred mode, the antenna is compatible with Bluetooth. In an alternative mode, any other protocol or technology available on the market can be used. which are cited by way of example but not limitation: Zigbee, Thread, WIFI, XBee, ZWave, Symphony Link, Sigfox, radio frequency, LoRa, among others. By means of an antenna inside the wireless module (11) communication is established with the mobile device or computer 20; the antenna allows the sending and receiving of data from the wireless module (11) to and from the mobile device or computer 20, as well as the reception of indications, programming or service and diagnostic connections from the DASB 10 itself; In a preferred mode, the wireless module 11 sends and receives information for certain periods of time, for example when it is activated to send information to the mobile device or computer 20 every 5 minutes or when said period is over, it can go into low power consumption for a period of 3 minutes. A first alternative mode occurs when the wireless module 11 "wakes up" each time it needs to send or receive information. A second alternative modality occurs when the wireless module 11 comprises an antenna of the low power consumption type, which allows the wireless module 11 to be "on" all the time, receive or send information as required by the wireless module. eleven.

In a third alternative modality called preferred, it consists of having a plurality of DASBs 10 arranged in various parts of the user's body, to collect a plurality of cardiac biopotential signals, which can be processed in different channels, that is, one channel per channel. each DASB 10. In this case, there is a DASB 10 that will act as "master" which will coordinate the rest of the DASB 10 that will act as "slaves" collecting the information packet from each of these, following some communication protocol to avoid signal collapse, such as assigning a number to each DASB 10 in the network and calling them in order every certain time, for example, when the DASB 10 “master” calls the DASB 10 “slave 1” which will transmit for a certain period of time such as 10s, then it will terminate the communication and continue with the DASB 10 "slave 2" which will also transmit to the DASB 10 "master" for a determined period of time; thus until the DASB 10 "slave n", later the DASB 10 involved enter a state of low consumption during a determined period of time; At the end of the time period, all the DASB 10 take a new sample, resuming the cycle again.

In any mode, the digital signal is encoded by the wireless module 11, this helps to avoid interference with other equipment, add security to the sending of data to the mobile device or computer 18, it also helps to package the data and reduce transmission times , which contributes to lengthening the life of the battery 13; The coding means that can be used are widely known in the field, of which the following can be pointed out: Numeric, alphabetic and alphanumeric coding, which can be used interchangeably in the present invention.

In figures 2 to 10 we can see the signal methods or processes carried out by a DASB 10 in particular, these methods must be kept in mind here as if they were inserted to the letter; In a preferred modality, the first block represents the electrical supply such as a battery or battery13, which allows the initialization of peripherals; If the DASB 10 was powered up for the first time, it enters a low power state, to initialize or “wake up” the wireless module 11, the user must keep the button 16 activated for a specific time (for example, 3 seconds), which will allow the DASB 10 to return to peripheral initialization, where instead, if the DASB 10 was not first powered up, it will initialize the Bluetooth Low Energy (BLE) module with the that the antenna is equipped in this preferred mode and will then permanently implement the BLE pending tasks. Subsequently, the wireless module 11 activates the antenna which in turn puts the BLE in advertising mode (this means that the mobile device or computer 20 can establish a connection with the DASB 10) for a period of time (eg 1min). If a connection was not made, the DASB 10 enters a low consumption state for a period of time (eg 10ms to 5 min), whereas, on the contrary, if the connection was established correctly, the timer will stop to maintain the state. connection, then the state of the visual peripherals changes in this case the LED 18, therefore the mobile device or computer 20 sends an indication for the previous configuration of peripherals, later a timer configuration is carried out for the start of the new cycle, then the second voltage regulator 15 of 1.8 VDC is enabled in order to carry out a configuration of the sampling parameters of the AFE device 14.

The interruption of the AFE device 14 indicates that the sampling or acquisition of biopotential signals has started, that is, that the biopotential sensors send the acquired signals to the ADC or directly to the wireless module 11 (depending on the modality) where In any case, the wireless module 11 acquires a specific amount of samples or signals (called data), once this happens, the AFE device 14 is reset, then it is sent to the second 1.8 VDC voltage regulator at low consumption state, done this, the wireless module 11 sends an indication to the mobile device or computer 20, to start sending data, where the mobile device or computer 20 "responds" to the wireless module 11 that is ready for the reception of data, starting the timer for sending data.

When it is required to send data, the wireless module 11 performs a timer interrupt to perform data encoding and packaging before sending data to the mobile device or computer 20, or to a "master" DASB 10, in any case, during the interruption of the timer (and before the data is sent by the wireless module 11) an encoding of the sampled data is carried out by the DASB 10, immediately afterwards, the DASB 10 packages the sampled data, to later carry out a sending of the data packet by means of the wireless module 11 which in turn terminates the timer interruption allowing it to continue with its account, which is carried out until the specific amount of encrypted and packaged data to be sent is reached , once the mobile device or computer 20 has received the specific amount of encrypted and packaged data (i.e. it has the complete data), it sends a signal to the wireless module 11 so that it proceeds to stop the timer for sending data; finally, the wireless module 11 sends a data transmission termination indication to the mobile device or computer 20.

The interruption of the timer for the start of a new cycle returns specifically in the section on the configuration of the timer for the start of the new cycle, which was previously described.

If there is a loss of connection to the mobile device or computer 20, there is a deactivation of the timers, a reset of the AFE 14 and a disabling of the second 1.8 VDC voltage regulator, this causes a return to the BLE advertising process in where its entire cycle that was previously described is repeated.

The shutdown interruption by button 16 can happen at any instant, which when activated, starts the timer to calculate button 16 activation times, if button 16 was kept activated for the specific time (eg 5 sec.) it enters a a low power state, on the other hand, if the button 16 was not kept activated for the specified time, a reset of the AFE device (14) and data transmission is performed.

In the event of an alternative mode, the DASB 10 can carry out a mesh connection, firstly, a DASB 10 is configured as "master" or as "slave" to continue synchronizing with the DASB network. 10 and thus an interaction with the DASB 10 network is achieved.

As illustrated in figure 11, once the encrypted and packaged data has been received by the mobile device or computer 20, it in turn transmits it to a computer server or first server 21 via WiFi or by any other means that gives you access to the internet (illustrated as “cloud” in figure 12); already in possession of the server 21, it decodes and reconstructs the conditioned digital signal, proceeds to its due labeling for its identification and traceability and its subsequent storage; the aforementioned label comprises at least one number, batch or consecutive of the biopotential signal sample package, user identification number, which can be associated with a database in which personal data such as name, address, telephone number reside mobile, emergency telephone number, doctor's telephone number, doctor's name, among other data; however, the package of digitized, conditioned, labeled, and reconstructed signals is sent to a second server 22 that supports a medical platform with software or apps dedicated to authorized users and followers, where said package of digitized, conditioned, and reconstructed signals is stored and can be consulted on the aforementioned platform; Simultaneously, the packet of digitized, conditioned, labeled, and reconstructed signals is sent to a third processing server 23, which processes and classifies the packet of digitized, conditioned, labeled, and reconstructed signals in search of heart disease, in a preferential modality. (illustrated in figure 11) the search for heart disease is based on the use of a pair of neural networks in tandem (i.e. a first neural network is used and in case of detecting a heart disease the second neural network is used to identify the possible heart disease as well as its percentage of affinity with the ECG under study); where said neural networks each have a data module to be processed, a reference data module, and a machine learning module for calibrating the classification model, resulting in a model adjusted by the user; Now, the aforementioned package of digitized, conditioned, labeled and reconstructed signals is processed and classified using the model adjusted by the user's reference, in one of these states: in a state of rest or ordinary cardiac activity and in a state of activity medium, high or extraordinary heart rate; With the above, it is intended to determine the state of cardiac activity of the user based on the package of digitized, conditioned, labeled, and reconstructed signals acquired, once the state of cardiac activity to which the package of digitized, conditioned signals belongs has been determined. and reconstructed, in diserto it is added to the affinity label of the state of cardiac activity of the user, with these data the package of digitized, conditioned, labeled and reconstructed signals is stored in the machine learning module for the calibration of the classification model with which the user-adjusted neural network model is recalibrated, this is done by recalculating the "W" weights of both neural networks based on the data obtained from digitized, conditioned, labeled and reconstructed signals that are fed or saved automatically. customary in the aforementioned machine learning module for the calibration of the classification model; which derives in a classification of "ordinary" or "extraordinary" state of the user by the first neural network, the result of the second neural network being the frame with a picture of some heart disease that appears in the database of the module reference data (tachycardia, bradycardia, arrhythmia, etc.); of finding an extraordinary state and when framing any heart disease, the authorized followers are notified by means of an alert issued by the second server 22 and received by the mobile device 20 via the first server 21, projecting their cardiac state that was extracted from the package of Digitized, conditioned and reconstructed labeled signals, in addition the data is saved in the machine learning module for the calibration of the classification model.

In an alternative modality (not illustrated) there are two stages of heart disease search, in the first stage of heart disease search based on sinus rhythm, and the second stage based on the use of two neural networks in tandem; Thus, the first stage, which is based on sinus rhythm, is carried out when, from the package of digitized, conditioned, and reconstructed signals already catalogued, a probability of a cardiac event is estimated by analyzing the package of digitized, conditioned, and reconstructed signals looking for heart disease. that can be analyzed or are evident with the simple determination of sinus rhythm (tachycardia, bradycardia, arrhythmia).

If from the previous step it is determined that the sinus rhythm carried by the package of digitized, conditioned and reconstructed signals has a high affinity with some heart disease associated with sinus rhythm, the server 23 proceeds to a new comparison, where the package of digitized signals, conditioned, labeled and reconstructed are now compared with the heart disease models stored in the database of the third server 23, with which a percentage of affinity to some heart disease model is determined. In the event that a high affinity is determined (an affinity percentage greater than 60%) with any of the stored heart disease models, an alert is issued to the server 22 which sends the alert to the mobile device 20 via the server 21 showing the ECG reconstructed that was extracted from the package of digitized, conditioned and reconstructed signals labeled with the percentage of affinity as well as the type of cardiopathy in question, in addition to sending a notification to the user's mobile device or computer; In the event that it is determined that there is no high affinity with any of the stored heart disease models, a new label is added to the package of digitized, conditioned and reconstructed signals indicating that it has gone through this second step of heart disease comparison, again supporting the package of digitized, conditioned and reconstructed signals in the data module of the neural network to be processed.

Continuing with the alternative modality (not illustrated), the search for heart disease based on a pair of tandem neural networks is carried out when the package of digitized, conditioned, reconstructed and labeled signals that have passed the first stage of heart disease search which are based on in sinus rhythm, server 23 is supported by the data module to be processed, with which they are now in a position to be processed in the first neural network, which allows us to know if there is an "ordinary" or "extraordinary" user event or state ”, in the second case, the second neural network is used, which allows us to know the cardiopathy with a greater affinity as well as its percentage of affinity.

However, in any modality, the search for heart disease based on a pair of neural networks in tandem is carried out when the digitized signals, conditioned and reconstructed in a dissertation, are deposited in the data module to be processed, from where they are taken to pass them through the first neural network, which is properly trained and calibrated i.e. The "W" weights have been obtained for each of the neurons that make up the input layer as well as those of the intermediate layers (which is detailed in the example), as illustrated in figures 18, 19, 20 the first neural network in its preferred modality the neural input layer houses data from the package of digitized, reconstructed and labeled signals that have been subjected to the two aforementioned heart disease comparison processes, it being evident that the number of neurons in the input layer It depends on the number of data that said packet of signals includes, typically between 100 and 10 thousand data, which depends somewhat on the memory capacity, type of battery, antenna transmission speed, data transmission protocol, distance to the data receiver, among other technical characteristics of the DASB 10, in a preferred modality the first neural network comprises a pair of intermediate neuronal layers, where the number of neurons of the first intermediate neural layer has between 30% and 50% the number of neurons than the input layer, for its part, the second intermediate neuronal layer has between 3% and 20% the number of neurons than the first intermediate neuronal layer, with which the number of neurons per layer decreases; In an alternative embodiment, the first neural network may comprise of only one intermediate layer of neurons wherein the number of neurons of the single intermediate neural layer is between 10% to 50% the number of neurons of the input layer; In other modalities, the first neural network can have three or more intermediate layers of neurons, which will improve its performance and reliability, but a more powerful computer will be required, together with the response times that will be lengthened due to the large number of data and operations that the computing computer would have to carry out; Going back, in any modality of the first neural network, the output neural layer comprises a single neuron, which evaluates the possibility of having a heart disease, so if the number that throws the output of the neuron of the output layer of the first neuron is close to zero (Vgr. values from zero to 0.69), it means that the package of digitized, reconstructed and labeled signals that have been subjected to the two aforementioned heart disease comparison processes have a low or no correlation with any heart disease, that is, it is in an "ordinary" state of the user, to which the server 23 in a preferred mode sends a couple of signals simultaneously of "all normal" or "nothing to report" to the second server 22 so that it can register on the medical platform as well as on the mobile device or computer via the server 21; In an alternative modality, the server 23 sends a signal of "everything normal" or "nothing to report" to the server 22 so that it can register it in the medical platform; In any modality, the package of digitized signals that were supplied to the first neural network are saved in the machine learning module for the calibration of the classification model, which will help to have more data to later calibrate the "W" weights of the first neural network. , which will help you to have better precision with the use of the system object of the present invention; now, in the opposite case, that is, if the number produced by the output of the neuron in the output layer of the first neural network is close to 1 (Vgr. values between 0.7 and 1), it means that the signal packet digitized, reconstructed and labeled that have been submitted has a high correlation with some heart disease, that is, an "extraordinary" state of the user is found, so it is necessary to use the second neural network to find the heart disease that has the best correlation with said package of data. digitized signals supplied to the first neural; thus said packet of digitized signals is now transferred to the data module to be processed of the second neural network.

The second neural network comprises a database of weights "W" somewhat different from the first neural network studied above, in addition to comprising a plurality of output neurons, however, it uses a similar scheme for the input neural layer. As well as for the intermediate layers, in its preferred modality, the input neural layer houses data from the packet of the aforementioned digitized, reconstructed and labeled signals that are now backed up in the data module to be processed of the second neural network, remembering that Said digitized signals have been processed by the first neural network, as in the first neural network, the digitized signals found in the data module to be processed are processed, it should also be noted that the number of neurons in the input layer of the second neural network depends on the number of data included in said packet of signals that, as previously noted, typically comprise between 100 and 10 thousand data, which depends somewhat on memory capacity, battery type, speed of antenna transmission, data transmission protocol, distance to the data receiver, among other technical characteristics of the DASB 10; Similarly to the first neural network, the second dissertation neural network in a preferred modality comprises a pair of intermediate neural layers, where the number of neurons in the first intermediate neural layer is between 30% and 50% of the number. of neurons than the input layer, for its part, the second intermediate neuronal layer has between 3% and 20% the number of neurons than the first intermediate neuronal layer, with which the number of neurons per layer is reduced; In an alternate embodiment, the second neural network may comprise of only one intermediate layer of neurons wherein the number of neurons in the single intermediate neural layer is between 10% to 50% the number of neurons in the input layer; In other modalities, the second neural network can have three or more intermediate layers of neurons, which will improve its performance and reliability, but a more powerful computer will be required, together with the response times that will be lengthened due to the large amount of data. data and operations that the computing computer would have to carry out; Taking up again, in any modality of the second neural network, the output neural layer comprises a plurality of neurons, where each one of the neurons of the output layer is similar to a heart disease, (see figure 21), making it clear that the The number of neurons in the output layer of the aforementioned second neural network is equal to the number of heart diseases that have been studied and their affinities are defined; in such a way that the output of a given neuron from the output layer of the second neural network will indicate the probability that the user has a certain heart disease (Vgr. Arrhythmia) and its connections with data that are indicators of said heart disease will have weights " W” much larger; this being the case for all diseases borne; This information is processed by statistical means and generates a possible scenario that indicates the most related heart disease or diseases, as well as their percentage of affinity. With this information, the heart disease with the highest affinity among those possible is identified by statistical means, once it has been identified. said cardiopathy, if it has a high affinity percentage (for example, from 60% onwards), the user and authorized followers are warned or alerted to the type of scenario to which it has an affinity (the type of cardiopathy and its affinity percentage), this is achieved by sending said information to the second server 22, said information is also sent to the first server 21 for storage in the machine learning module for calibrating the classification model of both neural networks; For its part, server 22 processes the information and issues an alert, which is displayed on the medical platform, notifying authorized persons for emergencies, as well as being sent to the first server 21, which in turn sends the alert to the mobile device or computer 20, the latter will display an alert signal on the screen as well as a button that allows to know that the user has seen the alert (see figure 17c), in an alternative modality a button can also be displayed that indicates if the user is okay .

We will use an example that will help us to describe in a didactic way the method as well as the system for collecting biopotential signals and processing for cardiac monitoring, prognosis and alerts, which, as we have already pointed out above, uses a pair of tandem neural networks (it is first uses a neural network to verify if there is a heart disease, if so, a second neural network is used to detect the type of heart disease), so the example is also very useful to reinforce the understanding of the method for acquiring and sending of biopotential data (ECG) by means of a DASB 10 or a plurality thereof; the example in dissertation also helps us to describe the heart disease detection method based on a pair of neural networks object of the present invention; Thus we have that, as illustrated in figure 16, there is a user, who is dressed with a DASB 10 arranged on the left chest, at a distance "d" from the central part where the breasts meet (see figure 15 ) the distance “d” is measured using the three middle fingers ( i.e. index, middle and ring fingers); An alternative modality is shown in figure 16 where a first "master" DASB 10 is placed on the left chest, another on the right DASB 10 "slave 1" chest, one on the left side of the DASB 10 "slave 2" waist. ” and a last one in V6, at the intersection of the 5th left intercostal space and anterior axillary line DASB 10 “slave 3”; where the biopotential difference of each point where the DASB 10 have been placed is collected.

Once the master DASB 10 has been placed on the user, the device's built-in button 16 must be held down for a minimum of 3 seconds, to make the DASB 10 visible to nearby bluetooth-enabled mobile devices 20 (see figures 17a, 17b, 17c), the mobile device 20 will first require user registration, generation of a password, and user data, such as name, age, sex, weight, height, among others (see figure 17a). , subsequently, from the mobile device 20 the connection with the DASB 10 must be established and indicate the mode in which it will be operating (see figure 17b); In the mode in which multiple DASB 10 are used, the “slave” devices are progressively “registered”, starting with the DASB 10 “slave 1”, DASB 10 “slave 2”, DASB 10 “slave 3” and last the DASB 10 “master”; once the activation procedure is finished, the mobile device 20 displays a legend indicating that the DASB 10 or DASBs 10 are "online" and "connected"; However, it is worth mentioning that in an alternative modality the connection of two or more DASB 10 is not necessary, since they can work independently, the quantity and location of DASB 10 to be implemented will depend on the user's requirements.

The configuration of the work mode of the DASB 10(s) will only be carried out once, later, when the devices are already configured, the synchronization between the DASB 10 involved will automatically be carried out for taking the samples at time intervals. The samples of each DASB 10 are sent to the mobile device 20 which will later send them to the first server 21.

Once the connection is established, the mobile device 20 by means of an application sends an indication to the DASB 10 or to the "master" DASB 10 (depending on the modality) to start taking samples; In the modality of a solitary DASB 10, upon receiving this, the indication takes a sample of 5,000 data (one piece of data approximately every 2 milliseconds, for a period of approximately 10 seconds), encodes it and sends an indication to the mobile device 20 who will prepare to the reception of data, for its part, the DASB 10 starts sending the data once the mobile device 20 sends a signal to the DASB 10 so that it starts with the data transmission; in the mode in which the user employs a plurality of DASB 10s, the "master" DASB 10 begins with the collection of the slave DASB 10s, sending a signal to the "slave" DASB 10s in the order in which they were given " high" so that they start sending data to the "master" DASB 10, once the number of data (usually 5000 data) or data "packet" (which usually contains 1250 packets) has been completed , the DASB 10 “master” indicates to the DASB 10 slave in turn that it is transmitting, interrupt the transmission; immediately afterwards, it sends an indication to start data transmission to the next DASB 10 "slave", this is repeated until the data packets of each of the DASB 10 "slaves" registered have been collected; subsequently, since the "master" DASB 10 contains the data packets, these are transmitted to the mobile device 20, which in turn retransmits them to the first server 20. In an alternative modality, the "slave" DASB 10 will transmit the data sampled directly to the mobile device or computer, or where appropriate will be retransmitted by the mesh nodes.

Table 1 shows a small sample of the data collected by the DASB 10 sent to the mobile device 20 and retransmitted to the first server 21.

Table 1

The data packet received by the first server 21 is decoded, whereby the data in table 1 is transformed into the data in table 2.

Table 2

Bearing in mind that Table 2 contains a sample of the data packet (remembering that in a preferred mode 5000 data packets are used), the 5000 data packet is sent to two different locations in parallel, the first location is the second server 22 that allows backing up the data package in addition to being able to be consulted on the medical platform, the other location is the third server 23 which is in charge of processing the data packages from the mobile device or computer 20, this server hosts the neural networks in charge of processing the data packets already decoded (such as the sample data illustrated in figure 2), said data packet is introduced to the first neural network as "Data" data in the input layer, and each data is multiplied by a weight “W” (see figure 18), which have already been determined based on “training” iterations (which will be discussed later) with both “normal” ECG data and those that represent a heart disease; now, for each piece of data, a neuron (which in the case of the input layer houses a "Data" from table 2) will have an associated weight "W", which can be seen as the affinity that that particular neuron has before this data; vgr. Assuming that "Data 2500" (Data in position 2500 in Table 2) is a data indicator of arrhythmia, then the weight "W2500" associated with "Data 2500" will have a very high value; As illustrated in figure 18, each neuron in the input layer is multiplied by a previously determined value of weight W according to the following equation:

Yi=f*(∑W _ij Data _i )

Where “Y _i ” is the output of each neuron, f is the activation function of la, “W _ij ” is the weight of the connection of data “i” with neuron “j”, and “Data _i ” is the data i that is received; the activation function "f" is taken as the Sigmoid that is expressed in the following equation:

F(Data)= 1/(1+e ^-Data )

It is worth mentioning that the activation function "f" can be used other than the Sigmoid expressed lines above, such as the hyperbolic tangent function (tanh), ReLU, or any nonlinear preference curve suitable to be used as an activation function in networks. neural; now, at the output of each "Yi" neuron of the input layer there will be a value between 0-1, which will tell us how much said neuron was activated; then, all the outputs of the neurons of the input layer will be passed to all the neurons of a first intermediate layer (which makes up the intermediate layers of the neural network) for the purposes of this example, 2048 neurons are used in the first intermediate layer and 100 in the second intermediate layer, the intermediate layers are illustrated in Figures 19, 20; the number of intermediate layers as well as the number of neurons is determined according to the precision and speed of response required by the system, the more layers and neurons the neural network will be able to learn more and adjust more, however it will require more processing resources including a long response time, so for our neural network that we exemplify here, only two intermediate layers and an output layer with one neuron are used (see figure 20).

The training of both the first and second neural networks can be understood as obtaining a database or matrix of weights "W" for each neural network that allows having the correct weights for each parameter, that is, the correct affinities of each neuron before each data, for this it is necessary to make each neural network go through a training process where ECG data is introduced whose state is known in advance (that is, if it is a healthy signal, its state should be a 1 or close to 1, for the second neural network special attention is paid to the ECGs of the different heart diseases that medical science is aware of, in this particular each heart disease is catalogued, but for practical purposes of detection for the first neural network it should be considered that a cardiopathy yields a value of zero or close to zero); then, the decoded training data is introduced to the first and second neural networks, in order to compare the output obtained against the expected output using an error function, such as binary cross entropy, least squares, among others. Vgr., a training data of a healthy person is introduced to the neural network and this gives us a value of 0.3 when the expected output value should be 1, thus a large discrepancy is observed, which is analyzed with the following function absolute error:

f(Data) = |0.3-1|/1 = 0.7

Depending on how big the error is, it will be how big the adjustment to the weights “W” will be made; This adjustment is done using a method called stochastic gradient descent, where a change of the “W” weights is done at random, re-evaluating the neural network iteratively; if the new result is better, the new “W” weights are saved as the base ones, iterating again until the error function is minimized.

Once the databases or weight matrices "W" have been obtained for each neural network and for each layer of these, one is in a position to process the complete data package that is partially illustrated in Table 2, (ie the 5000 data ) in the first neural network, where the output data from the input layer is passed to the first intermediate layer that for our example comprises 2048 neurons, each of these neurons processes the data delivered by the input layer and the processed using the summation of input data with a matrix of weights "W" according to the equation for "Y _i ", this is repeated with the output data of the first intermediate layer which serve as input data for the second layer intermediate, in an alternative modality you can have more intermediate layers, for which the number of neurons of each layer must be calculated in addition to obtaining the matrices of the weights "W" already trained for each of these subsequent layers; For the purposes of the example in speech at the output of the first neural network, there is a neuron evaluating the output value of said neuron, namely that if said output value of the neuron in the output layer is greater than 0.7, it means that the ECG that was processed is "normal", ie that the user status is "ordinary" and that there is no cardiac event or heart disease to report, sending a notification to the second server 22; however, if the value is less than 0.7, this means that the user's condition is "extraordinary" so there may be some heart disease; In this case, the decoded data packet (partially illustrated in Table 2) is submitted to the second neural network, with the peculiarity that this second neural network comprises a database of weights "W" somewhat different from the first neural network studied above, in addition to comprising a plurality of output neurons, however for the purposes of this example it also uses 2048 neurons in its first intermediate layer and 100 neurons in its second intermediate layer; each of the neurons in the output layer is similar to a heart disease. For the purposes of this example, Figure 21 shows three neurons in the output layer of the second neural network, making it clear that the number of neurons in the layer The output of the aforementioned second neural network is equal to the number of heart diseases that have been studied and their affinities are defined; reverting our attention to the present example and to figure 21 from which it follows that the output of the NA neuron will indicate the probability that the user has arrhythmia, and its connections with data that are indicators of arrhythmia will have much larger “W” weights; the same for NT that indicates the probability of tachycardia, and successively for all the diseases suffered; this information is processed by statistical means and generates a possible scenario that indicates the most related heart disease or diseases as well as their affinity percentage, also identifying the heart disease with the highest affinity percentage, sending this information to the second server 22; this information is also sent to the first server 21 for storage in the machine learning module for calibrating the classification model; For its part, server 22 processes the information and issues an alert, which is displayed on the medical platform, notifying authorized persons for emergencies, as well as being sent to the first server 21, which in turn sends the alert to the mobile device or computer 20, the latter will display an alert signal on the screen as well as a button that allows to know that the user has seen the alert (see figure 17c), in an alternative modality a button can also be displayed that indicates if the user is okay .

It is important to highlight that for the Model adjusted by the user's reference, as long as a greater number of people use the DASB 10 and these, at the same time, use it in a prolonged way over time, it will be fed into the database and at the same time Over time, the machine learning module for the calibration of the neural network classification model will remain in constant "learning", becoming increasingly efficient in identifying cardiac anomalies in addition to customizing its functions according to each user individually. .

Having described the present invention in sufficient detail to enable a technician with knowledge in the matter to reproduce it, it is found to have a high degree of industrial application, as well as inventive activity, it should be noted that the aforementioned technician with knowledge in the matter may envision alternative modalities of the present invention which should be considered within the scope and spirit of the following CLAIMS.

Claims (24)

1. Biopotential signal collection and processing system for monitoring consisting of at least one biopotential signal acquisition device (DASB), a computer or mobile device; a plurality of servers, where:
   The at least one biopotential signal acquisition device is made up of: a wireless module which consists of a microcontroller with an antenna, likewise, it is powered by a first voltage regulator, which adapts the voltage of a battery, said module wireless, receives the signal from an AFE digital block that is provided with an analog-digital converter (or ADC for its acronym in English) as well as at least one biopotential sensor which acquires biopotential signals (ECG) through of at least one electrode, the ADC adapts the signal coming from at least one biopotential sensor so that it can be processed, encoded, packaged and subsequently transmitted via wireless by the wireless module to the mobile device or computer;
   a first server, which receives a coded and packaged signal from the mobile device or computer, said first server decodes and reconstructs the conditioned digital signal, proceeds to its proper labeling for its identification and traceability, as well as for its subsequent storage and sending simultaneous to a second server and third server;
   the second server supports a dedicated medical platform where said packet of digitized, conditioned and reconstructed signals are stored;
   the third server processes the package of digitized, conditioned and reconstructed signals in search of heart disease, based on a first neural network and a second neural network operating in tandem where the first neural network classifies the user's status as "ordinary" or "extraordinary" ”, in this last state of the user, the second neural network is used, which identifies a picture of some heart disease, as well as its percentage of affinity.
2. Biopotential signal collection and processing system for monitoring according to claim 1, wherein both neural networks each respectively have a data module to be processed, a reference data module, a machine learning module for calibration of the classification model.
3. Biopotential signal collection and processing system for monitoring according to claim 1, wherein the first neural network consists of a single neuron in its output layer, which is calibrated and trained to classify the user's state as "ordinary". ” or “extraordinary”
4. Biopotential signal collection and processing system for monitoring according to claim 3, wherein the output layer neuron classifies the user's state as "ordinary", the third server sends a signal to the second server, as well as to the third server. first server to report the status of the user both on the medical platform and on the mobile device or computer.
5. Biopotential signal collection and processing system for monitoring according to claim 3, wherein the output layer neuron classifies the user's state as "extraordinary" the third server processes the biopotential signal in the second neural network.
6. Biopotential signal collection and processing system for monitoring according to claim 1, wherein the second neural network consists of a plurality of neurons in its output layer, each neuron in said output layer being related to a heart disease.
7. System for collecting biopotential signals and processing for monitoring according to claim 6, wherein the neurons of the output layer of the second neural network will indicate the probability that the user has a certain heart disease, the affinity of each heart disease in The output neurons are analyzed by statistical means where a possible scenario is generated that indicates the most related heart disease or diseases as well as their percentage of affinity. With this information, the heart disease that has the highest percentage of affinity is identified, if it has a high affinity percentage, the user and authorized followers are warned or alerted to the type of scenario to which they have affinity.
8. Biopotential signal collection and processing system for monitoring according to claim 7, wherein the alert to the user and authorized followers comprises that the third server send said information to the second server, as well as to the first server for storage in the module machine learning for the calibration of the classification model of both neural networks; For its part, the second server processes the information and issues an alert, which is displayed on the medical platform, notifying authorized persons for emergencies, as well as being sent to the first server, which in turn sends the alert to the mobile device or computer. 20 to alert the user.
9. Biopotential signal collection and processing system for monitoring according to claim 1, wherein the battery of the biopotential signal acquisition device (DASB) is electrically coupled to a first voltage regulator as well as a second voltage regulator , where the first voltage regulator is in electrical connection with the wireless module, a button and the digital block of the AFE; the second voltage regulator is in electrical connection with a first shunt capacitor module, with the AFE digital module, with an oscillator which in turn is in electrical connection with the AFE digital module.
10. Biopotential signal collection and processing system for monitoring according to claim 9, wherein the second voltage regulator is controlled by a digital signal emitted by the wireless module.
11. Biopotential signal collection and processing system for monitoring according to claim 1, wherein the wireless module of the biopotential signal acquisition device (DASB) is electrically coupled to the second voltage regulator or to the battery, omitting the use of the first voltage regulator.
12. Biopotential signal collection and processing system for monitoring according to claim 1, wherein the biopotential signal acquisition device (DASB) comprises a plurality of AFEs.
13. Biopotential signal collection and processing system for monitoring according to claim 9, wherein the wireless module has an internal ADC, allowing it to directly receive at least one biopotential analog signal.
14. A method for the acquisition of biopotential data (ECG) by means of a biopotential signal acquisition device (DASB) composed of a wireless module which comprises a microcontroller with an antenna, likewise, it is fed by a first regulator of voltage, which adapts the voltage of a battery, said wireless module receives the signal from an AFE digital block, which is provided with an analog-digital converter (or ADC for its acronym in English), as well as at least one biopotential sensor which acquires biopotential signals (ECG) through at least one electrode, the ADC adapts the signal coming from at least one biopotential sensor so that it can be processed, encoded, packaged and transmitted via wireless later by the wireless module to the mobile device or computer, the method comprises:
    Launch to wireless module, which activates the Bluetooth module and antenna, allowing wireless connection with a mobile device;
    send an indication to the wireless module for peripheral configuration using the mobile device;
    enable the second voltage regulator of the DASB to configure the sampling parameters of the AFE digital module;
    beginning the acquisition of biopotential signals from the AFE digital module detected by the biopotential sensors;
    receive or collect a specific amount of biopotential signals from the AFE digital module (which will be called "data") through the wireless module, upon completion of this task the wireless module resets the AFE digital module;
    disable the second DASB voltage regulator by sending it to low power mode; then send a signal to the mobile device through the wireless module to start sending data;
    send by the mobile device a signal back to the wireless module indicating that it is ready to receive the data; immediately afterwards, a timer for sending data is initialized;
    interrupting via the wireless module the timer while it is encoding and packaging the data, resetting the timer as soon as the data is encoding and packaging;
    initiate the transmission of encrypted and packaged data to the mobile device through the wireless module, this happens until the mobile device reports having all the encrypted and packaged data sent by the wireless module, the latter stops the timer in addition to sending an indication to the mobile device indicating the termination of data transmission.
15. A method for the acquisition and sending of biopotential data (ECG) between bipotential signal acquisition devices (DASB) composed of a wireless module, which comprises a microcontroller with an antenna, likewise, it is fed by a first regulator of voltage, which adapts the voltage of a battery, said wireless module receives the signal from an AFE digital block, which is provided with an analog-digital converter (or ADC for its acronym in English), as well as at least one biopotential sensor, which acquires biopotential signals (ECG) through at least one electrode, the ADC adapts the signal coming from at least one biopotential sensor so that it can be processed, encoded, packaged and transmitted via wireless later by the wireless module to the mobile device or computer, the method comprises:
    Initialize the “slave” DASB wireless module, thereby activating the Bluetooth module and antenna, allowing wireless connection to a “master” DASB wireless module;
    Send through the wireless module of the DASB "slave" an indication to the wireless module DASB "master" for the configuration of peripherals;
    Enable the second voltage regulator of the “slave” DASB to configure the sampling parameters of the AFE digital module of the “slave” DASB;
    Begin the acquisition of biopotential signals from the AFE digital module of the "slave" DASB detected by the biopotential sensors of the "slave"DASB;
    Receive or collect through the "slave" DASB wireless module a specific amount of biopotential signals from the AFE digital module (which will be called "data"). Upon completion of this task, the "slave" DASB wireless module resets said AFE digital module from DASB “slave”;
    disable the second “slave” DASB voltage regulator by sending it to low power mode; Immediately afterwards, through the wireless module of the DASB "slave" send a signal to the wireless module of the DASB "master" to start sending data;
    send through the wireless module of the "master" DASB a return signal to the wireless module of the "slave" DASB indicating that it is ready to receive the data; then start a timer in the "slave" DASB to send data;
    interrupting via the wireless module of the DASB "slave" the timer while it encodes and packages the data, resetting the timer as soon as the data is encoded and packages;
    Initiate through the wireless module of the DASB "slave" the transmission of encrypted and packetized data to the DASB "master" this happens until the DASB "master" reports having all the encrypted and packetized data sent by the wireless module of the DASB " slave”, the latter stops the timer in addition to sending an indication to the DASB “master” indicating the completion of data transmission.
16. A method of processing packaged coded biopotential (ECG) data (also known as packaged coded data) for the detection of heart disease comprising:
    Sending the encrypted and packaged data from the mobile device to a first computing server;
    Decode and reconstruct the conditioned digital signal through the first computer server, immediately label and identify it for traceability and subsequent storage;
    Simultaneously sending the decoded, labeled and reconstructed conditioned digital signals to a second computing server, as well as to a third computing server;
    Storing through the second computing server the decoded, labeled and reconstructed conditioned digital signals;
    Process through the third computer server the conditioned, decoded, labeled and reconstructed digital signals by means of two neural networks in tandem in search of heart disease;
    Determining by the third computer server the user's cardiac activity status (ie rest, ordinary cardiac activity, average cardiac activity, high cardiac activity, extraordinary cardiac activity) by analyzing the heart rate of the decoded, labeled and reconstructed conditioned digital signals, adding a label to these of the user's cardiac activity status, and thereafter storing and sending the decoded, labeled and reconstructed conditioned digital signals to the second computing server;
    In the event that a high affinity with a known picture of tachycardia, bradycardia or arrhythmia is determined by the second computing server, the second server issues an alert, which is sent to the mobile device through the first computing server;
    In the event that the second computing server determines a low or null affinity with a known tachycardia, bradycardia or arrhythmia picture, the second server sends a signal to the third computing server so that it can compare
    In the event that the second computing server determines a high affinity with a known picture of tachycardia, bradycardia or arrhythmia, the second server sends a signal to the third computing server so that it proceeds to compare the decoded, labeled conditioned digital signals and reconstructed with heart diseases stored in its database; Upon finding a high affinity with any heart disease stored in the database, the third computing server sends a signal to the second computing server so that it issues an alert and sends it to the mobile device through the first computing server; if it does not find an affinity with any heart disease stored in the database of the third computer server, this appends a label to the conditioned, decoded, labeled and reconstructed digital signals, indicating that the two heart disease comparisons have been made;
    Analyze through the third computer server the digital signals conditioned, decoded, labeled and reconstructed by means of a first duly calibrated and trained neural network, which yields a prediction about a possible heart disease;
    Analyze through the third computer server the digital signals conditioned, decoded, labeled and reconstructed by means of a second duly calibrated and trained neural network, which yields a prediction on a plurality of scenarios that correspond to different possible heart diseases that the user could experience. or develop in the near future; where the possible scenarios of heart disease are associated with a probability of occurrence as well as a percentage of affinity with the heart disease in question, then the statistical mode is calculated, the probability of occurrence is reviewed as well as the percentage of affinity to the heart disease in question, of the possible scenarios of heart disease thrown by the second neural network, if these are high, an alert is issued to the second computing server, which in turn will issue an alert to the mobile device through the first computing server.
17. A method of processing packaged coded biopotential (ECG) data (also known as packaged coded data) for the detection of heart disease comprising:
    Sending the encrypted and packaged data from the mobile device to a first computing server;
    Decoding and reconstructing the conditioned digital signal through the first computer server, immediately afterwards the label and identifies it for traceability and subsequent storage;
    Simultaneously sending the decoded, labeled and reconstructed conditioned digital signals to a second computing server as well as to a third computing server;
    Storing through the second computing server the decoded, labeled and reconstructed conditioned digital signals;
    Process through the third computer server in a first stage the conditioned, decoded, labeled and reconstructed digital signals in search of heart disease based on sinus rhythm;
    If it is determined from the previous step that the sinus rhythm carried by the package of digitized, conditioned, and reconstructed signals has a high affinity with some heart disease associated with sinus rhythm, the third server now compares the package of digitized, conditioned, labeled, and reconstructed signals with the heart disease models stored in the database of the third server, with which a percentage of affinity to some heart disease model is determined;
    If a high affinity with any of the stored heart disease models is determined from the previous step, the third server issues an alert to the second server which sends the alert to the mobile device via the first server;
    If a heart disease that can be diagnosed via sinus rhythm is not found, attach a label to the decoded, labeled and reconstructed conditioned digital signals through the third server, indicating that the first stage of heart disease search has been carried out; then sending the packet of digitized, conditioned, labeled and reconstructed signals to the third server to be analyzed by means of two tandem neural networks for heart disease (second stage of heart disease search);
    Analyze through the third computer server during the second stage of heart disease search the digital signals conditioned, decoded, labeled and reconstructed by means of a first duly calibrated and trained neural network, where the first neural network has a single neuron in the layer output which classifies the state of the user as "ordinary" or "extraordinary", in this last state of the user the second neural network is used which identifies a picture of some cardiopathy, as well as its percentage of affinity;
    If an "extraordinary" user state has been detected in the previous step, analyze through the third computing server the conditioned, decoded, labeled and reconstructed digital signals by means of a second duly calibrated and trained neural network, the output layer of said second neural network comprises a plurality of neurons, so that there is an output neuron for each heart disease, where the output of the given neuron of the output layer of the second neural network will indicate the probability that the user has the heart disease that said given neuron treats;
    Determine through the third server and by statistical means based on the data of the output neurons of the second neural network, the heart disease with the highest affinity percentage, if said affinity percentage is high (greater than 60%) emits a alerts the second computing server who in turn will issue an alert to the mobile device through the first computing server.
18. A method of processing packaged coded biopotential (ECG) data (also known as packaged coded data) for the detection of heart disease comprising:
    Sending the encrypted and packaged data from the mobile device to a first computing server;
    Decoding and reconstructing the conditioned digital signal through the first computer server, immediately labeling and identifying it for traceability and subsequent storage;
    Simultaneously sending the decoded, labeled and reconstructed conditioned digital signals to a second computing server as well as to a third computing server;
    Storing through the second computing server the decoded, labeled and reconstructed conditioned digital signals;
    Determining by the third computer server the user's cardiac activity status (ie rest, ordinary cardiac activity, average cardiac activity, high cardiac activity, extraordinary cardiac activity) by analyzing the heart rate of the conditioned, decoded, labeled, and reconstructed digital signals , adding a label to these of the user's cardiac activity status, and thereafter said decoded, labeled and reconstructed conditioned digital signals are stored and sent to the second computing server;
    Process through the third computer server the conditioned, decoded, labeled and reconstructed digital signals by means of two neural networks in tandem in search of heart disease;
    Analyze through the third computer server the digital signals conditioned, decoded, labeled and reconstructed by means of a first duly calibrated and trained neural network, where the first neural network has a single neuron in the output layer which classifies the state of the user in "ordinary" or "extraordinary", in this last state of the user the second neural network is used, which identifies a picture of some heart disease, as well as its percentage of affinity;
    If an "extraordinary" user state is detected in the previous step, analyze through the third computing server the conditioned, decoded, labeled and reconstructed digital signals by means of a second duly calibrated and trained neural network, the output layer of said second neural network consists of a plurality of neurons, in such a way that there is an output neuron for each heart disease, where the output of the given neuron of the output layer of the second neural network will indicate the probability that the user has the heart disease that said given neuron treats;
    Determine through the third server and by statistical means based on the data of the output neurons of the second neural network the heart disease with the highest affinity percentage, if said affinity percentage is high (greater than 60%) it issues an alert to the second computing server who in turn will issue an alert to the mobile device through the first computing server.
19. The biopotential data processing method according to claims 17 or 18, wherein the output neuron of the output layer of the first neural network has a value between zero and 0.69 and means that it has a low correlation with any heart disease. which is equivalent to an "ordinary" state of the user.
20. The biopotential data processing method according to claims 17 or 18, wherein the output neuron of the output layer of the first neural network has a value between 0.7 to 1 and means that it has a high correlation with some heart disease. which is equivalent to an “extraordinary” state of the user.
21. The biopotential data processing method according to claims 17 or 18, wherein the first neural network has a single intermediate neural layer, wherein the number of neurons of the single intermediate neural layer is between 10% to 50% the number of neurons than the input layer.
22. The biopotential data processing method according to claims 17 or 18, wherein the first neural network has two intermediate neuronal layers, where the number of neurons in the first intermediate neural layer is between 30% to 50% the number of neurons than the input layer, for its part, the second intermediate neuronal layer has between 3% and 20% the number of neurons than the first intermediate neuronal layer, with which the number of neurons per layer decreases.
23. The biopotential data processing method according to claims 17 or 18, wherein the alert issued by the third server to the second computing server who in turn will issue an alert to the mobile device through the first computing server is achieved by sending the information of the cardiopathy as well as its percentage of affinity both to the second server and to the first server;
    the second server processes the information and issues an alert to the first server so that it can send it to the mobile device;
24. The first server backs up the heart disease information and its percentage of affinity in the machine learning module for the calibration of the classification model of both neural networks.

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