Cybersecurity in implantable medical devices

Mención Internacional en el título de doctorImplantable Medical Devices (IMDs) are electronic devices implanted within the body to treat a medical condition, monitor the state or improve the functioning of some body part, or just to provide the patient with a capability that he did not possess before [86]. Current examples of IMDs include pacemakers and defibrillators to monitor and treat cardiac conditions; neurostimulators for deep brain stimulation in cases such as epilepsy or Parkinson; drug delivery systems in the form of infusion pumps; and a variety of biosensors to acquire and process different biosignals. Some of the newest IMDs have started to incorporate numerous communication and networking functions—usually known as “telemetry”—, as well as increasingly more sophisticated computing capabilities. This has provided implants with more intelligence and patients with more autonomy, as medical personnel can access data and reconfigure the implant remotely (i.e., without the patient being physically present in medical facilities). Apart from a significant cost reduction, telemetry and computing capabilities also allow healthcare providers to constantly monitor the patient’s condition and to develop new diagnostic techniques based on an Intra Body Network (IBN) of medical devices [25, 26, 201]. Evolving from a mere electromechanical IMD to one with more advanced computing and communication capabilities has many benefits but also entails numerous security and privacy risks for the patient. The majority of such risks are relatively well known in classical computing scenarios, though in many respects their repercussions are far more critical in the case of implants. Attacks against an IMD can put at risk the safety of the patient who carries it, with fatal consequences in certain cases. Causing an intentional malfunction of an implant can lead to death and, as recognized by the U.S. Food and Drug Administration (FDA), such deliberate attacks could be far more difficult to detect than accidental ones [61]. Furthermore, these devices store and transmit very sensitive medical information that requires protection, as dictated by European (e.g., Directive 95/46/ECC) and U.S. (e.g., CFR 164.312) Directives [94, 204]. The wireless communication capabilities present in many modern IMDs are a major source of security risks, particularly while the patient is in open (i.e., non-medical) environments. To begin with, the implant becomes no longer “invisible”, as its presence could be remotely detected [48]. Furthermore, it facilitates the access to transmitted data by eavesdroppers who simply listen to the (insecure) channel [83]. This could result in a major privacy breach, as IMDs store sensitive information such as vital signals, diagnosed conditions, therapies, and a variety of personal data (e.g., birth date, name, and other medically relevant identifiers). A vulnerable communication channel also makes it easier to attack the implant in ways similar to those used against more common computing devices [118, 129, 156], i.e., by forging, altering, or replying previously captured messages [82]. This could potentially allow an adversary to monitor and modify the implant without necessarily being close to the victim [164]. In this regard, the concerns of former U.S. vice-president Dick Cheney constitute an excellent example: he had his Implantable Cardioverter Defibrillator (ICD) replaced by another without WiFi capability [219]. While there are still no known real-world incidents, several attacks on IMDs have been successfully demonstrated in the lab [83, 133, 143]. These attacks have shown how an adversary can disable or reprogram therapies on an ICD with wireless connectivity, and even inducing a shock state to the patient [65]. Other attacks deplete the battery and render the device inoperative [91], which often implies that the patient must undergo a surgical procedure to have the IMD replaced. Moreover, in the case of cardiac implants, they have a switch that can be turned off merely by applying a magnetic field [149]. The existence of this mechanism is motivated by the need to shield ICDs to electromagnetic fields, for instance when the patient undergoes cardiac surgery using electrocautery devices [47]. However, this could be easily exploited by an attacker, since activating such a primitive mechanism does not require any kind of authentication. In order to prevent attacks, it is imperative that the new generation of IMDs will be equipped with strong mechanisms guaranteeing basic security properties such as confidentiality, integrity, and availability. For example, mutual authentication between the IMD and medical personnel is essential, as both parties must be confident that the other end is who claims to be. In the case of the IMD, only commands coming from authenticated parties should be considered, while medical personnel should not trust any message claiming to come from the IMD unless sufficient guarantees are given. Preserving the confidentiality of the information stored in and transmitted by the IMD is another mandatory aspect. The device must implement appropriate security policies that restrict what entities can reconfigure the IMD or get access to the information stored in it, ensuring that only authorized operations are executed. Similarly, security mechanisms have to be implemented to protect the content of messages exchanged through an insecure wireless channel. Integrity protection is equally important to ensure that information has not been modified in transit. For example, if the information sent by the implant to the Programmer is altered, the doctor might make a wrong decision. Conversely, if a command sent to the implant is forged, modified, or simply contains errors, its execution could result in a compromise of the patient’s physical integrity. Technical security mechanisms should be incorporated in the design phase and complemented with appropriate legal and administrative measures. Current legislation is rather permissive in this regard, allowing the use of implants like ICDs that do not incorporate any security mechanisms. Regulatory authorities like the FDA in the U.S or the EMA (European Medicines Agency) in Europe should promote metrics and frameworks for assessing the security of IMDs. These assessments should be mandatory by law, requiring an adequate security level for an implant before approving its use. Moreover, both the security measures supported on each IMD and the security assessment results should be made public. Prudent engineering practices well known in the safety and security domains should be followed in the design of IMDs. If hardware errors are detected, it often entails a replacement of the implant, with the associated risks linked to a surgery. One of the main sources of failure when treating or monitoring a patient is precisely malfunctions of the device itself. These failures are known as “recalls” or “advisories”, and it is estimated that they affect around 2.6% of patients carrying an implant. Furthermore, the software running on the device should strictly support the functionalities required to perform the medical and operational tasks for what it was designed, and no more [66, 134, 213]. In Chapter 1, we present a survey of security and privacy issues in IMDs, discuss the most relevant mechanisms proposed to address these challenges, and analyze their suitability, advantages, and main drawbacks. In Chapter 2, we show how the use of highly compressed electrocardiogram (ECG) signals (only 24 coefficients of Hadamard Transform) is enough to unequivocally identify individuals with a high performance (classification accuracy of 97% and with identification system errors in the order of 10−2). In Chapter 3 we introduce a new Continuous Authentication scheme that, contrarily to previous works in this area, considers ECG signals as continuous data streams. The proposed ECG-based CA system is intended for real-time applications and is able to offer an accuracy up to 96%, with an almost perfect system performance (kappa statistic > 80%). In Chapter 4, we propose a distance bounding protocol to manage access control of IMDs: ACIMD. ACIMD combines two features namely identity verification (authentication) and proximity verification (distance checking). The authentication mechanism we developed conforms to the ISO/IEC 9798-2 standard and is performed using the whole ECG signal of a device holder, which is hardly replicable by a distant attacker. We evaluate the performance of ACIMD using ECG signals of 199 individuals over 24 hours, considering three adversary strategies. Results show that an accuracy of 87.07% in authentication can be achieved. Finally, in Chapter 5 we extract some conclusions and summarize the published works (i.e., scientific journals with high impact factor and prestigious international conferences).Los Dispositivos Médicos Implantables (DMIs) son dispositivos electrónicos implantados dentro del cuerpo para tratar una enfermedad, controlar el estado o mejorar el funcionamiento de alguna parte del cuerpo, o simplemente para proporcionar al paciente una capacidad que no poseía antes [86]. Ejemplos actuales de DMI incluyen marcapasos y desfibriladores para monitorear y tratar afecciones cardíacas; neuroestimuladores para la estimulación cerebral profunda en casos como la epilepsia o el Parkinson; sistemas de administración de fármacos en forma de bombas de infusión; y una variedad de biosensores para adquirir y procesar diferentes bioseñales. Los DMIs más modernos han comenzado a incorporar numerosas funciones de comunicación y redes (generalmente conocidas como telemetría) así como capacidades de computación cada vez más sofisticadas. Esto ha propiciado implantes con mayor inteligencia y pacientes con más autonomía, ya que el personal médico puede acceder a los datos y reconfigurar el implante de forma remota (es decir, sin que el paciente esté físicamente presente en las instalaciones médicas). Aparte de una importante reducción de costos, las capacidades de telemetría y cómputo también permiten a los profesionales de la atención médica monitorear constantemente la condición del paciente y desarrollar nuevas técnicas de diagnóstico basadas en una Intra Body Network (IBN) de dispositivos médicos [25, 26, 201]. Evolucionar desde un DMI electromecánico a uno con capacidades de cómputo y de comunicación más avanzadas tiene muchos beneficios pero también conlleva numerosos riesgos de seguridad y privacidad para el paciente. La mayoría de estos riesgos son relativamente bien conocidos en los escenarios clásicos de comunicaciones entre dispositivos, aunque en muchos aspectos sus repercusiones son mucho más críticas en el caso de los implantes. Los ataques contra un DMI pueden poner en riesgo la seguridad del paciente que lo porta, con consecuencias fatales en ciertos casos. Causar un mal funcionamiento intencionado en un implante puede causar la muerte y, tal como lo reconoce la Food and Drug Administration (FDA) de EE.UU, tales ataques deliberados podrían ser mucho más difíciles de detectar que los ataques accidentales [61]. Además, estos dispositivos almacenan y transmiten información médica muy delicada que requiere se protegida, según lo dictado por las directivas europeas (por ejemplo, la Directiva 95/46/ECC) y estadunidenses (por ejemplo, la Directiva CFR 164.312) [94, 204]. Si bien todavía no se conocen incidentes reales, se han demostrado con éxito varios ataques contra DMIs en el laboratorio [83, 133, 143]. Estos ataques han demostrado cómo un adversario puede desactivar o reprogramar terapias en un marcapasos con conectividad inalámbrica e incluso inducir un estado de shock al paciente [65]. Otros ataques agotan la batería y dejan al dispositivo inoperativo [91], lo que a menudo implica que el paciente deba someterse a un procedimiento quirúrgico para reemplazar la batería del DMI. Además, en el caso de los implantes cardíacos, tienen un interruptor cuya posición de desconexión se consigue simplemente aplicando un campo magnético intenso [149]. La existencia de este mecanismo está motivada por la necesidad de proteger a los DMIs frete a posibles campos electromagnéticos, por ejemplo, cuando el paciente se somete a una cirugía cardíaca usando dispositivos de electrocauterización [47]. Sin embargo, esto podría ser explotado fácilmente por un atacante, ya que la activación de dicho mecanismo primitivo no requiere ningún tipo de autenticación. Garantizar la confidencialidad de la información almacenada y transmitida por el DMI es otro aspecto obligatorio. El dispositivo debe implementar políticas de seguridad apropiadas que restrinjan qué entidades pueden reconfigurar el DMI o acceder a la información almacenada en él, asegurando que sólo se ejecuten las operaciones autorizadas. De la misma manera, mecanismos de seguridad deben ser implementados para proteger el contenido de los mensajes intercambiados a través de un canal inalámbrico no seguro. La protección de la integridad es igualmente importante para garantizar que la información no se haya modificado durante el tránsito. Por ejemplo, si la información enviada por el implante al programador se altera, el médico podría tomar una decisión equivocada. Por el contrario, si un comando enviado al implante se falsifica, modifica o simplemente contiene errores, su ejecución podría comprometer la integridad física del paciente. Los mecanismos de seguridad deberían incorporarse en la fase de diseño y complementarse con medidas legales y administrativas apropiadas. La legislación actual es bastante permisiva a este respecto, lo que permite el uso de implantes como marcapasos que no incorporen ningún mecanismo de seguridad. Las autoridades reguladoras como la FDA en los Estados Unidos o la EMA (Agencia Europea de Medicamentos) en Europa deberían promover métricas y marcos para evaluar la seguridad de los DMIs. Estas evaluaciones deberían ser obligatorias por ley, requiriendo un nivel de seguridad adecuado para un implante antes de aprobar su uso. Además, tanto las medidas de seguridad implementadas en cada DMI como los resultados de la evaluación de su seguridad deberían hacerse públicos. Buenas prácticas de ingeniería en los dominios de la protección y la seguridad deberían seguirse en el diseño de los DMIs. Si se detectan errores de hardware, a menudo esto implica un reemplazo del implante, con los riesgos asociados y vinculados a una cirugía. Una de las principales fuentes de fallo al tratar o monitorear a un paciente es precisamente el mal funcionamiento del dispositivo. Estos fallos se conocen como “retiradas”, y se estima que afectan a aproximadamente el 2,6 % de los pacientes que llevan un implante. Además, el software que se ejecuta en el dispositivo debe soportar estrictamente las funcionalidades requeridas para realizar las tareas médicas y operativas para las que fue diseñado, y no más [66, 134, 213]. En el Capítulo 1, presentamos un estado de la cuestión sobre cuestiones de seguridad y privacidad en DMIs, discutimos los mecanismos más relevantes propuestos para abordar estos desafíos y analizamos su idoneidad, ventajas y principales inconvenientes. En el Capítulo 2, mostramos cómo el uso de señales electrocardiográficas (ECGs) altamente comprimidas (sólo 24 coeficientes de la Transformada Hadamard) es suficiente para identificar inequívocamente individuos con un alto rendimiento (precisión de clasificación del 97% y errores del sistema de identificación del orden de 10−2). En el Capítulo 3 presentamos un nuevo esquema de Autenticación Continua (AC) que, contrariamente a los trabajos previos en esta área, considera las señales ECG como flujos de datos continuos. El sistema propuesto de AC basado en señales cardíacas está diseñado para aplicaciones en tiempo real y puede ofrecer una precisión de hasta el 96%, con un rendimiento del sistema casi perfecto (estadístico kappa > 80 %). En el Capítulo 4, proponemos un protocolo de verificación de la distancia para gestionar el control de acceso al DMI: ACIMD. ACIMD combina dos características, verificación de identidad (autenticación) y verificación de la proximidad (comprobación de la distancia). El mecanismo de autenticación es compatible con el estándar ISO/IEC 9798-2 y se realiza utilizando la señal ECG con todas sus ondas, lo cual es difícilmente replicable por un atacante que se encuentre distante. Hemos evaluado el rendimiento de ACIMD usando señales ECG de 199 individuos durante 24 horas, y hemos considerando tres estrategias posibles para el adversario. Los resultados muestran que se puede lograr una precisión del 87.07% en la au tenticación. Finalmente, en el Capítulo 5 extraemos algunas conclusiones y resumimos los trabajos publicados (es decir, revistas científicas con alto factor de impacto y conferencias internacionales prestigiosas).Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Arturo Ribagorda Garnacho.- Secretario: Jorge Blasco Alís.- Vocal: Jesús García López de Lacall

Biopotential signals and their applicability to cibersecurity problems

Biometric systems are an uprising technique of identification in today’s world. Many different biometric systems have been used in everyone’s daily life in the past years, such as fingerprint, face scan, ECG, and others. More than 20 years evince that the Elektrokardiogramm (EKG) or Electrocardiogram (ECG) is a feasible method to perform user identification as each person has their unique and inherent Elektrokardiogramm (EKG). A biometric system is based on something that every human being is and cannot lose or possess as it is an eye, the DNA, palm print, vein patterns, iris, retina, etc. For this reason, during the last decade, biometric identification or authentication has gained ground between the classic authentication systems as it was a PIN or a physical key. All biometric systems, to be accepted, must fulfill a set of requirements including universality, uniqueness, permanence, and collectability. The EKG is a biometric trait that not only fulfills those requirements but also has some advantages over other biometric traits. To use an EKG as the biometric trait for identification is motivated by four key points: 1) the collection of an EKG is a non-invasive technique so may contribute to the acceptability among the population; 2) a human being can only be identified if they are alive as their heart must be beating; 3) all living beings have their EKG so it is inclusive; 4) an EKG not only provides identification but also provides a medical and even emotional diagnose. There exist many works regarding user identification with EKGs in the current state-of-the-art. Biometric identification with EKGs has been deployed using many different techniques. Some works use the fiducial points of the EKG signal (T-peak, R-peak, P-onset, QRS-offset, ...) to perform the user identification and others use feature extraction performed by a Neural Network as the classification or identification method. As the EKG is a signal which is expressed in time and frequency, many different Neural Network models can exploit the dissimilarity between each EKG signal from each user to perform user identification such as Recurrent Neural Networks, Convolutional Neural Networks, Long-Short Term Memory, Principal Component Analysis, among others offering very competitive results. Focusing on user identification, depending on the user condition in each case, as has been commented before, the EKG not only contributes as an identification method but also offers a diagnosis as it is a person’s condition from a medical point of view or a person’s status regarding their emotional state. Some research has studied certain conditions such as anxiety over EKG identification showing that higher heart rates might be more complex to identify individuals. Nevertheless, there are some drawbacks in the current state-of-the-art regarding identification with EKG. Many systems use very complexly Deep Learning architectures or, as commented, extract the features by a fiducial analysis making the biometric system too complex and computationally costly. One important flaw, not only in biometric systems but in science, is the lack of publicly available datasets and the use of private ones to perform different studies. Using a private database for any research makes the experiments and results irreproducible and it could be considered a drawback in any science field. Furthermore, many of these works use the EKG signal in a sense that it can be recovered from the identification system so there is no privacy protection for the user as anyone could retrieve their EKG signal. Owing to the many drawbacks of a biometric system based on ECG signals, ELEKTRA is presented in this thesis as a new identification system whose aim is to overcome all the inconveniences of the current proposals. ELEKTRA is a biometric system that performs user identification by using EKGs converted into a heatmap of a set of aligned R-peaks (heartbeats), forming a matrix called an Elektrokardiomatrix (EKM). ELEKTRA is based on past work where the EKM was already created for medical purposes. As far as the literature covers up to this date, all the existing research regarding the use of the EKM is focused on the diagnosis of different Cardiovascular Disease (CVD) such as Congestive Heart Failure, Atrial Fibrillation, and Heart Rate Variability, among others. Therefore, the work presented in this thesis, presumably, is the first one to use the EKM as a valid identification method. In aim to offer reproducible results, four different public databases are taken to show the model feasibility and adaptability: i) the Normal Sinus Rhythm Database (NSRDB), ii) the MIT-BIH Arrhythmia Database (MITBIHDB), iii) the Physikalisch-Technische Bundesanstalt (PTBDB), and iv) the Glasgow University Database (GUDB). The first three of them (i, ii and iii) are taken from Physionet a freely-available repository with medical research data, managed by the MIT Laboratory. However, the fourth database (iv ) is also freely available by petition to Glasgow University. Furthermore, to test ELEKTRA’s adaptability and feasibility of the biometric system presented, four different datasets are built from the databases where the EKG signals are segmented into windows to create several Elektrokardiomatrix (EKM)s. The number of EKMs built for each dataset will depend on the length of the records. For example, for the Normal Sinus Rhythm Database (NSRDB) as the EKG records are very extensive, 3000 EKMs or images per user will be obtained. However, for the three other databases, the highest possible number of EKM images is obtained until the signal is lost. It is important to take into account that depending on the number of heartbeats taken to be represented in each EKM, a different number of EKMs is obtained for the three databases in which EKG recordings are shorter. As higher the number of heartbeats o R-peaks taken (i.e., 7bpf), the fewer images will be obtained. Once the datasets of EKMs are constructed, a simple yet effective Convolutional Neural Network (CNN) is built by one 2D Convolution with ReLU activation, a max-pooling operation followed by a dropout to include regularisation and, and finally, a layer with flattened and dense operations with a softmax or sigmoid function depending if the classification task is categorical o binary to achieve the final classification. With this simple CNN, the feasibility and adaptability of ELEKTRA are demonstrated during all the experiments. The four databases are tested during chapters 3, 4, and 5 where the experimentation takes place. In Chapter 3, the NSRDB is studied as the baseline of identification with control users. Different experiments are conducted with aim of studying ELEKTRA’s behavior. In the first experiments, how many heartbeats are needed to identify a user and the costs of convergence of the model depending on the time computing and the number of heartbeats taken to be represented in the EKM are studied. In this case, similar results are achieved in all the experiments as results close to 100% of accuracy are obtained. In the classification of a non-seen user a user, from a different database that has not been seen in any other experiment, is processed and tested against the network. The result obtained is that a non-seen user or an impersonator would only bypass the system one in ten times which can be considered a low ratio when many systems are blocked after three to five attempts. The classification of a user is tested to have a closer situation in which a low-cost sensor is used. For this experiment, an EKG signal is modified by adding Gaussian noise and then processed as any other signal. As a demonstration of our robust system, an accuracy of 99% is obtained indicating that a noisy signal can be processed too. The last experiment over the NSRDB is where this database is used to test the feasibility of ELEKTRA by testing how many images or EKM are enough to identify a user. Even though there is a decrease in accuracy when the number of images used to train the network is decreased too, a 97% of accuracy is obtained when training the network with only 300 EKMs per user. This chapter concludes that, as shown in all the experiments, ELEKTRA is a valid and feasible identification method for control users. The MIT-BIH Arrhythmia Database (MIT-BIHDB) is a database comprising patients with Arrhythmia and random users, and the Physikalisch- Technische Bundesanstalt (PTBDB) comprises patients with different CVD together with healthy users. Hence, the main goal in Chapter 4 is to study the identification system proposed over users with CVD showing ELEKTRA’s adaptability. First of all, the MIT-BIHDB is tested achieving outperforming results and showing how ELEKTRokardiomatrix Application to biometric identification with Convolutional Neural Networks (ELEKTRA) is capable to identify a pool of users with and without arrhythmia with just a slight decrease of the network’s accuracy as a 97% of accuracy is obtained. Secondly, the whole PTBDB is taken to test the biometric system. The result obtained in this experiment is lower than in the other ones (a 93% of accuracy) as the number of images used to train the network has suffered a great decrease compared to the other experiments and 232 users are being studied. Lastly, ELEKTRA has tested over 162 users from the PTBDB with specific CVD which, namely, are Bundle branch block, Cardiomyopathy, Dysrhythmia, Myocardial infarction, Myocarditis, and Valvular heart disease. Through this experiment, the aim is to see ELEKTRA’s behaviour when only users with CVD are included. Better results are obtained compared to the last experiment. It can be owed that the number of users has decreased and that a CVD makes more unique each EKG as many researchers use the EKM for diagnosis purposes. The conclusion extracted from all the experiments from this chapter is that ELEKTRA is capable to identify users with and without CVD approaching a real-life scenario. In Chapter 5 the Glasgow University Database (GUDB) is tested to evaluate the performance of user identification when the users are performing different activities. The GUDB comprises 25 users performing five different activities with different levels of cardiovascular effort: sitting, walking on a treadmill, doing a maths exam, using a handbike, and running on a treadmill. The proposed biometric system is tested with each of these activities for 3 and 5 bpf achieving different results in each case. For the experiments performed where an activity requiring lower cardiovascular effort such as sitting or walking, the accuracy obtained is close to 100% as it is 99.19% for sitting and 98.59% for walking. Then for the scenarios where higher heartbeat rates are supposed the experiment results in lower accuracies as it is jogging with an 82.63% and biking with a 95.51%. For the maths scenario, its outcome is different; the heartbeat rate for each user could be different depending on how nervous each user is. Hence, a 94.0% is obtained with this activity. The conclusion extracted from these first experiments is that it is more complex to identify users when they are performing an activity that requires a higher cardiovascular effort and, consequently have a higher heart rate. For the following experiment, all scenarios have been merged to study the behaviour of a system that has been trained with users performing different activities. In this case, the results obtained seemed to be close to the mean of the results obtained before as the general accuracy for all the scenarios with 3bpf is 91.32%. For the subsequent experiments, some of the scenarios have been merged into two different categories. On the one hand, the more calmed activities (sitting and walking) have been merged in the so-called Low Cardiovascular Activity (LCA) scenario. The accuracy obtained by training and testing with these two activities together is 97.74% and an EER of 1.01%. On the other hand, the High Cardiovascular Activity (HCA) scenario is composed by activities that require a higher cardiovascular effort (jogging and biking). In this case, the results obtained have decreased compared to the last ones as the accuracy is 85.71%. It can be noticed that what has suffered a considerable increase is the False Rejection Rate (FRR) which is 14.17% without implying an increase in the False Acceptance Rate (FAR) which is still very low as it is 0.6%. The last experiments have been called fight of scenarios as there is a confrontation between scenarios by merging some of them and training with some activities or scenarios and predicting with different ones. The first experiments that can be found in this section are training with the LCA group and testing with the HCA group and vice versa. The results here show a great decrease in the performance as accuracies are 37.24% and 46.42%, respectively. This fact implies that it is more complex to identify users that have been registered with a different heartbeat rate. Last but not least, there are a set of experiments where the activities have been confronted such as training the network with the sitting scenario and testing with the jogging scenario. These experiments confirm the hypothesis for higher heart rates, are more complex to identify users, and even more when the network has been trained over calmed users. Even though, one of the main advantages of the presented model is that, even for low accuracies, the False Acceptance Rate has not increased compared to the other experiments meaning that an impostor could not achieve bypassing the system. Lastly, in Chapter 6 conclusions and discussions are offered. A comparison between ELEKTRA and other biometric systems based on EKGs from the current state-of-the-art is offered. These researches from the literature are examined to show how ELEKTRA outperforms all of them in regards to some of the aspects such as efficiency, complexity, accuracy, error rates, and reproducibility among others. It is important to remark that, compared to the other works, in all experiments performed in this doctoral thesis, really high performances with high accuracies and low error rates are achieved. In fact, what is remarkable is that this performance is obtained using a very simple CNN conformed by just one convolutional layer. By achieving outstanding results with a simple neural network, the solidity of ELEKTRA is proven. By this, ELEKTRA contributes to the state-of-the-art by providing a new method for user identification with EKGs with many benefits. Outstanding results in terms of high accuracy and low error rates in the experiments assure the efficiency of ELEKTRA. The fact that the databases used to perform the experimentation in this doctoral thesis are publicly available, makes this work reproducible in contrast to many other works in the literature. In fact, as the databases used are different depending on the users’ nature conforming to each database, it is established that the identification method proposed is inclusive as all living beings have their own EKG and high accuracies are also obtained when testing the model over users with different CVD. Moreover, as it has been proven that users with CVD can also be identified without having major drawbacks, ELEKTRA offers an identification system that can also offer a diagnosis of the user who is being identified in terms of their medical health. In addition, thanks to the GUDB, ELEKTRA can determine for the first time, as far as the literature reaches, that performing user identification with EKGs over users performing activities requiring a higher cardiovascular effort and consequently having higher heartbeat rates, is more complex. In conclusion, by the studies and experiments performed in this doctoral thesis, it can be assumed that ELEKTRA is a feasible and efficient identification method for biometrics with EKG and outperforms the current stateof- the-art proposals in user identification with EKG.Los sistemas biométricos son una técnica de identificación en auge en la actualidad. En los últimos años se han utilizado muchos sistemas diferentes en la vida cotidiana, como la huella dactilar, el escáner facial, o el ECG, entre otros. De hecho, son más de 20 años los que avalan que el Elektrokardiogramm (EKG) o el Electrocardiogram (ECG) es un método fiable para realizar identificación de usuarios. En esta tesis se propone un nuevo método de identificación biométrica denominado ELEKTRA. Por otro lado, existen algunos inconvenientes en el estado del arte actual respecto a la identificación con EKG. Muchos sistemas utilizan arquitecturas muy complejas de Deep Learning o extraen las características importantes mediante un análisis fiduciario, haciendo que el sistema biométrico sea demasiado complejo o costoso. Un fallo importante, no solo en los sistemas biométricos, es la falta de bases de datos públicas y el uso de bases de datos privadas para la investigación. El uso de bases de datos privadas en cualquier estudio hace que los experimentos y los resultados sean irreproducibles y son un inconveniente en cualquier campo de la ciencia. En esta tesis doctoral se ha desarrollado ELEKTRA, un sistema de identificación biométrica, mediante el uso de imagénes llamadas Elektrokardiomatrix (EKM). Estas imágenes se construyen a partir de realizar un mapa de calor de un conjunto de picos R (latidos) alineados, formando una matriz. Con el fin de ofrecer resultados reproducibles, se usan cuatro diferentes bases de datos públicas para demostrar la viabilidad y adaptabilidad del modelo: la Normal Sinus Rhythm Database (NSRDB), la MIT-BIH Arrhythmia Database (MIT-BIHDB), la Physikalisch-Technische Bundesanstalt (PTBDB) y la Glasgow University Database (GUDB). Se han creado nuevas sub-bases de datos de EKMs a partir de cada una de las bases de datos mencionadas. Además, para testear la adaptabilidad y viabilidad de ELEKTRA como sistema biométrico se construye una CNN sencilla, pero eficaz, con una sola capa Convolucional. Las cuatro bases de datos anteriormente mencionadas se han testeado en los Capítulos 3, 4 y 5. En el Capítulo 3 se estudia la NSRDB como prueba de concepto de identificación en usuarios control. Se realizan diferentes experimentos con el objetivo de estudiar el comportamiento de ELEKTRA. Las características estudiadas con esta base de datos son: cuántos latidos son necesarios para identificar a un usuario; los costes de convergencia del modelo presentado; la clasificación de un usuario jamás visto proveniente de una base de datos diferente; la clasificación de un usuario cuya señal EKG ha sido modificada añadiendo ruido Gaussiano; y la viabilidad de ELEKTRA probando cuántas imágenes o EKM son suficientes para identificar a un usuario. En cuanto a las bases de datos que contienen usuarios con CVD, la MITBIHDB contiene pacientes con Arritmia y usuarios sanos, y la PTBDB contiene pacientes con diferentes CVD junto a usuarios sanos. Estas dos bases de datos se estudian en el Capítulo 4, donde se estudia la adaptabilidad de ELEKTRA a distintas CVDs. En primer lugar, se testea la MIT-BIHDB logrando resultados prometedores y mostrando cómo ELEKTRA es capaz de identificar usuarios con y sin arritmia en el mismo grupo. En segundo lugar, se toma la PTBDB completa obteniendo porcentajes altos de acierto y bajos en cuanto a tasas de error concierne. Y por último, se prueba ELEKTRA sobre algunos usuarios con CVD específicos de la PTBDB para ver su comportamiento cuando sólo se incluyen usuarios con CVD. El resultado de estos experimentos muestra cómo ELEKTRA es capaz de identificar a los usuarios con y sin CVD acercándose a un escenario real. Por último, en el capítulo 5 se prueba ELEKTRA sobre la GUDB para evaluar el rendimiento de la identificación de usuarios cuando éstos realizan diferentes actividades cardiovasculares. La GUDB consta de 25 usuarios que realizan cinco actividades diferentes con distintos niveles de esfuerzo cardiovascular (sentarse, caminar, hacer un examen de matemáticas, usar una bicicleta de mano y correr en una cinta). El sistema biométrico propuesto se prueba con cada una de estas actividades para mostrar que es más complejo identificar a los usuarios cuando realizan una actividad que requiere un mayor esfuerzo cardiovascular y, en consecuencia, tienen una mayor frecuencia cardíaca. Los experimentos realizados consisten en fusionar diferentes actividades para estudiar las diferencias entre las frecuencias cardíacas y cómo la identificación del usuario está relacionada la misma. El experimento más representativo se realiza entrenando el modelo con el escenario en el que el usuario está sentado y realizando la clasificación ciega de usuarios del escenario en el cual están corriendo. En este experimento, se obtiene una precisión realmente baja demostrando que para frecuencias de latidos más altas es más complejo identificar a un usuario. De hecho, una de las principales ventajas del modelo presentado es que, incluso con una precisión baja, la Tasa de Falsa Aceptación no ha aumentado en comparación con los otros experimentos, lo que significa que un impostor no podría conseguir eludir el sistema. Sin embargo, si la base de datos se lanza sobre todas las actividades fusionadas, se muestran resultados precisos que ofrecen un modelo inclusivo para entrenar y probar sobre usuarios que realizan diferentes actividades. De este modo, ELEKTRA contribuye al estado del arte proporcionando un nuevo método de identificac