1,031 research outputs found
Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures
The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures.
The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance.
The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration.
The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources.
The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures.
This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications
Scale Stain: Multi-Resolution Feature Enhancement in Pathology Visualization
Digital whole-slide images of pathological tissue samples have recently
become feasible for use within routine diagnostic practice. These gigapixel
sized images enable pathologists to perform reviews using computer workstations
instead of microscopes. Existing workstations visualize scanned images by
providing a zoomable image space that reproduces the capabilities of the
microscope. This paper presents a novel visualization approach that enables
filtering of the scale-space according to color preference. The visualization
method reveals diagnostically important patterns that are otherwise not
visible. The paper demonstrates how this approach has been implemented into a
fully functional prototype that lets the user navigate the visualization
parameter space in real time. The prototype was evaluated for two common
clinical tasks with eight pathologists in a within-subjects study. The data
reveal that task efficiency increased by 15% using the prototype, with
maintained accuracy. By analyzing behavioral strategies, it was possible to
conclude that efficiency gain was caused by a reduction of the panning needed
to perform systematic search of the images. The prototype system was well
received by the pathologists who did not detect any risks that would hinder use
in clinical routine
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
Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network
To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for analyzing and monitoring power line integrity. The results of experiments performed over 32 km long power line under different scenarios are presented. The proposed framework may be useful for monitoring traditional power lines as well as alternative energy source parks and large users like industries. We discovered that the quantity of data transferred changes based on the problem and the size of the planned data packet. When all phases were absent from all meters, we noted a significant decrease in the amount of data collected from the power line of interest. This implies that there is a power outage during the monitoring. When even one phase is reconnected, we only obtain a portion of the information and a solution to interpret this was necessary. Our Q-network was able to identify and classify simulated 190 entire power outages and 700 single phase outages. The mean square error (MSE) did not exceed 0.10% of the total number of instances, and the MSE of the smart meters for a complete disturbance was only 0.20%, resulting in an average number of conceivable cases of errors and disturbances of 0.12% for the whole operation.publishedVersio
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