A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Security and privacy services based on biosignals for implantable and wearable device

Mención Internacional en el título de doctorThe proliferation of wearable and implantable medical devices has given rise to an interest in developing security schemes suitable for these devices and the environment in which they operate. One area that has received much attention lately is the use of (human) biological signals as the basis for biometric authentication, identification and the generation of cryptographic keys. More concretely, in this dissertation we use the Electrocardiogram (ECG) to extract some fiducial points which are later used on crytographic protocols. The fiducial points are used to describe the points of interest which can be extracted from biological signals. Some examples of fiducials points of the ECG are P-wave, QRS complex,T-wave, R peaks or the RR-time-interval. In particular, we focus on the time difference between two consecutive heartbeats (R-peaks). These time intervals are referred to as Inter-Pulse Intervals (IPIs) and have been proven to contain entropy after applying some signal processing algorithms. This process is known as quantization algorithm. Theentropy that the heart signal has makes the ECG values an ideal candidate to generate tokens to be used on security protocols. Most of the proposed solutions in the literature rely on some questionable assumptions. For instance, it is commonly assumed that it possible to generate the same cryptographic token in at least two different devices that are sensing the same signal using the IPI of each cardiac signal without applying any synchronization algorithm; authors typically only measure the entropy of the LSB to determine whether the generated cryptographic values are random or not; authors usually pick the four LSBs assuming they are the best ones to create the best cryptographic tokens; the datasets used in these works are rather small and, therefore, possibly not significant enough, or; in general it is impossible to reproduce the experiments carried out by other researchers because the source code of such experiments is not usually available. In this Thesis, we overcome these weaknesses trying to systematically address most of the open research questions. That is why, in all the experiments carried out during this research we used a public database called PhysioNet which is available on Internet and stores a huge heart database named PhysioBank. This repository is constantly being up dated by medical researchers who share the sensitive information about patients and it also offers an open source software named PhysioToolkit which can be used to read and display these signals. All datasets we used contain ECG records obtained from a variety of real subjects with different heart-related pathologies as well as healthy people. The first chapter of this dissertation (Chapter 1) is entirely dedicated to present the research questions, introduce the main concepts used all along this document as well as settle down some medical and cryptographic definitions. Finally, the objectives that this dissertation tackles down are described together with the main motivations for this Thesis. In Chapter 2 we report the results of a large-scale statistical study to determine if heart signal is a good source of entropy. For this, we analyze 19 public datasets of heart signals from the Physionet repository, spanning electrocardiograms from multiple subjects sampled at different frequencies and lengths. We then apply both ENT and NIST STS standard battery of randomness tests to the extracted IPIs. The results we obtain through the analysis, clearly show that a short burst of bits derived from an ECG record may seem random, but large files derived from long ECG records should not be used for security purposes. In Chapter3, we carry out an análisis to check whether it is reasonable or not the assumption that two different sensors can generate the same cryptographic token. We systematically check if two sensors can agree on the same token without sharing any type of information. Similarly to other proposals, we include ECC algorithms like BCH to the token generation. We conclude that a fuzzy extractor (or another error correction technique) is not enough to correct the synchronization errors between the IPI values derived from two ECG signals captured via two sensors placed on different positions. We demonstrate that a pre-processing of the heart signal must be performed before the fuzzy extractor is applied. Going one step forward and, in order to generate the same token on different sensors, we propose a synchronization algorithm. To do so, we include a runtimemonitoralgorithm. Afterapplyingourproposedsolution,werun again the experiments with 19 public databases from the PhysioNet repository. The only constraint to pick those databases was that they need at least two measurements of heart signals (ECG1 and ECG2). As a conclusion, running the experiments, the same token can be dexix rived on different sensors in most of the tested databases if and only if a pre-processing of the heart signal is performed before extracting the tokens. In Chapter 4, we analyze the entropy of the tokens extracted from a heart signal according to the NISTSTS recommendation (i.e.,SP80090B Recommendation for the Entropy Sources Used for Random Bit Generation). We downloaded 19 databases from the Physionet public repository and analyze, in terms of min-entropy, more than 160,000 files. Finally, we propose other combinations for extracting tokens by taking 2, 3, 4 and 5 bits different than the usual four LSBs. Also, we demonstrate that the four LSB are not the best bits to be used in cryptographic applications. We offer other alternative combinations for two (e.g., 87), three (e.g., 638), four (e.g., 2638) and five (e.g., 23758) bits which are, in general, much better than taking the four LSBs from the entropy point of view. Finally, the last Chapter of this dissertation (Chapter 5) summarizes the main conclusions arisen from this PhD Thesis and introduces some open questions.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Arturo Ribagorda Garnacho.- Secretario: Jorge Blasco Alis.- Vocal: Jesús García López de la Call

Synoptic analysis techniques for intrusion detection in wireless networks

Current system administrators are missing intrusion alerts hidden by large numbers of false positives. Rather than accumulation more data to identify true alerts, we propose an intrusion detection tool that e?ectively uses select data to provide a picture of ?network health?. Our hypothesis is that by utilizing the data available at both the node and cooperative network levels we can create a synoptic picture of the network providing indications of many intrusions or other network issues. Our major contribution is to provide a revolutionary way to analyze node and network data for patterns, dependence, and e?ects that indicate network issues. We collect node and network data, combine and manipulate it, and tease out information about the state of the network. We present a method based on utilizing the number of packets sent, number of packets received, node reliability, route reliability, and entropy to develop a synoptic picture of the network health in the presence of a sinkhole and a HELLO Flood attacker. This method conserves network throughput and node energy by requiring no additional control messages to be sent between the nodes unless an attacker is suspected. We intend to show that, although the concept of an intrusion detection system is not revolutionary, the method in which we analyze the data for clues about network intrusion and performance is highly innovative