Embedded platform for ECG biometric recognition

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Unveiling the Biometric Potential of Finger-Based ECG Signals

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

Coding mode decision algorithm for binary descriptor coding

In visual sensor networks, local feature descriptors can be computed at the sensing nodes, which work collaboratively on the data obtained to make an efficient visual analysis. In fact, with a minimal amount of computational effort, the detection and extraction of local features, such as binary descriptors, can provide a reliable and compact image representation. In this paper, it is proposed to extract and code binary descriptors to meet the energy and bandwidth constraints at each sensing node. The major contribution is a binary descriptor coding technique that exploits the correlation using two different coding modes: Intra, which exploits the correlation between the elements that compose a descriptor; and Inter, which exploits the correlation between descriptors of the same image. The experimental results show bitrate savings up to 35% without any impact in the performance efficiency of the image retrieval task. © 2014 EURASIP.

ECG signals for biometric applications: are we there yet?

The potential of the electrocardiographic (ECG) signal as a biometric trait has been ascertained in the literature over the past decade. The inherent characteristics of the ECG make it an interesting biometric modality, given its universality, intrinsic aliveness detection, continuous availability, and inbuilt hidden nature. These properties enable the development of novel applications, where non-intrusive and continuous authentication are critical factors. Examples include, among others, electronic trading platforms, the gaming industry, and the auto industry, in particular for car sharing programs and fleet management solutions. However, there are still some challenges to overcome in order to make the ECG a widely accepted biometric. In particular, the questions of uniqueness (inter-subject variability) and permanence over time (intra-subject variability) are still largely unanswered. In this paper we focus on the uniqueness question, presenting a preliminary study of our biometric recognition system, testing it on a database encompassing 618 subjects. We also performed tests with subsets of this population. The results reinforce that the ECG is a viable trait for biometrics, having obtained an Equal Error Rate of 9.01% and an Error of Identification of 15.64% for the entire test population.

Biometric recognition system using low bandwidth ECG signals

Biometric recognition has recently emerged as part of applications where the privacy of the information is crucial, as in the health care field. This paper presents a biometric recognition system based on the Electrocardiographic signal (ECG). The proposed system is based on a state-of-the-art recognition method which extracts information from the frequency domain. In this paper we propose a new method to increase the spectral resolution of low bandwidth ECG signals due to the limited bandwidth of the acquisition sensor. Preliminary results show that the proposed scheme reveals a higher identification rate and lower equal error rate when compared to previous approaches.

Classificação semântica de imagens com k-NN e RN

Um dos maiores desafios tecnológicos no presente é o de se conseguir gerar e manter, de uma maneira eficiente e consistente, uma base de dados de objectos multimédia, em particular, de imagens. A necessidade de desenvolver métodos de pesquisa automáticos baseados no conteúdo semântico das imagens tornou-se de máxima importância. MPEG-7 é um standard que descreve o contudo dos dados multimédia que suportam estes requisitos operacionais. Adiciona um conjunto de descritores audiovisuais de baixo nível. O histograma é a característica mais utilizada para representar as características globais de uma imagem. Neste trabalho é usado o “Edge Histogram Descriptor” (EHD), que resulta numa representação de baixo nível que permite a computação da similaridade entre imagens. Neste trabalho, é obtida uma caracterização semântica da imagem baseada neste descritor usando dois métodos da classificação: o algoritmo k Nearest Neighbors (k-NN) e uma Rede Neuronal (RN) de retro propagação. No algoritmo k-NN é usada a distância Euclidiana entre os descritores de duas imagens para calcular a similaridade entre imagens diferentes. A RN requer um processo de aprendizagem prévia, que inclui responder correctamente às amostras do treino e às amostras de teste. No fim deste trabalho, será apresentado um estudo sobre os resultados dos dois métodos da classificação.

Aeródromos do continente: metodologia de análise de descritores para o seu planeamento

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de especialização de Vias de Comunicação e Transportes

Embedded System for ECG Biometrics

Comunication in Internationa Conference with Peer Review First International Congress on Cardiovasular Technologies - CARDIOTECHNIX, Vilamoura, Portugal, 2013

Desenvolvimento e mensuração do diâmetro transversal do cerebelo fetal por ressonância magnética

A ressonância magnética fetal é um método eficaz na avaliação pré-natal da morfologia normal do cérebro e no diagnóstico de patologias do sistema nervoso central, sendo um importante complemento clínico à ecografia. O cerebelo é uma das estruturas menos afetadas em casos de restrição de crescimento fetal, tornando-se um bom indicador na avaliação do desenvolvimento fetal e da idade gestacional. Deste modo, a avaliação biométrica fetal é fundamental no diagnóstico pré-natal do desenvolvimento cerebral. Objetivo – Avaliação do diâmetro transversal do cerebelo (estrutura anatómica de referência do sistema nervoso central) do feto e posterior comparação com um estudo internacional reconhecido nesta matéria. Material e métodos – A amostra foi constituída por 84 gestantes que realizaram ressonância magnética fetal numa clínica de imagiologia médica da região Centro. A medição considerada para a avaliação do desenvolvimento fetal foi o diâmetro transversal do cerebelo. Resultados – Os resultados obtidos para o diâmetro transversal do cerebelo por ressonância magnética fetal vieram a demonstrar a ausência de diferenças médias estatisticamente significativas (p>0,05) em função do número de semanas de gestação, face aos valores teóricos aferidos no estudo de Garel. Conclusão – Com base nos resultados obtidos, para o diâmetro transversal do cerebelo podemos concluir que o parâmetro analisado é coerente com a publicação de Catherine Garel – Le développement du cerveau foetal atlas IRM et biometrie.

Dominant set approach to ECG biometrics

Electrocardiographic (ECG) signals are emerging as a recent trend in the field of biometrics. In this paper, we propose a novel ECG biometric system that combines clustering and classification methodologies. Our approach is based on dominant-set clustering, and provides a framework for outlier removal and template selection. It enhances the typical workflows, by making them better suited to new ECG acquisition paradigms that use fingers or hand palms, which lead to signals with lower signal to noise ratio, and more prone to noise artifacts. Preliminary results show the potential of the approach, helping to further validate the highly usable setups and ECG signals as a complementary biometric modality.

Indicadores prognósticos na hipertensão pulmonar

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular - Ramo de especialização: Intervenção Cardiovascular

Sistema de reconhecimento de impressões digitais baseado em FPGA

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia de Eletrónica e Telecomunicações

ECG Analysis Using Consensus Clustering

Biosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. © 2014 EURASIP.

ECG Biometrics: A Template Selection Approach

Electrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.

ECG-based biometrics: A real time classification approach

Behavioral biometrics is one of the areas with growing interest within the biosignal research community. A recent trend in the field is ECG-based biometrics, where electrocardiographic (ECG) signals are used as input to the biometric system. Previous work has shown this to be a promising trait, with the potential to serve as a good complement to other existing, and already more established modalities, due to its intrinsic characteristics. In this paper, we propose a system for ECG biometrics centered on signals acquired at the subject's hand. Our work is based on a previously developed custom, non-intrusive sensing apparatus for data acquisition at the hands, and involved the pre-processing of the ECG signals, and evaluation of two classification approaches targeted at real-time or near real-time applications. Preliminary results show that this system leads to competitive results both for authentication and identification, and further validate the potential of ECG signals as a complementary modality in the toolbox of the biometric system designer.