Individual identification via electrocardiogram analysis

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations

Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

Sistema de reconhecimento biométrico baseado no electrocardiograma

Este projecto pretende criar uma plataforma do tipo framework, para desenvolvimento de software que permita a implementação de sistemas biométricos de identificação e autenticação pessoal, usando sinais electrofisiológicos. O sinal electrocardiograma (ECG) é uma característica biométrica em ascensão, existindo fortes indícios de que contém informação suficiente para discriminar um indivíduo de um conjunto vasto de população. Usa-se a framework desenvolvida para criar aplicações que permitam avaliar o desempenho de várias abordagens do estado da arte do reconhecimento biométrico, baseadas no ECG. A arquitectura típica destes sistemas biométricos inclui blocos de aquisição, préprocessamento, extracção de características e classificação de sinais ECG, utilizando tipicamente duas abordagens distintas. Uma das abordagens (fiducial) assenta em pormenores dos diferentes segmentos da forma de onda do sinal ECG, enquanto que a outra abordagem (nonfiducial) tem a vantagem de não depender criticamente desses pormenores. Neste projecto ainda será explorada uma nova variante numa abordagem (non-fiducial) baseada em compressão de dados. Finalmente, pretende-se ainda estudar o desempenho destas abordagens em sinais ECG adquiridos nas mãos, o que constitui um desafio, dado não existirem actualmente estudos sistemáticos usando este tipo de sinais

Reconhecimento de padrões baseado em compressão: um exemplo de biometria utilizando ECG

The amount of data being collected by sensors and smart devices that people use on their daily lives has been increasing at higher rates than ever before. That enables the possibility of using biomedical signals in several applications, with the aid of pattern recognition algorithms in several applications. In this thesis we investigate the usage of compression based methods to perform classification using one-dimensional signals. In order to test those methods, we use as testbed example, electrocardiographic (ECG) signals and the task biometric identification. First and foremost, we introduce the notion of Kolmogorov complexity and how it relates with compression methods. Then, we explain how can these methods be useful for pattern recognition, by exploring different compression-based measures, namely, the Normalized Relative Compression, a measure based on the relative similarity between strings. For this purpose, we present finite-context models and explain the theory behind a generalized version of those models, called the extended-alphabet finite-context models, a novel contribution. Since the testbed application for the methods presented in the thesis is based on ECG signals, we explain what constitutes such a signal and the methods that should be used before data compresison can be applied to them, such as filtering and quantization. Finally, we explore the application of biometric identification using the ECG signal into more depth, making some tests regarding the acquisition of signals and benchmark different proposals based on compresison methods, namely, non-fiducial ones. We also highlight the advantages of such an alternative approach to machine learning methods, namely, low computational costs and not requiring any kind of feature extraction, making this approach easily transferable into different applications and signals.A quantidade de dados recolhidos por sensores e dispositivos inteligentes que as pessoas utilizam no seu dia a dia tem aumentado a taxas mais elevadas do que nunca. Isso possibilita a utilização de sinais biomédicos em diversas aplicações práticas, com o auxílio de algoritmos de reconhecimento de padrões. Nesta tese, investigamos o uso de métodos baseados em compressão para realizar classificação de sinais unidimensionais. Para testar esses métodos, utilizamos, como aplicação de exemplo, o problema de identificação biométrica através de sinais eletrocardiográficos (ECG). Em primeiro lugar, introduzimos a noção de complexidade de Kolmogorov e a forma como a mesma se relaciona com os métodos de compressão. De seguida, explicamos como esses métodos são úteis para reconhecimento de padrões, explorando diferentes medidas baseadas em compressão, nomeadamente, a compressão relativa normalizada (NRC), uma medida baseada na similaridade relativa entre strings. Para isso, apresentamos os modelos de contexto finito e explicaremos a teoria por detrás de uma versão generalizada desses modelos, chamados de modelos de contexto finito de alfabeto estendido (xaFCM), uma nova contribuição. Uma vez que a aplicação de exemplo para os métodos apresentados na tese é baseada em sinais de ECG, explicamos também o que constitui tal sinal e os métodos que devem ser utilizados antes que a compressão de dados possa ser aplicada aos mesmos, tais como filtragem e quantização. Por fim, exploramos com maior profundidade a aplicação da identificação biométrica utilizando o sinal de ECG, realizando alguns testes relativos à aquisição de sinais e comparando diferentes propostas baseadas em métodos de compressão, nomeadamente os não fiduciais. Destacamos também as vantagens de tal abordagem, alternativa aos métodos de aprendizagem computacional, nomeadamente, baixo custo computacional bem como não exigir tipo de extração de atributos, tornando esta abordagem mais facilmente transponível para diferentes aplicações e sinais.Programa Doutoral em Informátic

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Bioelectrical User Authentication

There has been tremendous growth of mobile devices, which includes mobile phones, tablets etc. in recent years. The use of mobile phone is more prevalent due to their increasing functionality and capacity. Most of the mobile phones available now are smart phones and better processing capability hence their deployment for processing large volume of information. The information contained in these smart phones need to be protected against unauthorised persons from getting hold of personal data. To verify a legitimate user before accessing the phone information, the user authentication mechanism should be robust enough to meet present security challenge. The present approach for user authentication is cumbersome and fails to consider the human factor. The point of entry mechanism is intrusive which forces users to authenticate always irrespectively of the time interval. The use of biometric is identified as a more reliable method for implementing a transparent and non-intrusive user authentication. Transparent authentication using biometrics provides the opportunity for more convenient and secure authentication over secret-knowledge or token-based approaches. The ability to apply biometrics in a transparent manner improves the authentication security by providing a reliable way for smart phone user authentication. As such, research is required to investigate new modalities that would easily operate within the constraints of a continuous and transparent authentication system. This thesis explores the use of bioelectrical signals and contextual information for non-intrusive approach for authenticating a user of a mobile device. From fusion of bioelectrical signals and context awareness information, three algorithms where created to discriminate subjects with overall Equal Error Rate (EER of 3.4%, 2.04% and 0.27% respectively. Based vii | P a g e on the analysis from the multi-algorithm implementation, a novel architecture is proposed using a multi-algorithm biometric authentication system for authentication a user of a smart phone. The framework is designed to be continuous, transparent with the application of advanced intelligence to further improve the authentication result. With the proposed framework, it removes the inconvenience of password/passphrase etc. memorability, carrying of token or capturing a biometric sample in an intrusive manner. The framework is evaluated through simulation with the application of a voting scheme. The simulation of the voting scheme using majority voting improved to the performance of the combine algorithm (security level 2) to FRR of 22% and FAR of 0%, the Active algorithm (security level 2) to FRR of 14.33% and FAR of 0% while the Non-active algorithm (security level 3) to FRR of 10.33% and FAR of 0%