419 research outputs found
Ecg biometrics using deep learning and relative score threshold classification
PD/BDE/130216/2017The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation.publishersversionpublishe
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
Learning Biosignals with Deep Learning
The healthcare system, which is ubiquitously recognized as one of the most influential
system in society, is facing new challenges since the start of the decade.The myriad of
physiological data generated by individuals, namely in the healthcare system, is generating
a burden on physicians, losing effectiveness on the collection of patient data. Information
systems and, in particular, novel deep learning (DL) algorithms have been prompting a
way to take this problem.
This thesis has the aim to have an impact in biosignal research and industry by
presenting DL solutions that could empower this field. For this purpose an extensive study
of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive
Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed.
Different architecture configurations were explored for signal processing and decision
making and were implemented in three different scenarios: (1) Biosignal learning and
synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram
(ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate
autonomously three types of biosignals with a high degree of confidence. As for (2) three
CNN-based architectures, and a RNN-based architecture (same used in (1)) were used
for both biometric identification, reaching values above 90% for electrode-base datasets
(Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric
authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH
and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal
and detection of its deviation was made and tested in two different scenarios: presence of
noise using autoencoder and fully-connected network (reaching 99% accuracy for binary
classification and 71% for multi-class), and; arrhythmia events by including a RNN to the
previous architecture (57% accuracy and 61% sensitivity).
In sum, these systems are shown to be capable of producing novel results. The incorporation
of several AI systems into one could provide to be the next generation of
preventive medicine, as the machines have access to different physiological and anatomical
states, it could produce more informed solutions for the issues that one may face in the
future increasing the performance of autonomous preventing systems that could be used
in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context
as explainable AI could trigger this perception and link the inner states of a network with
the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes
da sociedade, enfrenta novos desafios desde o Ãnicio da década. A mirÃade de dados fisiológicos
gerados por indÃviduos, nomeadamente no sistema de saúde, está a gerar um fardo
para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de
informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda
(DL) têm sido usados na procura de uma solução para este problema.
Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais,
apresentando soluções de DL que poderiam melhorar esta área de investigação. Para
esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais
convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para
o estudo de biosinais.
Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de
sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e sÃntese de
biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema
de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz
de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança.
Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN
(a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores
acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT
-BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo
taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para
CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção
do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruÃdo
usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão
na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN
na arquitetura anterior (57 % de precisão e 61 % de sensibilidade).
Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir
resultados inovadores. A incorporação de vários sistemas de inteligência artificial em
um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os
algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir
soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida
quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes
sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no
contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os
estados internos de uma rede à s caracterÃsticas biológicas
A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments
The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.
Embedded system for individual recognition based on ECG biometrics
Biometric recognition is emerging has an alternative solution for applications where the privacy of the information is crucial.
This paper presents an embedded biometric recognition system based on the Electrocardiographic signals (ECG) for individual
identification and authentication. The proposed system implements a real-time state-of-the-art recognition algorithm, which extracts
information from the frequency domain. The system is based on a ARM Cortex 4. Preliminary results show that embedded
platforms are a promising path for the implementation of ECG-based applications in real-world scenario
Advanced Biometrics with Deep Learning
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others
Feasibility of Single-Arm Single-Lead ECG Biometrics
Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 201
Towards a continuous biometric system based on ECG signals acquired on the steering wheel
Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models – Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.info:eu-repo/semantics/publishedVersio
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