Browsing by Author "Carreiras, Carlos"
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- Dominant set approach to ECG biometricsPublication . Lourenço, André Ribeiro; Rota Bulò, Samuel; Carreiras, Carlos; Silva, Hugo; Fred, Ana L. N.; Pelillo, MarcelloElectrocardiographic (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.
- ECG Analysis Using Consensus ClusteringPublication . Lourenço, André Ribeiro; Carreiras, Carlos; Bulò, Samuel Rocha; Fred, Ana Luísa NobreBiosignals 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 using a dissimilarity space representationPublication . Ferreira, Rui; Fred, Ana; Lourenço, André Ribeiro; Carreiras, Carlos; Marques, FranciscoElectrocardiogram (ECG) biometrics are a relatively recent trend in biometric recognition, with at least 13 years of development in peer-reviewed literature. Most of the proposed biometric techniques perform classifi-cation on features extracted from either heartbeats or from ECG based transformed signals. The best representation is yet to be decided. This paper studies an alternative representation, a dissimilarity space, based on the pairwise dissimilarity between templates and subjects' signals. Additionally, this representation can make use of ECG signals sourced from multiple leads. Configurations of three leads will be tested and contrasted with single-lead experiments. Using the same k-NN classifier the results proved superior to those obtained through a similar algorithm which does not employ a dissimilarity representation. The best Authentication EER went as low as 1:53% for a database employing 503 subjects. However, the employment of extra leads did not prove itself advantageous.
- ECG Biometrics: A Template Selection ApproachPublication . Lourenço, André Ribeiro; Carreiras, Carlos; Silva, Hugo Plácido da; Fred, Ana Luísa NobreElectrocardiography (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 signals for biometric applications: are we there yet?Publication . Carreiras, Carlos; Lourenço, André; Fred, Ana Luísa Nobre; Ferreira, RuiThe 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.
- Off-the-person electrocardiography: performance assessment and clinical correlationPublication . Silva, Hugo Plácido da; Carreiras, Carlos; Lourenço, André Ribeiro; Fred, Ana; Neves, Rui César das; Ferreira, RuiPrevious work by our group introduced a novel concept and sensor design for “off-the-person” ECG, for which evidence on how it compares against standard clinical-grade equipment has been largely missing. Our objectives with this work are to characterise the off-the-person approach in light of the current ECG systems landscape, and assess how the signals acquired using this simplified setup compare with clinical-grade recordings. Empirical tests have been performed with real-world data collected from a population of 38 control subjects, to analyze the correlation between both approaches. Results show off-the-person data to be correlated with clinical-grade data, demonstrating the viability of this approach to potentially extend preventive medicine practices by enabling the integration of ECG monitoring into multiple dimensions of people’s everyday lives. © 2015, IUPESM and Springer-Verlag Berlin Heidelberg.
- Towards a continuous biometric system based on ECG signals acquired on the steering wheelPublication . Pinto, João Ribeiro; Cardoso, Jaime S.; Lourenço, André; Carreiras, CarlosElectrocardiogram 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.
- A web-based platform for biosignal visualization and annotationPublication . Lourenço, André Ribeiro; Silva, Hugo Plácido da; Carreiras, Carlos; Alves, Ana Priscila; Fred, Ana Luísa NobreWith the advent of wearable sensing and mobile technologies, biosignals have seen an increasingly growing number of application areas, leading to the collection of large volumes of data. One of the difficulties in dealing with these data sets, and in the development of automated machine learning systems which use them as input, is the lack of reliable ground truth information. In this paper we present a new web-based platform for visualization, retrieval and annotation of biosignals by non-technical users, aimed at improving the process of ground truth collection for biomedical applications. Moreover, a novel extendable and scalable data representation model and persistency framework is presented. The results of the experimental evaluation with possible users has further confirmed the potential of the presented framework.