Abstract
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.

Abstract
Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.

Abstract
This article presents the design and evaluation of an algorithm for urinary bladder segmentation in medical images, from contrastless computed tomography studies of patients suffering from bladder wall tumours. These situations require versatile methods of segmentation, able to adapt to the structural changes the tumours provoke in the bladder wall, reflected as irregularities on the images obtained, creating adversities to the segmentation process. This semi-automatic method uses fuzzy c-means clustering, a Gaussian-curve-based intensity transformation, and active contour models, requiring only the physician's input of a single seed point for each anatomical view, in order to segment the bladder volume in all frames that include it. The performance of the method was evaluated on eight patients of The Cancer Genome Atlas-Urothelial Bladder Carcinoma collection, achieving approximately 79% of successful segmentations for small tumour patients (below 2.0 cm of diameter) and approximately 72% between 2.0 and 2.9 cm. Successful segmentations for small tumour patients presented an average of 3.7 mm Hausdorff distance and 91.0% degree of overlap. The promising performance attained, especially for small tumour patients, revealed a high potential of this method to serve as basis for an effective early-stage bladder wall tumour computer-aided diagnosis system.

Abstract
Ordinal classification is a specific and demanding task, where the aim is not only to increase accuracy, but to also capture the natural order between the classes, and penalize incorrect predictions by how much they deviate from this ranking. If an ordinal classifier must be able to comply with all these requirements, a suitable ordinal metric must be able to accurately measure its degree of compliance. However, the current metrics are unable to completely capture these considerations when assessing classification performance. Moreover, most suffer from sensitivity to imbalanced classes, very common in ordinal classification. In this paper, we propose two variants of a novel performance index that accounts for both accuracy and ranking in the performance assessment of ordinal classification, and is robust against imbalanced classes. © 2018 IEEE.

Abstract
The performance of biometric systems is known to decay over time, eventually rendering them ineffective. Focused on ECG-based biometrics, this work aims to study the permanence of these signals for biometric identification in state-of-the-art methods, and measure the effect of template update on their long-term performance. Ensuring realistic testing settings, four literature methods based on autocorrelation, autoencoders, and discrete wavelet and cosine transforms, were evaluated with and without template update, using Holter signals from THEW’s E-HOL 24 h database. The results reveal ECG signals are unreliable for long-term biometric applications, and template update techniques offer considerable improvements over the state-of-the-art results. Nevertheless, further efforts are required to ensure long-term effectiveness in real applications. © 2019, Springer Nature Switzerland AG.

Abstract