ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

Electrocardiograms (ECGs) have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits, such as difficulty to counterfeit, liveness detection, and ubiquity. Also, with the success of Deep Learning technologies, ECG biometric recognition has received increasing interest in recent years. However, it is not easy to evaluate the improvements of novel ECG proposed methods, mainly due to the lack of public data and standard experimental protocols. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. Both verification and identification tasks are investigated, as well as single- and multi-session scenarios. Finally, we also perform single- and multi-lead ECG experiments, considering traditional scenarios using electrodes in the chest and limbs and current user-friendly wearable devices. In addition, we present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database and able to operate successfully across various scenarios and multiple databases. We introduce our proposed feature extractor, trained with multiple sinus-rhythm heartbeats belonging to 55,967 subjects, and provide a general public benchmark evaluation with detailed experimental protocol. We evaluate the system performance over four different databases: i) our in-house database, ii) PTB, iii) ECG-ID, and iv) CYBHi. With the widely used PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in verification, and accuracies of 100% and 96.46% in identification, respectively in single- and multi-session analysis. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field.Comment: 11 pages, 4 figure

Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study

Objective: Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures. Methods: We designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics. Results: After balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09). Conclusion: This study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stabilityEuropean Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No860813 – TReSPAsS-ETNTRESPASS-ET

Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based modelsGJO, AS-G, LJJ-B received a research grant from the Carlos III Institute of Health under the health Strategy action 2020-2022 with reference PI20/00792. Tis study is also supported partially by projects TRESPASS-ETN (H2020-MSCAITN-2019-860813), PRIMA (H2020-MSCA-ITN-2019-860315), IDEA-FAST (IMI2-2018-15-853981), BIBECA (RTI2018-101248-B-I00 MINECO/FEDER

Caregiver burden and its related factors in advanced Parkinson’s disease: data from the PREDICT study

Introduction: Caring for a person with Parkinson’s disease (PD) is associated with an increased risk of psychiatric morbidity and persistent distress. The objective of this study was to describe the burden and the related factors of caregivers of advanced PD (APD) patients either treated with continuous dopaminergic delivery systems or standard therapy. Methods: This cross-sectional, epidemiologic study conducted in 13 Italian sites enrolled PD patients treated with continuous dopaminergic delivering systems [either levodopa/carbidopa intestinal gel (LCIG) infusion or continuous subcutaneous apomorphine infusion (CSAI)] or continuation of standard of care (SOC) with a caregiver. Patient quality of life (QoL) and caregiver burden were assessed using the Parkinson’s Disease Questionnaire (PDQ-8) and Zarit Burden Inventory (ZBI), respectively. Results: 126 patients (mean age 69.3 ± 8 years) and their caregivers (mean age 57.9 ± 12.9) were enrolled. Most caregivers were spouses. Fifty-three patients were treated with LCIG, 19 with CSAI, and 54 with SOC. Mean ZBI scores were 29.6 ± 14.4 for LCIG, 35.8 ± 20.2 for CSAI, and 31.4 ± 16.0 for SOC. Caregivers of LCIG, CSAI, and SOC patients showed no burden or mild/moderate burden in 74, 53, and 63% of the cases, respectively. Mean PDQ-8 scores were 11.25 ± 5.67, 11.26 ± 5.55, and 14.22 ± 6.51 in LCIG, CSAI, and SOC patients. Neurologists considered patients “very much or much improved” in 89, 58, and 13% of the LCIG, CSAI, and SOC groups using the Clinical Global Impression–Global Improvement Scale. Predictors significantly associated with caregiver burden were patients and caregivers’ judgment of QoL and caregivers’ need to change work. Conclusions: Caregiver burden showed a tendency to be lower when patients are treated with LCIG than with CSAI or SOC

FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data

Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.Comment: 10 pages, 1 figure, WACV 2024 Workshop

GIM3D plus: A labeled 3D dataset to design data-driven solutions for dressed humans

Segmentation and classification of clothes in real 3D data are particularly challenging due to the extreme variation of their shapes, even among the same cloth category, induced by the underlying human subject. Several data-driven methods try to cope with this problem. Still, they must face the lack of available data to generalize to various real-world instances. For this reason, we present GIM3D plus (Garments In Motion 3D plus), a synthetic dataset of clothed 3D human characters in different poses. A physical simulation of clothes generates the over 5000 3D models in this dataset with different fabrics, sizes, and tightness, using animated human avatars representing different subjects in diverse poses. Our dataset comprises single meshes created to simulate 3D scans, with labels for the separate clothes and the visible body parts. We also provide an evaluation of the use of GIM3D plus as a training set on garment segmentation and classification tasks using state-of-the-art data-driven methods for both meshes and point clouds

A functional skeleton transfer

The animation community has spent significant effort trying to ease rigging procedures. This is necessitated because the increasing availability of 3D data makes manual rigging infeasible. However, object animations involve understanding elaborate geometry and dynamics, and such knowledge is hard to infuse even with modern data-driven techniques. Automatic rigging methods do not provide adequate control and cannot generalize in the presence of unseen artifacts. As an alternative, one can design a system for one shape and then transfer it to other objects. In previous work, this has been implemented by solving the dense point-to-point correspondence problem. Such an approach requires a significant amount of supervision, often placing hundreds of landmarks by hand. This paper proposes a functional approach for skeleton transfer that uses limited information and does not require a complete match between the geometries. To do so, we suggest a novel representation for the skeleton properties, namely the functional regressor, which is compact and invariant to different discretizations and poses. We consider our functional regressor a new operator to adopt in intrinsic geometry pipelines for encoding the pose information, paving the way for several new applications. We numerically stress our method on a large set of different shapes and object classes, providing qualitative and numerical evaluations of precision and computational efficiency. Finally, we show a preliminar transfer of the complete rigging scheme, introducing a promising direction for future explorations

Intrinsic/extrinsic embedding for functional remeshing of 3D shapes

3D acquisition pipeline delivers 3D digital models accurately representing real-world objects, improving the geometric accuracy and realism of virtual reconstructions. However, even after intensive clean-up, the captured models fall short of many of the requirements imposed by the downstream application, such as video-games, virtual reality, digital movies, etc. Often, the captured 3D model can only serve as a starting point for a cascade of subsequent phases, by either digital artists or geometry processing algorithms, such as a complete remeshing (or retopology), surface parameterization, and skinning for animation. In contrast, we propose a novel remeshing-by-matching approach, where we automatically combine the accurate 3D geometry of the captured model with the tessellation of a target pre-existing template which already satisfies all the professional requirements. At the core of this process, there is a matching strategy based on the functional mapping framework. To this end, we introduce a new set of basis functions designed for this context: termed Coordinates Manifold Harmonics (CMH). We evaluate this strategy (quantitatively and qualitatively) over models of different classes, obtaining a favourable comparison with existing methods

Magnetic properties of frustrated two-dimensional S=1/2 antiferromagnets on a square lattice

An overview on the basic magnetic properties of vanadates which represent prototypes of frustrated two-dimensional S=1/2 antiferromagnets on a square lattice is presented. It will be shown how information on the ground state sublattice magnetization on the static uniform susceptibility and on the frustration driven lattice distortions can be achieved by means of NMR spectroscopy and magnetization measurements. The low-energy spin excitations investigated by means of NMR and muSR relaxation measurements will be analyzed and the anomalous very-low-frequency dynamics originating from the degeneracy of the ground state discussed. Finally the effects of an hydrostatic pressure on the degree of frustration of the vanadates will be addressed