Measuring vital signs can reveal the state of body functioning and help to detect a health problem. In the state-of-the-art, numerous methods and devices are available for measuring vital signs. However, with the advent of artificial intelligence, new methods have been proposed that employ this technology. This paper aims to highlight the recent methods and devices based on artificial intelligence and novel techniques for measuring vital signs and processing algorithms. We analyzed 122 papers and classified them into six categories: (i) body temperature, (ii) blood oxygen saturation, (iii) heart rate monitoring, (iv) respiratory rate, (v) blood pressure, and (vi) simultaneous vital sign measurements. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology was used for the search and selection of scientific papers. The criteria to guide the scope of the review were defined with the Population, Intervention, Comparison, Outcomes, and Context (PICOC) methodology. The review highlighted significant efforts to develop and implement contactless, non-invasive devices for continuous monitoring outside clinical environments. It also revealed clear pathways for integrating AI at different stages of measurement and signal processing methods.

This study presents FLIGENT-Net, a multi-domain signal representation framework for gastric histopathological image classification that treats tissue images as complex two-dimensional signals whose diagnostic content is distributed across spatial, spectral, relational, and contextual domains. Using the GasHisSDB dataset, FLIGENT-Net investigates spatial representations extracted through a convolutional neural network (CNN), frequency-domain information obtained using the Fast Fourier Transform (FFT), graph-based relational modeling through a Graph Convolutional Network (GCN) acting as a graph-signal-processing operator, and global contextual modeling through a Vision Transformer (ViT). Rather than evaluating the framework solely according to classification accuracy, a systematic characterization was performed using individual and combined configurations, computational cost, training dynamics, interpretability, Pearson and Spearman correlation of importance maps, and Pixel Deletion analysis. FLIGENT-Net achieved 96.98% Accuracy, 95.45% Precision, 96.95% Recall, 96.20% F1-Score, and 0.9959 AUC-ROC. Notably, the simpler CNN+GCN configuration achieved a higher Accuracy of 97.76%, while CNN+GCN+FFT achieved 97.75%; this outcome shows that combining all four representation domains does not monotonically improve classification accuracy, reinforcing the characterization-oriented contribution of this study over a purely performance-driven one. The importance-map analysis revealed distinct patterns across representations, with low global Pearson and Spearman correlations. Pixel Deletion yielded a positive ∆AUC of 0.1450 for FLIGENT-Net. Overall, the results characterize distinct classification, computational, interpretability, and faithfulness profiles across multi-domain signal representations, offering a computational methods case study in spectral and graph-based image-signal analysis for biomedical applications.

This study characterizes spatial, spectral, relational, and contextual representations for binary gastric histopathological image classification using FLIGENT-Net. The framework combines an ImageNet-pretrained convolutional backbone, patch-level fast Fourier transform (FFT) descriptors, graph convolution over a cosine-similarity graph, and a Transformer Encoder. Experiments use the 160-by-160 GasHisSDB subset, a persisted stratified 60%/20%/20% partition, and one training run per configuration with seed 42. In the nine completed configurations of the revised evaluation, FLIGENT-Net achieved 97.31% accuracy, 96.57% precision, 96.61% recall, 96.59% F1-score for the Abnormal class, and 0.9964 AUC-ROC. An additional native-grid FFT control yielded 97.43% accuracy and 96.74% F1 under an alternative spectral protocol that preserved the 160-by-160 sampling grid while changing the local FFT window sizes and spectral discretization. The conventional CNN baseline (ResNet18) obtained the highest accuracy and F1-score among these completed runs, at 98.51% and 98.13%, respectively. These results do not establish a predictive advantage for the complete multi-domain architecture; rather, they characterize how the added representation domains affect predictive behavior, graph topology, computational cost, and attribution response under a common protocol. The study contributes a reproducible integration of the four representation domains and a controlled empirical characterization of their combinations. Transformer attention visualizations describe information allocation among contextualized nodes, while a common input-gradient deletion protocol examines attribution behavior. Together, these analyses identify the behavior and limitations of the implemented fusion strategy and provide a basis for its further development. Performance estimates are conditional on the available patch-level split and should not be interpreted as patient-, slide-, or source-image-independent generalization; run-to-run variability is also not estimated because one fixed seed is used per configuration.