Epilepsy, a chronic neurological disorder marked by recurrent and unpredictable seizures, poses significant risks of injury and compromises patient quality of life. The accurate forecasting of seizures is paramount for enabling timely interventions and improving safety. Since the 1970s, research has increasingly focused on analyzing bioelectrical signals for this purpose. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a powerful tool for seizure prediction. This review, conducted by PRISMA guidelines, analyzes studies from 2020 to August 2025. It explores the evolution from traditional ML classifiers toward advanced DL architecture, including convolutional and recurrent neural networks and transformer-based frameworks, applied to bioelectrical signals. While these approaches show promising performance, significant challenges persist in patient generalization, standardized evaluation, and clinical validation. This review synthesizes current advancements, provides a critical analysis of methodological limitations, and outlines future directions for developing robust, clinically relevant seizure prediction systems to enhance patient autonomy and outcomes.

Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection using vibration signal analysis. The approach employs graphical empirical mode decomposition (GEMD) to decompose vibration signals into their intrinsic mode functions, extracting relevant structural features. These features are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN) to automatically differentiate between a healthy structure and one affected by corrosion. To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. The proposed approach is validated using a 3D nine-bay truss-type bridge model encountered in the Vibrations Laboratory at the Autonomous University of Querétaro, Mexico. The evaluation considers three different corrosion levels: (1) incipient, (2) moderate, and (3) severe, along with a healthy condition. The combination of GEMD and CNN provides a highly accurate corrosion detection framework that achieves 100% classification accuracy while remaining effective regardless of the damage location and severity, making it a reliable tool for early-stage corrosion assessment that enables timely maintenance and enhances structural health monitoring to improve the long life and safety of civil structures.

Early prediction of epileptic seizures remains an active research area in scalp electroencephalography (EEG) analysis. Current methods focused on this topic often rely on handcrafted features that insufficiently capture the multiscale nonlinear dynamics of preictal activity, process EEG channels independently without modeling brain connectivity, or demand computationally expensive manual hyperparameter tuning. To address these limitations, this study presents a framework integrating multifractal analysis (MFA), graph neural networks (GNNs), and a differential evolution algorithm (DEA) to classify non-overlapping one-minute EEG segments as preictal (within 60 min before onset) or reference (interictal) states. Five complementary MFA techniques extract nonlinear descriptors from each segment, which serve as node attributes in a graph of the 21 EEG channels. Unlike conventional approaches, this graph explicitly encodes neuroanatomical proximity to capture spatial brain dynamics and inter-channel topological dependencies. This graph-based representation allows the GNN to learn from the relational structure of the brain network, capturing spatiotemporal interactions critical for early prediction, while the DEA systematically optimizes the GNN architecture, eliminating subjective manual tuning. Under a segment-level validation protocol on the CHB-MIT database, the framework achieved 96.38% accuracy, 94.36% sensitivity, 98.41% specificity, and an AUC-ROC of 99.37%. These results demonstrate the effectiveness of combining multifractal descriptors, graph-based processing, and evolutionary optimization for segment-level discrimination. Nevertheless, patient-wise validation remains essential for clinical translation, and this work is consequently presented as a proof-of-concept demonstration.

Cardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) signal before onset, which is generally undetectable by the patient. Hence, timely detection of these changes in ECG signals could help develop a tool to anticipate an SCD event and respond appropriately in patient care. In this sense, this work proposes a novel computational methodology that combines the maximal overlap discrete wavelet packet transform (MODWPT) with stacked autoencoders (SAEs) to discover suitable features in ECG signals and associate them with SCD prediction. The proposed method efficiently predicts an SCD event with an accuracy of 98.94% up to 30 min before the onset, making it a reliable tool for early detection while providing sufficient time for medical intervention and increasing the chances of preventing fatal outcomes, demonstrating the potential of integrating signal processing and deep learning techniques within computational biology to address life-critical health problems.