The International League Against Epilepsy (ILAE), working via a multidisciplinary task force, has issued a position paper with updated seizure classification guidelines (2025) replacing its previous guidance (2017), establishing a revised de-facto standard for clinical assessment and classification of epileptic seizures. The 2025 model simplifies the system, making it easier to adopt in different clinical settings while also clarifying the taxonomic rules, the definition of consciousness, and the application of phenomena-based descriptors. As a result, clinical practitioners, technologists, and medical software developers also face the challenge of adopting the new standard in their applications and workflows. To aid in the realization of this update in medical device software, this article presents a proposed numerical structure for ILAE 2025 edition of the seizure classification system as a definition of interacting bitfields, establishing an open reference implementation to support common storage and interoperability between software environments. Assigning each orthogonal concept to a single bit, we encode the core ontology and expanded descriptor set so that they fit into 32-bit and 128-bit unsigned integers, respectively. TypeScript, known for its expressiveness and practical applicability, is used as the implementation language for these definitions. Examples are provided to help implementers incorporate the proposed specification into their software, as well guidance for mapping from the ILAE 2017 type system to the updated ILAE 2025 type system. The reference specification, with test cases and examples, is maintained in a public code repository and available under a permissive license.
The organization of distributed neural activity remains one of the central challenges in computational neuroscience. Contemporary frameworks, including Integrated Information Theory, Global Workspace Theory, entropy-based approaches, and electromagnetic field theories, have advanced our understanding of neural coordination but employ different mathematical descriptions of large-scale brain dynamics. This study investigates whether wave-function formalism provides a useful mathematical framework for representing evolving neural dynamics measured using electroencephalography (EEG). Rather than introducing a new electrophysiological measure or proposing a new mechanism of consciousness, the framework represents complementary EEG observables within a common state-space description of neural activity. Mathematical inspiration is drawn from Everett’s representation of evolving state spaces, although no claims are made regarding quantum computation or quantum processes in the brain. Electroencephalographic recordings from interictal, preictal, and ictal seizure states (150 EEG segments) were analyzed using complementary measures of signal amplitude, spectral organization, entropy, phase dynamics, cross-frequency coupling, and recurrence quantification. Statistical comparisons employed nonparametric analyses, effect-size estimation, confidence intervals, and post hoc testing. Significant differences were observed across all major electrophysiological domains. Ictal recordings exhibited increased signal amplitude, redistribution of spectral power toward lower frequencies, reduced entropy, altered phase organization, changes in cross-frequency coupling, and increased recurrence structure, indicating coordinated changes in evolving neural dynamics during seizure progression. These findings are interpreted within a mathematical framework that treats established EEG observables as complementary descriptors of a single evolving neural state. The principal contribution of this work is the proposal of a mathematically grounded and experimentally testable representation of evolving neural dynamics. Whether this formalism provides new insight into cognition or conscious experience remains an empirical question for future investigation.
Machine learning is now widely applied to magnetic resonance imaging-based brain tumor analysis, with task-specific performance metrics: the Dice similarity coefficient (DSC) quantifies segmentation overlap, while the area under the curve (AUC) and accuracy measure classification performance. A PRISMA 2020 systematic review was conducted, searching Scopus, Web of Science, PubMed, and IEEE Xplore, and identified 213 eligible studies published between January 2018 and the final search date of March 16, 2026. Segmentation performance was synthesized by random-effects meta-analysis and classification performance was summarized descriptively. Classification, detection, and grading tasks dominated the literature, followed by segmentation; U-Net variants were the most frequent architectures, with transformer-based models growing fastest. The pooled mean DSC was 0.871 (95% CI: 0.852–0.889) with a 95% prediction interval of 0.7880.953, and heterogeneity was extreme (I2 = 99.9%), so the pooled mean should be read as a descriptive summary of the literature rather than a single underlying effect. The descriptive mean AUC across classification studies was 0.960. Methodological gaps were pervasive: only 18.8% of studies incorporated explainability, only 18.8% performed external validation, and none reported robustness metrics. Benchmark accuracy has outpaced clinical evidence: until models are validated on independent cohorts, evaluated for robustness under realistic acquisition shifts, and made interpretable to clinicians, reported performance is unlikely to translate into patient care.
Accurate prediction of mortality in the intensive care unit (ICU) is crucial for informing clinical decision-making and optimizing resource allocation. Although machine learning methodologies have enhanced traditional severity-of-illness scoring systems, they encounter intrinsic limitations in modeling complex feature interactions within electronic health record (EHR) data. In this study, we introduced a hybrid quantum-classical machine learning (HQML) framework for predicting ICU mortality, evaluated using the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.2) database. Our methodology incorporates a classical autoencoder for dimensionality reduction (from 87 features to 16 latent dimensions), a 4-qubit parameterized quantum circuit (PQC) utilizing ZZFeatureMap encoding and RealAmplitudes variational ansatz (L = 3 layers, 48 trainable parameters), and a classical post-processing layer. In a retrospective cohort of 38,624 ICU admissions (12.8% mortality rate), the HQML model achieved an area under the receiver operating characteristic curve (AUROC) of 0.871 (95% CI: 0.859–0.883), outperforming XGBoost (0.858; 95% CI: 0.844–0.872), deep neural network (0.847), random forest (0.838), logistic regression (0.813), and SAPS-II (0.771). The improvement over XGBoost was statistically significant (DeLong p = 0.012) and corroborated by 5-fold cross-validation. The model demonstrated superior calibration (Brier score 0.089 vs. 0.095) and enhanced data efficiency. Comprehensive ablation experiments have elucidated the effects of circuit depth, quantum feature map selection, autoencoder latent dimensionality, class imbalance management, and quantum noise. Noise analysis suggests that gate error rates below 10−3 are necessary to maintain this advantage. These findings substantiate that hybrid quantum-classical architectures can yield incremental but statistically significant improvements in clinical prediction tasks under idealized simulation conditions. As the study is limited to a single-center dataset (MIMIC-IV), external validation on independent cohorts is required before clinical generalizability can be established.
