Publicação
Detecting the Onset of Neurological Critical Events via Structure-Informed AI: Clinical Applications in Epilepsy and Amyotrophic Lateral Sclerosis
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Santos, Augusto José Rabelo Almeida | |
| dc.contributor.author | Santos, Maria Teresa Ferreira | |
| dc.date.accessioned | 2025-11-06T14:12:24Z | |
| dc.date.available | 2025-11-06T14:12:24Z | |
| dc.date.issued | 2025-10-27 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence | pt_PT |
| dc.description.abstract | Neurological disorders such as Epilepsy and Amyotrophic Lateral Sclerosis (ALS) pose profound challenges due to their complex progression and significant impact on patients' quality of life. Accurate and early detection is essential but remains difficult due to the variability and subtlety of physiological signals. While machine learning and deep learning techniques have shown potential in analyzing these signals, their clinical utility is often hindered by poor interpretability, patient-specific variability, and class imbalance. This research addresses a critical gap in the field: the limited application of causal AI to enhance the robustness, generalization, and explainability of models used in neurological crisis detection. To this end, two novel feature engineering strategies are introduced, each designed to capture aspects of physiological data that traditional features often fail to represent. For seizure detection, a oneparameter family of features based on the powers of the covariance matrix is proposed, offering a unique representation that captures latent causal structures governing emergent macroscopic events. These features go beyond correlation and enable earlier and more reliable detection of seizures. Indeed, we demonstrate that these structure-informed features tend to be consistently separable in their natural ambient space, namely, the manifold of Symmetric Positive Definite (SPD) matrices endowed with the Affine-Invariant Riemannian metric, which both contributes to and helps explain the state-of-the-art classification performance. For ALS gesture recognition, a distinct set of features derived from lag correlation vectors is introduced, capturing the temporal dependencies and internal rhythmic patterns of electromyography (EMG)signals through windowed lag-based comparisons. These representations preserve signal dynamics often lost in conventional feature engineering, providing a richer and more discriminative input for classification models. Together, these innovations advance the application of causal inference and structure-informed signal representations in clinical AI, offering new pathways toward interpretable and effective diagnostic tools. | pt_PT |
| dc.identifier.tid | 204070473 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190192 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Seizure Detection | pt_PT |
| dc.subject | Gesture Recognition | pt_PT |
| dc.subject | Brain-Computer Interface | pt_PT |
| dc.subject | Causal AI | pt_PT |
| dc.subject | Powers of the Covariance Matrices | pt_PT |
| dc.subject | Lag Correlation Vectors | pt_PT |
| dc.subject | SDG 3 - Good health and well-being | pt_PT |
| dc.subject | SDG 4 - Quality education | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 10 - Reduced inequalities | pt_PT |
| dc.title | Detecting the Onset of Neurological Critical Events via Structure-Informed AI: Clinical Applications in Epilepsy and Amyotrophic Lateral Sclerosis | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Business Intelligence | pt_PT |