Electrophysiological signals, such as electroencephalography (EEG) signals, are complex by nature, exhibiting non-linear and non-stationary behavior, leading to limitations in their analysis and the development of effective clinical applications. Modal decomposition has proven valuable for studying the dynamic of physiological systems, particularly in disease diagnosis and functional state detection, by decomposing signals into modes that capture essential information. However, existing methods face difficulties with time-varying noise and singularities, such as crossing frequencies and vanishing amplitude components, leading to imprecise applications.
The proposed approach in this research book aims to evaluate the EKFs method and two optimized approaches of this technique, using the Metropolis-Hastings Markov Chain Monte Carlo (EKFs−MH) and Expectation-Maximization (EKFs−EM) algorithms, with both a numerically generated non-stationary signal and EEG signals for seizure prediction. The optimized approaches demonstrated superior performance, successfully managing singularities and minimizing errors in state-space representation.
- Estado de la publicación Activo
- Detalle de formato de producto Digital, descarga y en línea
- eISBN 9786287751552
- DOI 10.22430/reporte.8299
- Peso (Mb) 13508008
- Peso (Mb) 13191.41
- Peso (Mb) 12.88
- Páginas orientativas: 97
- Idioma Inglés
- Ciudad de publicación Medellín
- País de publicación Colombia
- Fecha de publicación 20261001
- Detalle comercial (PVP) $ 0,00
- SKU 74ed2edf71356408e335cfb8b290bff9
- SCI055000 > CIENCIA > Física > General
- PBKS
- 621.3 > Tecnología (ciencias aplicadas) > Ingeniería y operaciones afines > Física Aplicada > Eléctrico, electrónico, magnético, comunicaciones, ingeniería informática; iluminación
eBook
Digital: descarga y online - EPUB
Repositorio Institucional ITM:
List of Figures — p. viList of Tables — p. viiiAbstract — p. x1 Introduction — p. 11.1 Justification — p. 21.2 Problem description — p. 31.3 Hypothesis — p. 41.4 Objectives — p. 51.4.1 General Objective — p. 51.4.2 Specific objectives — p. 51.5 Manuscript structure — p. 52 State of the art — p. 62.1 Empirical Mode Decomposition (EMD) — p. 72.2 Proper Orthogonal Decomposition (POD) — p. 92.3 Variational Mode Decomposition (VMD) — p. 102.4 Time-frequency transform — p. 102.5 State-space representation — p. 112.6 General considerations — p. 123 Experimental framework — p. 133.1 Datasets — p. 133.1.1 Generated non-stationary signal — p. 133.1.2 Electroencephalography database – Siena Scalp EEG — p. 153.2 Time-dependent state space modal representation — p. 163.3 State-Space representation estimation — p. 193.3.1 Extended Kalman Filter — p. 193.3.2 Kalman Smoother — p. 223.3.3 Initial conditions estimation — p. 223.4 State-space representation optimization — p. 233.4.1 Expectation-Maximization (EM) — p. 233.4.2 Metropolis-Hastings Markov Chain Monte Carlo — p. 263.5 Classification — p. 293.5.1 Features generation — p. 293.5.2 Feature selection — p. 303.5.3 Random Forest (RF) — p. 313.6 Proposed methodology — p. 323.6.1 Methodology for the first objective — p. 333.6.2 Methodology for the second objective — p. 353.6.3 Methodology for the third objective — p. 394 Results and discussion — p. 444.1 First objective results — p. 444.2 Second objective results — p. 474.2.1 Non-stationary generated signal — p. 474.2.2 EEG signals. — p. 524.3 Third objective results — p. 545 Conclusions — p. 636 Future work and recommendations — p. 657 Intellectual property — p. 668 Impacts — p. 679 Conflicts of interest — p. 68Bibliography — p. 69
| Subtitulo | Applications in Electrophysiological Signals |
|---|---|
| Formato | grouped |
| ISBN | 9786287751552 |
| Palabras claves | modal analysis : non-stationary processes : electrophysiological signals : EEG : seizure prediction : extended Kalman filters : Metropolis-Hastings : Expectation-Maximization : signal decomposition : time-varying noise |
| Año de publicación | 2026 |