Gambús Cerrillo, Pedro LuisOrtín López, Marta2023-06-152023-06-152023-06-06https://hdl.handle.net/2445/199283Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Gambús Cerrillo, Pedro LuisSeveral studies address the process of loss of consciousness during the induction of general anaesthesia, but few of them discuss or study the process of recovery of consciousness once the of general anaesthesia has been administered successfully. The main objective of this project is to study and develop a predictive model of the duration of this process of consciousness recovery based on Machine Learning (ML) and the analysis of electroencephalographic (EEG) signals. A dataset comprising 143 patients from the 4th operating room of the Hospital Clínic of Barcelona was analysed. The project involved data pre-processing, including the segmentation of EEG signals during the recovery process, feature extraction, and correlation analysis. Five ML regression algorithms, namely Linear, Lasso, and Ridge Regression, Support Vector Regression (SVR), and Random Forest (RF), were evaluated using a Cross-Validation pipeline. Model performance, feature selection, and hyperparameter optimization were assessed using the R-squared score criterion. The best performing algorithm was the regularized linear regression model, Lasso, achieving an R-squared score of 0.74 ± 0.032 (mean and standard deviation). Through the correlation analysis and the feature selection performed by the algorithm, high predictive capabilities of consciousness recovery time were obtained for alpha and beta relative averaged band power in the first minute before stopping general anaesthesia administration. The findings demonstrate that EEG signals contain valuable information regarding the process of consciousness recovery, enabling the construction of ML predictive models. However, further studies are required to enhance our understanding of the consciousness recovery process and to validate the predictive model in a clinical setting. Future investigations should focus on increasing data variability, addressing biases in validation techniques, exploring additional EEG channels to capture global brain activity, and considering regulatory considerations for Artificial Intelligence algorithms.64 p.application/pdfengcc-by-nc-nd (c) Ortín López, Marta, 2023http://creativecommons.org/licenses/by-nc-nd/3.0/es/Enginyeria biomèdicaTreballs de fi de grauAnestèsiaConsciènciaElectroencefalografiaTeoria de la prediccióAprenentatge automàticBiomedical engineeringBachelor's thesesAnesthesiaConsciousnessElectroencephalographyMachine learningPrediction theoryinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccess