Conception d'un outil informatique basé sur un réseau de neurones artificiels pour la classification automatique des stades du sommeil

L’objectif de notre travail est de développer un outil d’analyse automatique des stades du sommeil basé sur les réseaux de neurones artificiels (RNA). Dans ce papier nous présentons notre démarche pour la conception de cet outil. La première difficulté consiste dans le choix de la représentation des signaux physiologiques et en particulier de l’électroencéphalogramme (EEG). Une fois la représentation adoptée, l’étape suivante est la conception du réseau de neurones optimal déterminé par un processus d’apprentissage et de validation sur les données issues d’un ensemble d'enregistrements de nuits de sommeil. Le résultat obtenu avec un taux de 63% de bonne classification pour six stades, nous incite à approfondir l’étude de cette problématique aux niveaux représentation et conception pour améliorer les performances de notre outil

Learning Better Clinical Risk Models.

Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models. As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings. Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd

Automation of Sleep Staging

This thesis primarily covers the automation problem for sleep versus awake detection, which is sometimes accomplished by differentiating the various sleep stages prior to clustering. This thesis documents various experimentation into areas where the performance can be improved, including classifer design and feature selection from EEG, EOG and Context. In terms of classifers, it was found that the neural network MLP outperforms the continuous Hidden Markov Model with an accuracy of 91.91%, and additional performance requires better feature sets and more training data. Improved EEG features based on time frequency representation were optimized to differentiate Awake with 93.52% sensitivity and 94.60% specificity, differentiate REM with 96.12% sensitivity and 93.63% specificity, differentiate Stages II and III with 96.81% sensitivity and 89.28% specificity, and differentiate Stages III and IV with 93.60% sensitivity and 90.43% specificity. Due to the limited data set, an example of applying contextual information using a One-Cycle-Duo-Direction model was built and shown to improve EEG features by up to 10%. This level of performance is comparable if not superior to the human scorer accuracy of 88% to 94%. This thesis improved some aspects of sleep staging automation, but due to the limitations on resources, the full potential of these improvements could not be demonstrated. To further develop these improvements, additional data sets customized by sleep staging experts is crucial