750 research outputs found

    Representation Learning: A Review and New Perspectives

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    The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

    Sleep Stage Classification: A Deep Learning Approach

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    Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

    Deep learning for automated sleep monitoring

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    Wearable electroencephalography (EEG) is a technology that is revolutionising the longitudinal monitoring of neurological and mental disorders, improving the quality of life of patients and accelerating the relevant research. As sleep disorders and other conditions related to sleep quality affect a large part of the population, monitoring sleep at home, over extended periods of time could have significant impact on the quality of life of people who suffer from these conditions. Annotating the sleep architecture of patients, known as sleep stage scoring, is an expensive and time-consuming process that cannot scale to a large number of people. Using wearable EEG and automating sleep stage scoring is a potential solution to this problem. In this thesis, we propose and evaluate two deep learning algorithms for automated sleep stage scoring using a single channel of EEG. In our first method, we use time-frequency analysis for extracting features that closely follow the guidelines that human experts follow, combined with an ensemble of stacked sparse autoencoders as our classification algorithm. In our second method, we propose a convolutional neural network (CNN) architecture for automatically learning filters that are specific to the problem of sleep stage scoring. We achieved state-of-the-art results (mean F1-score 84%; range 82-86%) with our first method and comparably good results with the second (mean F1-score 81%; range 79-83%). Both our methods effectively account for the skewed performance that is usually found in the literature due to sleep stage duration imbalance. We propose a filter analysis and visualisation methodology for CNNs to understand the filters that CNNs learn. Our results indicate that our CNN was able to robustly learn filters that closely follow the sleep scoring guidelines.Open Acces
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