Sleep Stage Classification: A Deep Learning Approach

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

Mass Spectrometric Methods Development for the Characterization of Components in Complex Mixtures for Enhanced Oil Recovery Operations and for Drug Development

High-resolution tandem mass spectrometry (MSn) coupled with separation techniques, such as high-performance liquid chromatography (HPLC) and gas chromatography (GC), has proven to be a valuable tool for the molecular level characterization of complex mixtures. The utilization of different methods, such as collision-activated dissociation (CAD) and gas-phase ion/molecule reactions, facilitates structural elucidation of the components in complex mixtures. This thesis primarily focuses on the development of tandem mass spectrometric methods for solving analytical challenges associated with the characterization of complex mixtures produced during enhanced oil recovery (EOR) operations and in drug discovery. Chapter 2 describes the instrumentation used for the research discussed in this thesis. In Chapter 3, the gas-phase reactivity of protonated polyfunctional model compounds toward trimethoxymethylsilane (TMMS) reagent and the utility of this reagent in the mass spectrometric identification of sulfone and aromatic functionalities in drug metabolites is discussed. Chapter 4 discusses an analytical methodology, namely Distillation Precipitation Fractionation Mass Spectrometry (DPF-MS), developed to perform molecular level profiling of crude oil. This analytical methodology involves the optimization of different mass spectrometric and ionization methods for the semi-quantitative molecular level characterization of crude oil and its fractions. Chapter 5 discusses a sensitive analytical method developed for the identification and quantitation of a tracer (2-fluorobenzoic acid) in oil reservoir brine produced during enhanced oil recovery. This method is based on solid-phase extraction followed by multiple reaction monitoring (MRM)based HPLC-MSn

Mapping of Ice and Gas on 1000 AU Scales

Many of the molecules in interstellar space are in the solid state, in so-called interstellar ices. The research in this thesis is focused around two key astronomical questions; How is ice distributed in star forming regions? How is ice affected by - or affecting - star formation processes? I provide answers to these questions through the analysis of submillimeter and near-infrared observations. The observations analysed in this thesis consist of (partially published) archival data acquired mainly with the AKARI and Herschel space telescopes, and the ground-based ESO/VLT. To facilitate the reduction and analysis of some of this data two major software packages (ARF2 and Omnifit) were created with the Python programming language The operation of both packages is fully documented in the thesis appendix. The study of methanol ice prevalence in star-forming regions found that methanol ice can be found towards many more lines of sight than previously reported, and that its abundance relative to water ice can vary between a few to ~40%. I also confirm that methanol very likely exists mixed in a water-rich ice component, a result consistent with our current understanding of methanol ice formation. Proof was found of high-temperature chemistry forming water in the warm postshock gas of YSOs. In this same region it was found that up to 99% of the methanol is being destroyed as it is sputtered from the surfaces of dust grains into the gas phase. A novel analysis technique of slitless AKARI near-infrared spectroscopy yields an unprecedented number of water ice column density estimates towards background star lines of sight covering 12 separate 10' x 10' fields of view in as many molecular clouds. A moderate correlation is found between water ice column density and dust optical depth at 250 microns, with the correlation potentially varying from cloud to cloud

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints