Automatic hierarchical attention neural network for detecting AD

Picture description tasks are used for the detection of cognitive decline associated with Alzheimer’s disease (AD). Recent years have seen work on automatic AD detection in picture descriptions based on acoustic and word-based analysis of the speech. These methods have shown some success but lack an ability to capture any higher level effects of cognitive decline on the patient’s language. In this paper, we propose a novel model that encompasses both the hierarchical and sequential structure of the description and detect its informative units by attention mechanism. Automatic speech recognition (ASR) and punctuation restoration are used to transcribe and segment the data. Using the DementiaBank database of people with AD as well as healthy controls (HC), we obtain an F-score of 84.43% and 74.37% when using manual and automatic transcripts respectively. We further explore the effect of adding additional data (a total of 33 descriptions collected using a ‘ digital doctor’ ) during model training, and increase the F-score when using ASR transcripts to 76.09%. This outperforms baseline models, including bidirectional LSTM and bidirectional hierarchical neural network without an attention mechanism, and demonstrate that the use of hierarchical models with attention mechanism improves the AD/HC discrimination performance

A Comprehensive Literature Review on Convolutional Neural Networks

The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further

Deep learning-based EEG emotion recognition: Current trends and future perspectives

Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human–computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions

Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods

Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection. In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application. The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings. Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection

Machine Learning Methods for Depression Detection Using SMRI and RS-FMRI Images

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data. In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either data-driven or model-driven methods such as cubes and atlases respectively. For structural MRI (sMRI) similarity of voxels of spatial cubes (data-driven) are explored. For resting-state fMRI (rs-fMRI) images, the similarity of the time series of both cubes (data-driven) and atlases (model-driven) are examined. Moreover, the similarity method of the inverse of Minimum Covariant Determinant is applied that excludes outliers from patterns and finds conditionally independent regions given the rest of regions. Next, a statistical test that is robust to outliers, identifies discriminative similarity features between two groups of MDDs and controls. Therefore, the key contribution is the way to get discriminative features that include obtaining similarity of voxel’s cubes/time series using the inverse of robust covariance along with the statistical test. The experimental results show that obtaining these features along with the Bernoulli Naïve Bayes classifier achieves superior performance compared with other methods. The performance of our method is verified by applying it to three imbalanced datasets. Moreover, the similarity-based methods are compared with deep learning and regional-based approaches for detecting MDD using either sMRI or rs-fMRI. Given that depression is famous to be a connectivity disorder problem, investigating the similarity of the brain’s regions is valuable to understand the behavior of the brain. The combinations of structural and functional brain similarities are explored to investigate the brain’s structural and functional properties together. Moreover, the combination of data-driven (cube) and model-driven (atlas) similarities of rs-fMRI are looked over to evaluate how they affect the performance of the classifier. Besides, discriminative similarities are visualized for both sMRI and rs-fMRI. Also, to measure the informativeness of a cube, the relationship of atlas regions with overlapping cubes and vise versa (cubes with overlapping regions) are explored and visualized. Furthermore, the relationship between brain structure and function has been probed through common similarities between structural and resting-state functional networks

Biomedical Data Classification with Improvised Deep Learning Architectures

With the rise of very powerful hardware and evolution of deep learning architectures, healthcare data analysis and its applications have been drastically transformed. These transformations mainly aim to aid a healthcare personnel with diagnosis and prognosis of a disease or abnormality at any given point of healthcare routine workflow. For instance, many of the cancer metastases detection depends on pathological tissue procedures and pathologist reviews. The reports of severity classification vary amongst different pathologist, which then leads to different treatment options for a patient. This labor-intensive work can lead to errors or mistreatments resulting in high cost of healthcare. With the help of machine learning and deep learning modules, some of these traditional diagnosis techniques can be improved and aid a doctor in decision making with an unbiased view. Some of such modules can help reduce the cost, shortage of an expertise, and time in identifying the disease. However, there are many other datapoints that are available with medical images, such as omics data, biomarker calculations, patient demographics and history. All these datapoints can enhance disease classification or prediction of progression with the help of machine learning/deep learning modules. However, it is very difficult to find a comprehensive dataset with all different modalities and features in healthcare setting due to privacy regulations. Hence in this thesis, we explore both medical imaging data with clinical datapoints as well as genomics datasets separately for classification tasks using combinational deep learning architectures. We use deep neural networks with 3D volumetric structural magnetic resonance images of Alzheimer Disease dataset for classification of disease. A separate study is implemented to understand classification based on clinical datapoints achieved by machine learning algorithms. For bioinformatics applications, sequence classification task is a crucial step for many metagenomics applications, however, requires a lot of preprocessing that requires sequence assembly or sequence alignment before making use of raw whole genome sequencing data, hence time consuming especially in bacterial taxonomy classification. There are only a few approaches for sequence classification tasks that mainly involve some convolutions and deep neural network. A novel method is developed using an intrinsic nature of recurrent neural networks for 16s rRNA sequence classification which can be adapted to utilize read sequences directly. For this classification task, the accuracy is improved using optimization techniques with a hybrid neural network