MemHolo: mixed reality experiences for subjects with Alzheimer’s disease

HoloLens is the most recent and advanced forms of wearable Mixed Reality (MR) technology. It enables the user wearing a head-mounted device to experience 3D holographic objects “inside” the visualization of the real environment where he or she is located. Existing HoloLens applications have been developed in domains such as data visualization, entertainment, industrial training, education, and tourism, but the use of this technology in the arena of mental health is largely unexplored. The paper presents a HoloLens-based system called MemHolo that addresses persons with mild Alzheimer’s Disease (AD). AD is associated to a chronic progressive neurodegenerative process that severely affects cognitive functioning (especially memory) and some motor functions. MemHolo is intended to be used as a cognitive training tool to practice short-term and spatial memory in a safe and controlled virtual environment, and to mitigate the effects of mental decline. The paper discusses the design process of MemHolo, and describes three evaluation studies on progressive prototypes. To our knowledge, MemHolo is the first HoloLens application designed natively for persons with AD. Our empirical work sheds a light on how these people experience HoloLens applications, highlights some challenges and potential benefits of using MR technology in the AD arena, and may pave the ground towards new forms of treatment.

     

Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network

Multimedia Tools and Applications - Alzheimer’s disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the...     

An efficient classification approach for detection of Alzheimer’s disease from biomedical imaging modalities

Multimedia Tools and Applications - The complex patterns of the neuroimaging data are analyzed successfully with bio-medical imaging applications. The patients with/without AD can be discriminated...     

A Two Stage Sequential Ensemble Applied to the Classification of Alzheimer’s Disease Based on MRI Features

We present a two stage sequential ensemble where data samples whose output from the first classifier fall in a low confidence output interval (LCOI) are processed by a second stage classifier. Training is composed of three processes: training the first classifier, determining the LCOI of the first classifier, and training the second classifier upon the data items whose output fall in the LCOI. The LCOI is determined varying a threshold on the false positive rate (FPR) and false negative rate (FNR) curves. We have tested the approach on a database of feature vectors for the classification of Alzheimer’s disease (AD) and control subjects extracted from structural magnetic resonance imaging (sMRI) data. In this paper, we focus on the combinations obtained when the first classifier is a relevance vector machine (RVM). Obtained results improve over previous results for this database.     

Machine learning technique for early detection of Alzheimer’s disease

Microsystem Technologies - Alzheimer’s disease (AD) is non-repairable brain disorder which impacts a person’s thinking along with shrinking the size of the brain, ultimately resulting...     

Multiclass classification of Parkinson’s disease using cepstral analysis

This paper addressees the problem of an early diagnosis of PD (Parkinson’s disease) by the classification of characteristic features of person’s voice knowing that 90% of the people with PD suffer from speech disorders. We collected 375 voice samples from healthy and people suffer from PD. We extracted from each voice sample features using the MFCC and PLP Cepstral techniques. All the features are analyzed and selected by feature selection algorithms to classify the subjects in 4 classes according to UPDRS (unified Parkinson’s disease Rating Scale) score. The advantage of our approach is the resulting and the simplicity of the technique used, so it could also extended for other voice pathologies. We used as classifier the discriminant analysis for the results obtained in previous multiclass classification works. We obtained accuracy up to 87.6% for discrimination between PD patients in 3 different stages and healthy control using MFCC along with the LLBFS algorithm.     

Alzheimer’s disease diagnosis based on long-range dependency mechanism using convolutional neural network

Multimedia Tools and Applications - Being able to collect rich morphological information of brain, structural magnetic resonance imaging (MRI) is popularly applied to computer-aided diagnosis of...     

Re-transfer learning and multi-modal learning assisted early diagnosis of Alzheimer’s disease

Nowadays more and more elderly people are suffering from Alzheimer’s disease (AD). Finely recognizing mild cognitive impairment (MCI) in early stage of the symptom is vital for AD therapy. However, brain image samples are relatively scarce, meanwhile have multiple modalities, which makes finely classifying brain images by computers extremely difficult. This paper proposes a fine-grained brain image classification approach for diagnosing Alzheimer’s disease, with re-transfer learning and multi-modal learning. First of all, an end-to-end deep neural network classifier CNN4AD is designed to finely classify diffusion tensor image (DTI) into four categories. And according to the characteristics of multi-modal brain image dataset, the re-transfer learning method is proposed based on transfer learning and multi-modal learning theories. Experimental results show that the proposed approach obtain higher accuracy with less labeled training samples. This could help doctors diagnose Alzheimer’s disease more timely and accurately.

     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

T-test based Alzheimer’s disease diagnosis with multi-feature in MRIs

Multimedia Tools and Applications - Diagnosing Alzheimer’s disease (AD) with magnetic resonance imaging (MRI) has attracted increasing attention. In this paper, we propose a new feature...     

Association rule-based feature selection method for Alzheimer’s disease diagnosis

A fundamental challenge that remains unsolved in the neuroimage field is the small sample size problem. Feature selection and extraction, which are based on a limited training set, are likely to display poor generalization performance on new datasets. To address this challenge, a novel voxel selection method based on association rule (AR) mining is proposed for designing a computer aided diagnosis (CAD) system. The proposed method is tested as a tool for the early diagnosis of Alzheimer’s disease (AD). Discriminant brain areas are selected from a single photon emission computed tomography (SPECT) or positron emission tomography (PET) databases by means of an AR mining process. Simultaneously activated brain regions in control subjects that consist of the set of voxels defining the antecedents and consequents of the ARs are selected as input voxels for posterior dimensionality reduction. Feature extraction is defined by a subsequent reduction of the selected voxels using principal component analysis (PCA) or partial least squares (PLS) techniques while classification is performed by a support vector machine (SVM). The proposed method yields an accuracy up to 91.75% (with 89.29% sensitivity and 95.12% specificity) for SPECT and 90% (with 89.33% sensitivity and 90.67% specificity) for PET, thus improving recently developed methods for early diagnosis of AD.     

What is happening now? Detection of activities of daily living from simple visual features

We propose and investigate a paradigm for activity recognition, distinguishing the “on-going activity” recognition task (OGA) from that addressing “complete activities” (CA). The former starts from a time interval and aims to discover which activities are going on inside it. The latter, in turn, focuses on terminated activities and amounts to taking an external perspective on activities. We argue that this distinction is quite natural and the OGA task has a number of interesting properties; e.g., the possibility of reconstructing complete activities in terms of on-going ones, the avoidance of the thorny issue of activity segmentation, and a straightforward accommodation of complex activities, etc. Moreover, some plausible properties of the OGA task are discussed and then investigated in a classification study, addressing: the dependence of classification performance on the duration of time windows and its relationship with actional types (homogeneous vs. non-homogeneous activities), and on the assortments of features used. Three types of visual features are exploited, obtained from a data set that tries to balance the pros and cons of laboratory-based and naturalistic ones. The results provide partial confirmation to the hypothesis and point to relevant open issues for future work.     

Outlier based literature exploration for cross-domain linking of Alzheimer’s disease and gut microbiota

In knowledge discovery, experts frequently need to combine knowledge from different domains to get new insights and derive new conclusions. Intelligent systems should support the experts in the search for relationships between concepts from different domains, where huge amounts of possible combinations require the systems to be efficient but also sufficiently general, open and interactive to enable the experts to creatively guide the discovery process. The paper proposes a cross-domain literature mining methodology that achieves this functionality by combining the functionality of two complementary text mining tools: clustering and topic ontology creation tool OntoGen and cross-domain bridging terms exploration tool CrossBee. Focusing on outlier documents identified by OntoGen contributes to the efficiency, while CrossBee allows for flexible and user-friendly bridging concepts exploration and identification. The proposed approach, which is domain independent and can support cross-domain knowledge discovery in any field of science, is illustrated on a biomedical case study dealing with Alzheimer’s disease, one of the most threatening age-related diseases, deteriorating lives of numerous individuals and challenging the ageing society as a whole. By applying the proposed methodology to Alzheimer’s disease and gut microbiota PubMed articles, we have identified Nitric oxide synthase (NOS) as a potentially valuable link between these two domains. The results support the hypothesis of neuroinflammatory nature of Alzheimer’s disease, and is indicative for the quest for identifying strategies to control nitric oxide-associated pathways in the periphery and in the brain. By addressing common mediators of inflammation using literature-based discovery, we have succeeded to uncover previously unidentified molecular links between Alzheimer’s disease and gut microbiota with a multi-target therapeutic potential.     

Protocol for fast screening of multi-target drug candidates: Application to Alzheimer’s disease

The treatment of many diseases may require drugs that are capable to attack multiple targets simultaneously. Obviously, the virtual screening of multi-target drug candidates is much more time consuming compared to the single-target case. This, in particular, concerns the last step of virtual screening where the binding free energy is computed by conventional molecular dynamics simulation. To overcome this difficulty we propose a simple protocol which is relied on the fast steered molecular dynamics simulation and on available experimental data on binding affinity of reference ligand to a given target. Namely, first we compute non-equilibrium works generated during pulling ligands from the binding site using the steered molecular dynamics method. Then as top leads we choose only those compounds that have the non-equilibrium work larger than that of a reference compound for which the binding free energy has been already known from experiment.Despite many efforts no cures for AD (Alzheimer’s disease) have been found. One of possible reasons for this failure is that drug candidates were developed for a single target, while there are exist many possible pathways to AD. Applying our new protocol to five targets including amyloid beta fibril, peroxisome proliferator-activated receptor γ, retinoic X receptor α, β- and γ-secretases, we have found two potential drugs (CID 16040294 and CID 9998128) for AD from the large PubChem database. We have also shown that these two ligands can interfere with the activity of popular Acetylcholinesterase target through strong binding towards it.     

Ball detection in soccer images using isophote’s curvature and discriminative features

Ball recognition in soccer matches is a critical issue for automatic soccer video analysis. Unfortunately, because of the difficulty in solving the problem, many efforts of numerous researchers have still not produced fully satisfactory results in terms of accuracy. This paper proposes a ball recognition approach that introduces a double level of innovation. Firstly, a randomized circle detection approach based on the local curvature information of the isophotes is used to identify the edge pixels belonging to the ball boundaries. Then, ball candidates are validated by a learning framework formulated into a three-layered model based on a variation of the conventional local binary pattern approach. Experimental results were obtained on a significant set of real soccer images, acquired under challenging lighting conditions during Italian “Serie A” matches. The results have been also favorably compared with the leading state-of-the-art methods.     

Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources

Alzheimer’s disease is a complex progressive neurodegenerative brain disorder, being its prevalence expected to rise over the next decades. Unconventional strategies for elucidating the genetic mechanisms are necessary due to its polygenic nature. In this work, the input information sources are five: a public DNA microarray that measures expression levels of control and patient samples, repositories of known genes associated to Alzheimer’s disease, additional data, Gene Ontology and finally, a literature review or expert knowledge to validate the results. As methodology to identify genes highly related to this disease, we present the integration of three machine learning techniques: particularly, we have used decision trees, quantitative association rules and hierarchical cluster to analyze Alzheimer’s disease gene expression profiles to identify genes highly linked to this neurodegenerative disease, through changes in their expression levels between control and patient samples. We propose an ensemble of decision trees and quantitative association rules to find the most suitable configurations of the multi-objective evolutionary algorithm GarNet, in order to overcome the complex parametrization intrinsic to this type of algorithms. To fulfill this goal, GarNet has been executed using multiple configuration settings and the well-known C4.5 has been used to find the minimum accuracy to be satisfied. Then, GarNet is rerun to identify dependencies between genes and their expression levels, so we are able to distinguish between healthy individuals and Alzheimer’s patients using the configurations that overcome the minimum threshold of accuracy defined by C4.5 algorithm. Finally, a hierarchical cluster analysis has been used to validate the obtained gene-Alzheimer’s Disease associations provided by GarNet. The results have shown that the obtained rules were able to successfully characterize the underlying information, grouping relevant genes for Alzheimer Disease. The genes reported by our approach provided two well defined groups that perfectly divided the samples between healthy and Alzheimer’s Disease patients. To prove the relevance of the obtained results, a statistical test and gene expression fold-change were used. Furthermore, this relevance has been summarized in a volcano plot, showing two clearly separated and significant groups of genes that are up or down-regulated in Alzheimer’s Disease patients. A biological knowledge integration phase was performed based on the information fusion of systematic literature review, enrichment Gene Ontology terms for the described genes found in the hippocampus of patients. Finally, a validation phase with additional data and a permutation test is carried out, being the results consistent with previous studies.     

Early diagnosis of Alzheimer’s disease based on Partial Least Squares and Support Vector Machine

An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance for the patient medical treatment. It has been shown that pathological manifestations of AD may be detected thought functional images even before that the patients becomes symptomatic. This fact has led researchers to propose new ways for analyzing functional data in order to get more accurate Computer-Aided Diagnosis (CAD) systems for this disorder. In this paper we show an effective approach for Single Photon Emission Computed Tomography feature extraction that improves the accuracy of CAD systems for AD. The proposed methodology uses a Partial Least Squares algorithm for extracting score vectors and the Out-Of-Bag error for selecting a number of scores that are used as features. Subsequently, a Support Vector Machine (SVM) based classifier determines the underlying class of the images, thus performing diagnostics. In order to test this approach we have used an image database for AD with 97 SPECT images from controls and AD patients. The images were visually labeled by experienced clinicians after the properly normalization. Several experiments have been developed to compare the proposed methodology and previous approaches. The results show that our method yields accuracy rates over 90%, outperforming several recently reported CAD systems for AD diagnosis.     

Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm

Since approximately 90% of the people with PD (Parkinson’s disease) suffer from speech disorders including disorders of laryngeal, respiratory and articulatory function, using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. All previous works are done to distinguish healthy people from people with Parkinson’s disease (PWP). In this paper, we propose to go further by multiclass classification with three classes of Parkinson stages and healthy control. So we have used 40 features dataset, all the features are analyzed and 9 features are selected to classify PWP subjects in four classes, based on unified Parkinson’s disease Rating Scale (UPDRS). Various classifiers are used and their comparison is done to find out which one gives the best results. Results show that the subspace discriminant reach more than 93% overall classification accuracy.     

Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification

Neural Computing and Applications - Cognitive impairment must be diagnosed in Alzheimer’s disease as early as possible. Early diagnosis allows the person to receive effective treatment...