Investigation of sensor placement for accurate fall detection

Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Interpreting PET scans by structured patient data: a data mining case study in dementia research

One of the goals of medical research in the area of dementia is to correlate images of the brain with other variables, for instance, demographic information or outcomes of clinical tests. The usual approach is to select a subset of patients based on such variables and analyze the images associated with those patients. In this paper, we apply data mining techniques to take the opposite approach: We start with the images and explain the differences and commonalities in terms of the other variables. In the first step, we cluster PET scans of patients to form groups sharing similar features in brain metabolism. To the best of our knowledge, it is the first time ever that clustering is applied to whole PET scans. In the second step, we explain the clusters by relating them to non-image variables. To do so, we employ RSD, an algorithm for relational subgroup discovery, with the cluster membership of patients as target variable. Our results enable interesting interpretations of differences in brain metabolism in terms of demographic and clinical variables. The approach was implemented and tested on an exceptionally large pre-existing data collection of patients with different types of dementia. It comprises 10 GB of image data from 454 PET scans, and 42 variables from psychological and demographical data organized in 11 relations of a relational database. We believe that explaining medical images in terms of other variables (patient records, demographic information, etc.) is a challenging new and rewarding area for data mining research

Mammographic density. Measurement of mammographic density

Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations

Genetic and structural study of DNA- directed RNA polymerase II of Trypanosoma brucei, towards the designing of novel antiparasitic agents

Trypanosoma brucei brucei (TBB) belongs to the unicellular parasitic protozoa organisms, specifically to the Trypanosoma genus of the Trypanosomatidae class. A variety of different vertebrate species can be infected by TBB, including humans and animals. Under particular conditions, the TBB can be hosted by wild and domestic animals; therefore, an important reservoir of infection always remains available to transmit through tsetse flies. Although the TBB parasite is one of the leading causes of death in the most underdeveloped countries, to date there is neither vaccination available nor any drug against TBB infection. The subunit RPB1 of the TBB DNAdirected RNA polymerase II (DdRpII) constitutes an ideal target for the design of novel inhibitors, since it is instrumental role is vital for the parasite's survival, proliferation, and transmission. A major goal of the described study is to provide insights for novel anti-TBB agents via a state-of-the-art drug discovery approach of the TBB DdRpII RPB1. In an attempt to understand the function and action mechanisms of this parasite enzyme related to its molecular structure, an in-depth evolutionary study has been conducted in parallel to the in silico molecular designing of the 3D enzyme model, based on state-of-the-art comparative modelling and molecular dynamics techniques. Based on the evolutionary studies results nine new invariant, first-time reported, highly conserved regions have been identified within the DdRpII family enzymes. Consequently, those patches have been examined both at the sequence and structural level and have been evaluated in regard to their pharmacological targeting appropriateness. Finally, the pharmacophore elucidation study enabled us to virtually in silico screen hundreds of compounds and evaluate their interaction capabilities with the enzyme. It was found that a series of chlorine-rich set of compounds were the optimal inhibitors for the TBB DdRpII RPB1 enzyme. All-in-all, herein we present a series of new sites on the TBB DdRpII RPB1 of high pharmacological interest, alongside the construction of the 3D model of the enzyme and the suggestion of a new in silico pharmacophore model for fast screening of potential inhibiting agents. © 2017 Papageorgiou et al

Detecting discriminative functional MRI activation patterns using space filling curves

INTRODUCTION The detection of relationships between human brain structures and brain functions (i.e., human brain mapping) has been recognized as one of the main goals of the Human Brain Project [1]. Several approaches have been used in this problem domain [2]. One of the approaches used in functional brain mapping is to seek associations between brain activation patterns and tasks performed. A current obstacle in this type of analysis is the lack of methods to automatically classify such patterns (i.e., activation regions) and quantitatively measure levels of their similarity. In this paper we propose a technique for detecting and classifying functional Magnetic Resonance Imaging (fMRI) activation patterns. More specifically, we seek to discover brain activation patterns that are associated with a particular disease. * This work was supported in part by the NSF (IIS-0083423) and the Pennsylvania Department of Health. Funding parties specifically disclaim re

Detecting Discriminative Functional MRI . . .

INTRODUCTION The detection of relationships between human brain structures and brain functions (i.e., human brain mapping) has been recognized as one of the main goals of the Human Brain Project [1]. Several approaches have been used in this problem domain [2]. One of the approaches used in functional brain mapping is to seek associations between brain activation patterns and tasks performed. A current obstacle in this type of analysis is the lack of methods to automatically classify such patterns (i.e., activation regions) and quantitatively measure levels of their similarity. In this paper we propose a technique for detecting and classifying functional Magnetic Resonance Imaging (fMRI) activation patterns. More specifically, we seek to discover brain activation patterns that are associated with a particular disease. * This work was supported in part by the NSF (IIS-0083423) and the Pennsylvania Department of Health. Funding parties specifically disclaim re

Integrating clinical information repositories: A framework for

We propose a framework for distributed analysis of medical images, in order to integrate data pooled from multiple studies and different clinical sites. We perform distributed post-processing of the data at their local site, coordinated by a central computing node. The clinical focus is to assist diagnosis by detecting Regions of Interest (ROIs) that can be used for distinguishing different clinical cases. The clinical cases are collected in spatially distributed centers. The features that are extracted from the image ROIs are useful in performing classification, clustering and similarity searches aiming to facilitate diagnosis and guide medical treatment. The system is based on a central information fusion center that communicates with the remote sites and coordinates the distributed analysis. The proposed distributed medical image analysis model facilitates knowledge sharing among clinicians and information integration, while the bandwidth requirements are kept to a minimum

Distinguishing Among 3-D Distributions for Brain Image Data Classification

ABSTRACT: To facilitate the process of discovering brain structure-function associations from image and clinical data, we have developed classification tools for brain image data that are based on measures of dissimilarity between probability distributions. We propose statistical as well as non-statistical methods for classifying three dimensional probability distributions of regions of interest (ROIs) in brain images. The statistical methods are based on computing the Mahalanobis distance and Kullback-Leibler distance between a new subject and historic data sets related to each considered class. The new subject is predicted to belong to the class corresponding to the dataset that has the smaller distance from the given subject. The non-statistical methods consist of a sequence of partitioning the brain image into hyper-rectangles followed by applying supervised neural network models. Experiments performed on synthetic data representing mixtures of nine distributions as well as on realistic brain lesion distributions from a study of attention-deficit hyperactivity disorder (ADHD) after closed head injury showed that all proposed methods are capable of providing accurate classification of the subjects with the Kullback-Leibler distance being the least sensitive on the size of the training set and on information about the new subject. The proposed statistical methods provide comparable classification to neural networks with appropriately generated attributes, while requiring less computational time