Ashwin Belle

Big Data Analytics in Healthcare.

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey

The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest.

An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.

A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis

Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a hierarchical method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander.

An Entropy-Based Automated Cell Nuclei Segmentation and Quantification: Application in Analysis of Wound Healing Process

The segmentation and quantification of cell nuclei are two very significant tasks in the analysis of histological images. Accurate results of cell nuclei segmentation are often adapted to a variety of applications such as the detection of cancerous cell nuclei and the observation of overlapping cellular events occurring during wound healing process in the human body. In this paper, an automated entropy-based thresholding system for segmentation and quantification of cell nuclei from histologically stained images has been presented. The proposed translational computation system aims to integrate clinical insight and computational analysis by identifying and segmenting objects of interest within histological images. Objects of interest and background regions are automatically distinguished by dynamically determining 3 optimal threshold values for the 3 color components of an input image. The threshold values are determined by means of entropy computations that are based on probability distributions of the color intensities of pixels and the spatial similarity of pixel intensities within neighborhoods. The effectiveness of the proposed system was tested over 21 histologically stained images containing approximately 1800 cell nuclei, and the overall performance of the algorithm was found to be promising, with high accuracy and precision values.

Impedance plethysmography on the arms: Respiration monitoring

A new method for extracting respiratory rate from electrical impedance measured on the arms is presented. The method requires application of only four electrodes to the subjects arms and is suitable to be used in a portable respiratory rate monitor and decision making systems. Set-Membership filtering is used to reduce the effect of motion artifact on the signal.

Automated midline shift and intracranial pressure estimation based on brain CT images

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

Fracture detection and quantitative measure of displacement in pelvic CT images

Traumatic pelvic injury is a severe and common injury in the United States. The automatic detection of fractures in pelvic CT images is a significant contribution for assisting physicians in making faster and more accurate patient diagnostic decisions and treatment planning. However, due to the low resolution and quality of the original images, the complexity of pelvic structures, and the difference in visual characteristics of fracture by their location, it is difficult to detect and accurately locate the pelvic fractures and determine the severity of the injury. In this paper, an automatic hierarchical algorithm for detecting pelvic bone fractures in CT scans is proposed. The algorithm utilizes symmetric comparison, adaptive windowing, boundary tracing, wavelet transform. Also, the quantitative measure of fracture severity in pelvic CT scans is defined. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, shows potential for clinical application. Statistical results also indicate the superiority of the proposed method.

Rule-based Computer Aided Decision Making for Traumatic Brain Injuries

This chapter provides an overview of various machine learning algorithms which are typically adopted into many predictive computer-assisted decision making systems for traumatic injuries. The objective here is to compare some existing machine learning methods using an aggregated database of traumatic injuries. These methods are used towards the development of rule-based computer-assisted decision-making systems that provide recommendations to physicians for the course of treatment of the patients. Since physicians in trauma centers are constantly required to make quick yet difficult decisions for patient care using a multitude of patient information, such computer assisted decision support systems are bound to play a vital role in improving healthcare. The content of this chapter also presents a novel image processing method to assess traumatic brain injuries (TBI).

Reduction of periodic motion artifacts from impedance plethysmography

Motion artifact reduction is a fundamental part in portable monitoring of physiological signals. Here, the performance of three different motion artifact reduction methods, independent component analysis, least mean square filters and normalized least mean square filters are compared when applied to the impedance plethysmography signal. The results show that the performance of each method depends on the type of motion artifact it is being applied to, and that none of the methods universally performs better than the other ones. As a result, authors suggest that the motion artifact reduction algorithm should be designed based on the nature of the motion artifact.