A Study on the Examination of RGB Scale Retinal Pictures Using Recent Methodologies

Retinal disease/condition examination is one of the significant areas of the medical field. A variety of retinal abnormality assessments based on fundus image-assisted trials are widely proposed by the researchers to examine the parts of the retina. Recently, traditional and soft computing-based approaches are executed to inspect the optic disc and the blood vessels of the retina to discover disease/damages. This work implements (i) A two-phase methodology based on Jaya Algorithm (JA) and Kapur's Entropy (KE) thresholding and level-set segmentation for the optic disc evaluation and (ii) JA-based Multi-scale Matched Filter (MMF) for the blood vessel assessment. During this analysis, various benchmark datasets such as RIM-ONE, DRIVE, and STARE are considered. The experimental study substantiates that JA-assisted retinal picture examination offers better results than other related existing methodologies.

Effect of Age on Heart Rate Variability Analysis in Breast Cancer Patients

Breast Cancer (BC) is the leading cause of death in women, worldwide. The Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) of BC can be studied using HRV measures. The main purpose of this chapter is to give an insight to clinicians via HRV measures with respect to age to make them understand the PS of patients. Data from 114 BC patients was segregated into two age groups, G1 (20 to 40 years) and G2 (41 to 75 years). The 5-minute electrocardiogram of the subjects was taken and HRV measures were extracted. One-way ANOVA with Posthoc Tukeys' HSD test was done. Triangular Index, Ratio of standard deviation of poincare plot perpendicular to the line of identity to the standard deviation along line of identity, Detrended Fluctuation Analysis descriptors, Approximate Entropy, Sample Entropy and Correlation Dimension significantly decreased from ECOG0 to 4 and from G1 to G2. The sympathetic activity increased with vagal withdrawal as age advanced.

Classification of Skin Lesion Using (Segmentation) Shape Feature Detection

Malignant melanoma has caused countless deaths in recent years. Many calculation methods have been created for automatic melanoma detection. In this chapter, based on the traditional concept of shape signature and convex hull, an improved boundary description shape signature is developed. The convex defect-based signature (CDBS) proposed in this paper scans contour irregularities and is applied to skin lesion classification in macroscopic images. Border irregularities of skin lesions are the predominant criteria for ABCD (asymmetry, border, color, and diameter) to distinguish between melanoma and nonmelanoma. The performance of the CDBS is compared with popular shape descriptors: shape signature, indentation depth function, invariant elliptic Fourier descriptor (IEFD), and rotation invariant wavelet descriptor (RIWD), where the proposed descriptor shows better results. Multilayer perceptron neural network is used as a classifier in this work. Experimental results show that the proposed approach achieves significant performance with mean accuracy of 90.49%.

Introduction to Motor Imagery-Based Brain-Computer Interface

In this chapter, motor imagery (MI) based brain-computer interface (BCI) is introduced incorporating the explanation of key components required to design a practical BCI device. Its application to the medical and nonmedical sector is discussed in detail. In the experimental study, a feature extraction method using time, frequency, and phase analysis of Motor imagery EEG is presented. For the classification of MI task, EEG signals are decomposed using a dual-tree complex wavelet transform (DTCWT) and then time, frequency, and phase features are extracted. The validation of the proposed method is conducted using BCI competition IV dataset 2b. A Support vector machine (SVM) classifier is used to perform the classification task. Performance of the proposed method is compared with the standard feature extraction methods. The proposed scheme achieved a larger average classification accuracy of 82.81% which is better than that obtained by other methods.

A Decision Tree on Data Mining Framework for Recognition of Chronic Kidney Disease

The term chronic kidney disease (CKD) refers to the malfunction of the kidney and its failure to remove toxins and other waste products from blood. Typical symptoms of CKD include color change in urine, swelling due to fluids staying in tissue, itching, flank pain, and fatigue. Timely intervention is essential for early recognition of CKD as it affects more than 10 million people in India. This chapter suggests a decision tree-based data mining framework to recognize CKD from Non chronic kidney disease (NCKD). Data sets derived from open source UCI repository was considered. Unlike earlier reported work, this chapter applies the decision rules based on the clustered data through k-means clustering process. Four cluster groups were identified and j48 pruned decision tree-based automated rules were formatted. The performance of the proposed framework was evaluated in terms of sensitivity, specificity, precision, and recall. A new quantitative measure, relative performance, and MCC were introduced which confirms the suitability of the proposed framework for recognition of CKD from NCKD.

Identification of High Risk and Low Risk Preterm Neonates in NICU

Infant cries are referred as the biological indicator where infant distress is expressed without any external stimulus. One can assess the physiological changes through cry characteristics that help in improving clinical decision. In a typical Neonatal Intensive Care Unit (NICU), recognizing high-risk and low-risk admitted preterm neonates is quite challenging and complex in nature. This chapter attempts to develop pattern recognition-based approach to identify high-risk and low-risk preterm neonates in NICU. Four clinical conditions were considered: two Low Risk (LR) and two High Risk (HR), LR1- Appropriate Gestational Age (AGA), LR2- Intrauterine Growth Restriction (IUGR), HR1-Respiratory Distress Syndrome (RDS), and HR2- Premature Rupture of Membranes (PROM). An overall cry unit of 800 (n=20 per condition) was used for the proposed study. After appropriate pre-processing, Bark Frequency Cepstral Coefficient (BFCC) was estimated using three methods. Schroeder, Zwicker and Terhardt; and Transmiller; and a non-linear Support Vector Machine (SVM) Classifier were employed to discriminate low-risk and high-risk groups. From the simulation results, it was observed that sensitivity specificity and accuracy of 91.47%, 91.42%, and 92.9% respectively were obtained using the BFCC estimated for classifying high risk and low risk with SVM classification.

Classification of EMG Signals Using Eigenvalue Decomposition-Based Time-Frequency Representation

Electromyogram (EMG) signals are commonly used by doctors to diagnose abnormality of muscles. Manual analysis of EMG signals is a time-consuming and cumbersome task. Hence, this chapter aims to develop an automated method to detect abnormal EMG signals. First, authors have applied the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT) method to obtain the time-frequency (TF) representation of motor unit action potentials (MUAPs) extracted from EMG signals. Then, the obtained TF matrices are used for features extraction. TF matrix has been sliced into several parts and fractional energy in each slice is computed. A percentile-based slicing is applied to obtain discriminating features. Finally, the features are used as an input to the classifiers such as random forest, least-squares support vector machine, and multilayer perceptron to classify the EMG signals namely, normal and ALS, normal and myopathy, and ALS and myopathy, and achieved accuracy of 83%, 80.8%, and 96.7%, respectively.

Measuring Consciousness in the Clinic

We don't doubt for a moment that we are conscious, but what is ‘Consciousness'? Understanding consciousness, its nature, and characteristics has remained a hard problem for several centuries. While philosophers, neuroscientists, physicists, psychologists, and psychiatrists grapple with this hard problem, clinicians are in need of a practical way to ‘measure' consciousness (or its surrogate). Determining whether a patient is conscious or not, and measuring the degree of consciousness, could be critical and potentially life-saving in a clinical scenario. In this chapter, we will review recent scientific approaches for modelling and measuring consciousness, and their clinical applications with an emphasis on a host of issues (theoretical, philosophical, methodological, technological, & clinical) and challenges that need to be satisfactorily and convincingly addressed going forward.

Biomedical Nanotechnology

This chapter aims to provide an overview of recent studies in the field of biomedical nanotechnology, which is described as the combination of biology and nanotechnology. The field includes innovations such as the improvement of biological processes at the nanoscale, the development of specific biomaterials, and the design of accurate measurement devices. Biomedical nanotechnology also serves areas like the development of intelligent drug delivery systems and controlled release systems, tissue engineering, nanorobotics (nanomachines), lab-on-a-chip, point of care, and nanobiosensor development. This chapter will mainly cover the biomedical applications of nanotechnology under the following titles: the importance of nanotechnology, the history of nanotechnology, classification of nanostructures, inorganic, polymer and composite nanostructures, fabrication of nanomaterials, applications of nanostructures, the designs of intelligent drug delivery systems and controlled release systems, bioimaging, bioseparation, nano-biomolecules, lab-on-a-chip, point of care, nanobiosensor development, tissue engineering and the future of biomedical nanotechnology.

ENT Endoscopic Surgery and Mixed Reality

Minimally invasive otorhinolaryngology surgery uses a system that consists of an endoscope, microscope, high-resolution display, and several surgical tools to perform procedures of the Ear, Nose and Throat (ENT) up to the upper Oesophagus. The complexity, and number of systems used, forces the surgeon to focus on multiple factors rather than exclusively on the procedure. This chapter focuses on the development of a system integrating the endoscopic feed with a Mixed Reality (MR) headset. For that, the visual data stream from an endoscopy system is integrated with an MR head-mounted device. An application was developed using Unity, Visual Studio, and Windows 10 SDK. The application also had the ability to access pre-operative images through its Graphical User Interface, and was integrated with the endoscopic feed wirelessly over a local area network. The application was tested in an educational abdominal phantom. The goal was to streamline the surgeon's focus more on the patient and to provide access to pre-operative images for in-procedure comparison at their fingertips.