Electrospinning

In this chapter authors explain electrospinning technique and how to involve electrospinning technique in biomedical engineering. It is a materials processing technique that uses an electric field to draw a polymer solution into ultra-fine fibers.   Further, this chapter aims to provide important information to researchers who aim to use electrospinning in their research. The electrospinning technique was invented a few decades ago and has recently been modified for biomedical applications.

Using Ontologies in eHealth and Biomedicine

The goal of the chapter is to enhancement the medical system performance and provides a overview of available healthcare system. It is also provide a idea for simulation of different results, activities, systems, groups, institutions, theories, the benefits ontologies bring to the e-Health and biomedicine domains and the effort already given in this respect. This system is seen as an application of ontologies in the occupational health domain and representation and integration of the semantic and modeling layers of a system based on ontologies for the prevention of occupational risks.

Hemoglobin Level Analysis in Hemodialysis Patients Treated With Erythropoiesis Stimulating Agents

In this chapter authors try to develop an expert system with the help of neural network method like Organizing Maps (SOMs) for hemodialysis patient.  Neural network models play a very important role for data analysis of hemodialysis patients with end-stage renal disease.  There are two main goals: firstly, the knowledge extraction from a database using Self-Organizing Maps (SOMs); and secondly, to provide an accurate prediction of Hb levels next month.

A Step Towards Characterization of Surgical Actions Involved in Mastoidectomy

In this chapter authors give an idea for automation of open surgery.  Mastoidectomy is a core surgical procedure in otologic surgery.  Here they present a novel approach to developing methods for parsing raw data (position and orientation of the surgical tool and end-effector force) into a sequence of surgical actions (APs) that can be used by a robot in the future.

Master And Slave Transluminal Endoscopic Robot (MASTER) for Natural Orifice Transluminal Endoscopic Surgery (NOTES)

In this chapter, authors try to explain and develop a robot for medical application. Some robot is already working with doctor and some is under improvement stage.  Master and Slave Transluminal Endoscopic Robot (MASTER) with 9 degree of freedom (DOF) end effectors which are long and flexible so as to enhance endoscopic procedures and notes.

ECG Data Analysis

In this chapter authors explain an idea for automation of heart failure with the help of ECG signals. An electrocardiogram (ECG) is a test that records the electrical activity of the heart. A brief description on automatic classification techniques is also given. ECG being the most vital physiological signal, its acquisition technique, noise and artifacts elimination methodologies are discussed in this chapter.

Breast Cancer

In this chapter authors did one technological survey for breast cancer and found very large scale of people working on this field.  So many techniques are used in that field and they found that Breast cancer is the second leading cause of cancer deaths in women and occurring one out of eight women in worldwide. They found that three techniques like Mammography, FNA and Open surgery are mostly seen in the literature of breast cancer.

Clinical Decision Support System To Prevent Toxicity In Patients Treated With Digoxin

In this chapter, authors develop a system for prevention and detection of congestive heart failure and fibrillation. Due to its narrow therapeutic range more than 10% of the patients treated with DGX can suffer toxic effects, but it is estimated that half of the cases of digitalis toxicity could be prevented. Two multivariate models were developed to prevent digitalis toxicity.

An Intelligent Algorithm for Home Sleep Apnea Test Device

In this chapter authors try to develop a system for Sleep apnea with the help of machine learning algorithms using ECG signals.  The application of an intelligent machine learning technique (Support Vector Machines, SVM) to diagnose the patients with sleep apnea syndrome using Electrocardiogram (ECG) signal.  Sleep apnea syndrome is a medical condition caused by sleep apnea which is defined as the cessation of breathing for short periods during sleep.

Hunting Drugs for Potent Antigens in the Silicon Valley

In this chapter author’s represent an idea for development of a model for epitope.  It is also explains the immunological aspects and algorithms applied for epitope selection, with their practical problems.  Current techniques used to model the native state conformation of epitopes are then described with their fundamental problems.  So, the algorithms to validate such models are discussed.  Techniques to screen effective potent drugs against the target epitopes are then considered.