Intelligent Biometric System Using Soft Computing Tools

Biometric Systems verify the identity of a claimant based on the person’s physical attributes, such as voice, face or fingerprints. Its application areas include security applications, forensic work, law enforcement applications etc. This work presents a novel concept of applying Soft Computing Tools, namely Artificial Neural Networks and Neuro-Fuzzy System, for person identification using speech and facial features. The work is divided in four cases, which are Person Identification using speech biometrics, facial biometrics, fusion of speech and facial biometrics and finally fusion of optimized speech and facial biometrics.

Regional and Community Health Information Exchange in the United States

A fundamental premise of continuity in patient care and safety suggests timely sharing of health information among different providers at the point of care and after the visit. In most healthcare systems, this is achieved through exchange of written medical information, phone calls and conversations. In an ideal world, this exchange of health information between disparate providers, healthcare systems, laboratories, pharmacies and payers would be achieved electronically and seamlessly. The potential benefits of electronic health exchange are improved patient care, increased efficiency of the healthcare system and decreased costs. The reality is that health information is electronically exchanged only to a limited extent within local communities and regions, much less nationally and internationally. One main challenge has been the inability of health information exchange organizations to develop a solid business case. Other challenges have been socio-political in that data ownership and stewardship have not been clearly resolved. Technological improvements over the past 20 years have provided significant advances towards safe and secure information exchange. This chapter provides a general overview of community health information exchange in the United States of America, its history and details of challenges faced by stakeholders. The lessons learned from successes and failures, research and knowledge gaps and future prospects are also discussed. Current and future technologies to facilitate and invigorate health information exchange are highlighted. Two examples of successful regional health information exchanges in the US states of Utah and Indiana are highlighted.

Social and Ethical Concerns of Biomedical Engineering Research and Practice

Biomedical engineering is an advanced and relatively new field in the healthcare sector. Owing to the very nature of the various professional challenges faced by healthcare professionals, the moral and ethical values seem to have taken the backburner. The factors contributing to it may include a sound knowledge of the healthcare professional on the legally-permissible ethical values, and the desperate situations requiring precise split-moment decision-making. No technological advancement without a human face is worth it, and hence, during the course of the degree, a biomedical engineering student needs to be exposed to various ethical issues through theory, live cases and demonstrations. Being intrinsically multi- and inter-disciplinary, biomedical engineering lacks precise ethical rules that delineate and delimit professional responsibility, thus blurring the ethical understanding of biomedical engineering. The solution seems to lie in giving due place to human virtues. In the coming days, bioethical issues are expected to be increasingly complicated and dominating the decision-making process owing to the advancements in sciences, and the ever-complicated cases handled by healthcare professionals. A global healthcare and ethics-related online open-access portal may serve as a common platform for all the stakeholders in the interest and ethical growth of biomedical engineering in particular and medical sciences in general.

Microfluidic Applications in Vascular Bioengineering

Microfluidics is the manipulation and control of fluids in small scale, and has heralded a new age in science as evidenced by the rapid increase in the amount and quality of academic and industrial research output in this area in the recent times. Microfluidics has shown tremendous promise in both fundamental and applied research in the field of vascular bioengineering. In this review, we outline the basic principles of microfluidic flow and fabrication techniques, and describe the recent advances in the applications of microfluidic devices in diagnostic and prognostic vascular bioengineering. The field is still in its infancy and has a great potential for research and development as it matures to deliver commercially viable products. This review, focusing on the current status of microfluidic applications to diagnose and treat blood-related disorders, should be a valuable and opportune addition to the literature of interest to both academia and industry.

Handling Large Medical Data Sets for Disease Detection

The breakthrough in the field of intelligent systems has spread its fruits to the field of biomedical engineering as well; where a series of models are being applied to automatically detect diseases based on some parameters or inputs. The continuous research in this field has resulted in a large amount of database being created for many diseases which becomes very difficult to train. Also the number of attributes is under constant rise. This increases the dimensionality of the problem and ultimately leads to poor performance. In this chapter we deal with the methods to handle these situations. We discuss the mechanism to divide data between different sub-systems. We also discuss the method of division of the attributes to reduce the training time and complexity. The resultant systems are able to train better due to low computational cost and hence give better performance. We validated this with the Breast Cancer database from the UCI Machine Learning repository and found our algorithm optimal.

Intelligent Decision Support System for Fetal Delivery using Soft Computing Techniques

In the present work an attempt is made to develop an intelligent Decision support system (IDSS) using the pathological attributes to predict the fetal delivery to be done normal or by surgical procedure. The pathological tests like Blood Sugar (BR), Blood pressure (BP), Resistivity Index (RI) and systolic / Diastolic (S/P) ratio will be recorded at the time of delivery. All attributes lie within a specific range for normal patient. The database consists of the attributes for cases 2 (i.e. normal and surgical delivery). Soft computing technique namely Artificial Neural Networks (ANN) are used for simulator. The attributes from dataset are used for training & testing of ANN models. Three models of ANN are trained using Back-Propagation Algorithm (BPA), Radial Basis Function Network (RBFN), Learning Vector Quantization Network (LVQN) and one hybrid approach is Adaptive Neuro-Fuzzy Inference System (ANFIS). The designing factors have been changed to get the optimized model, which gives highest recognition score. The optimized models of BPA, RBFN, LVQN and ANFIS gave accuracies of 93.75, 99.00, 87.50 and 99.50% respectively. Hence in our present research the ANFIS is the model whom efficiency and result are best .The ANFIS is the best network for mentioned problem. This system will assist doctor to take decision at the critical time of fetal delivery.

Methods for Improving Foot Displacement Measurements Calculated from Inertial Sensors

The use of inertial sensors to measure human movement has recently gained momentum with the advent of low cost micro-electro-mechanical systems (MEMS) technology. These sensors comprise accelerometer and gyroscopes which measure accelerations and angular velocities respectively. Secondary quantities such as displacement can be obtained by integration of these quantities, a method which presents challenging issues due to the problem of accumulative sensor errors. This chapter investigates the spectral evaluation of individual sensor errors and looks at the effectiveness of minimizing these errors using static digital filters. The primary focus is on the derivation of foot displacement data from inertial sensor measurements. The importance of foot, in particular toe displacement measurements is evident in the context of tripping and falling which are serious health concerns for the elderly. The Minimum Toe Clearance (MTC) as an important gait variable for falls-risk prediction and assessment, and therefore the measurement variable of interest. A brief sketch of the current devices employing accelerometers and gyroscopes is presented, highlighting the problems and difficulties reported in literature to achieve good precision. These have been mainly due to the presence of sensor errors and the error accumulative process employed in obtaining displacement measurements. The investigation first proceeds to identify the location of these sensor errors in the frequency domain using the Fast Fourier Transform (FFT) on raw inertial sensor data. The frequency content of velocity and displacement measurements obtained from integrating the inertial data using a well known strap-down method is then explored. These investigations revealed that large sensor errors occurred mainly in the low frequency spectrum while white noise exists in all frequency spectra. The efficacy of employing a band-pass filter to remove a large portion of these errors and their effect on the derived displacements is elaborated on. The cross-correlation of the FFT power spectra from a highly accurate optical measurement system and processed sensor data is used as a metric to evaluate the performance of the band-pass filter at several stages of the processing stage. The motivation is that a more fundamental method would require less computational demand and could lead to more efficient implementations in low-power and systems with limited resources, so that portable sensor based motion measurement system would provide a good degree of measurement accuracy.

Analysis of Biological Effect of AC-DC Electromagnetic Fields using the Lorenz Model

Electromagnetic fields (EMF) are essential to various applications directly involving humans. Fears about the biological effect of exposure to electromagnetic fields drive enormous research into this area. This research generates conflicting results, and consequently, uncertainty regarding possible health effects. This chapter studies a nonlinear Lorenz model describing interactions among charged particles and combined alternating (AC: alternating current) and static (DC: direct current) electromagnetic fields, for various combinations of frequencies, field strengths and relative angle (?) between the AC and DC magnetic fields. We investigate the effect on charged particles of three possible combinations of alternating and static electromagnetic fields: (i) AC electric field and DC magnetic field (ii) AC magnetic field and DC magnetic field (iii) AC electric field and AC and DC magnetic field. Then the behavior of the particle in these fields with different initial conditions and strong directional effects is observed when the angle between AC and DC magnetic fields is varied. The results show that the cyclotron resonance frequency is affected by charged particles’ initial position and initial velocity. Further, we observe strong effects of electric and magnetic fields on a charged particle in a biological cell with initial position and initial velocity.

Cryopreservation of Spermatozoa

Cryopreservation of sperm has many applications in agriculture, biotechnology, and clinical medicine. The spermatozoon is a very specialized cell that loses the ability of biosynthesis, repair, growth and cell division during the final phase of spermatogenesis. Cryopreservation of sperm generally requires a reduction or arrest of the metabolism of cells, thereby prolonging their life. Semen samples of mammalian species are diluted with a suitable diluent [containing a complex extender (e.g., egg-yolk, milk, milk-whey), cryoprotectant (e.g., glycerol)] and processed through different freezing protocol prior to storage in liquid nitrogen (-196°C). Despite the use of complex media and cryoprotectants, a substantial portion of the cells die during freezing and thawing (recovery rate do not exceeds more than 50%). As the complex media contain large numbers of undefined biomolecules (proteins, lipids, carbohydrates), it is difficult to analyze the beneficial/detrimental effects of a particular compound on sperm cryopreservation. In the present book chapter we have briefly discussed the knowledge and limitations of the current semen cryopreservation technology and mainly focused on the development of a simple cryopreservation model using chemically-defined medium and goat cauda-epididymal sperm. Using this model system, several novel cryoprotectants have been identified and biochemical basis of sperm membrane damage during cryopreservation has been investigated.

Techniques for Decomposition of EMG Signals

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.