Non-Invasive Cuffless Blood Pressure Monitoring System

These days most of the Blood Pressure (BP) measuring devices are having inflatable cuff that is needed to be occluded on the patient's arm for measuring blood pressure. This technique is not suitable in cases where continuous measurement of BP is required. Therefore, this work is aimed at designing of non-invasive and continuously monitors the blood pressure by using Pulse Transit Time (PTT) technique. For taking out PTT both of the signals are extracted from the body of the patient with the help of bio sensors i.e. Electrocardiogram (ECG) sensor and Photoplethysmogram (PPG) sensor. PTT was measured by taking the peak to peak time difference of ECG signal and PPG signal and this PTT is indirectly correlated with blood pressure, based on which Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) is calculated.

Medical Image Enhancement Using Edge Information-Based Methods

Medical image enhancement improves the quality and facilitates diagnosis. This chapter investigates three methods of medical image enhancement by exploiting useful edge information. Since edges have higher perceptual importance, the edge information based enhancement process is always of interest. But determination of edge information is not an easy job. The edge information is obtained from various approaches such as differential hyperbolic function, Haar filters and morphological functions. The effectively determined edge information is used for enhancement process. The retinal image enhancement method given in this chapter improves the visual quality of the vessels in the optic region. X-ray image enhancement method presented here is to increase the visibility of the bones. These algorithms are used to enhance the computer tomography, chest x-ray, retinal, and mammogram images. These images are obtained from standard datasets and experimented. The performance of these enhancement methods are quantitatively evaluated.

Analysis of HRV during the Menstrual Cycle and Postmenopause

Every woman experiences an extensive fluctuation in HRV during her menstrual cycle and even after menopause. A woman who lives long enough will experience menopause as a normal physiologic event. The study of the influence of premenopausal and postmenopausal symptoms on HRV has not been adequate. During this period, health management is an important factor to be considered as it affects the entire quality of women life. Many women having diverse physical, behavioural as well as psychological symptoms at the time of menopause and even after the menopause. Thus, HRV analysis is an appropriate tool to examine the physiological effects of the menstrual cycle in young healthy women and the postmenopause in old women. This chapter intends to study the influence of the menstrual cycle, and postmenopause on autonomic modulation of heart with a perspective of signal processing approach.

Electromyogram and Inertial Sensor Signal Processing in Locomotion and Transition Classification

Signal processing in biomedical engineering is essentially required for classification while serving mainly two aims. The first is noise removal and the second is signal representation. Signal representation deals with transforming the signal in such a way that the signal is most informative in that particular domain for the application at hand. This chapter will describe signal processing methods like spectrogram with specific applications to locomotion and transition classification using Electromyography (EMG) data. A wavelet analysis application on foot acceleration signals for automatic identification of toe off in locomotion and the ramp transition is also shown. Finally, the performance of EMG and accelerometer performance across different time windows of a gait cycle in locomotion and transition classification is presented with an emphasis on fusing the data from both sensors for better classification.

Success Dimensions of ICTs in Healthcare

During the last decades, the call for Information and Communication Technologies (ICTs) in healthcare has been augmented to endow with healthcare services at a global scale and to trim down medical errors that cost human lives. Enriched with explosive computing and high communicating power, ICTs like Internet, mobile telephony, and other enabled gadgets plays a prominent role in our day-to-day activities. With the potential to provide access to service for patients in difficult-to reach areas and facilitating medical record keeping and information sharing are the main considerations of leveraging ICTs in realm of clinical care. The insurgence of these innovating technologies into healthcare sectors is not only blurring the boundaries for the emergence of other new technologies but also causing a paradigm shift in providing acute and preventative care in public health. The main goal of this chapter is to offer readers an insight into how the emergence of ICTs have transformed healthcare sector by delivering cost-efficient and quality of care to patients.

Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm

Parameter estimation is a central issue in mathematical modelling of biomedical systems and for the development of patient specific models. The technique of estimating parameters helps in obtaining diagnostic information from computational models of biological systems. However, in most of the biomedical systems, the estimation of model parameters is a challenging task due to the nonlinearity of mathematical models. In this chapter, the method of estimation of nonlinear model parameters from measurements of state variables, using the extended Kalman filter, is extensively explained using an example of the three-dimensional model of the HIV/AIDS system.

Analysis of Electrocardiogram Data Compression Techniques

In this Chapter, a MATLAB-based approach is presented for compression of Electrocardiogram (ECG) data. The methodology employs in three different domains namely direct, transformed and parameter extraction methods. The selected techniques from direct ECG compression methods are TP, AZTEC, Fan, and Cortes. Moreover selected techniques from transformed ECG compression methods are Walsh Transform, DCT, and Wavelet transform. For each of the technique, the basic implementation of the algorithm was explored, and performance measures were calculated. All 48 records of MIT-BIH arrhythmia ECG database were employed for performance evaluation of various implemented techniques. Moreover, based on requirements, any basic techniques can be selected for further innovative processing that may include the lossless encoding.

Single Electronics for Biomedical Applications

The nanoelectronic circuits based on single electronics would revolutionise the new generation electronic bio-medical gadgets. The high speed nanoelectronic devices would make these gadgets faster and more accurate. The nanoelectronic integrated circuits would be a boon for power saving along with advanced portability. As the scaling down of silicon based integrated circuits is limited in nanometer regime alternative materials like organic molecules, polymers, carbon nanotubes and graphene are focal point of research. These materials exhibit various electrical, electronic and mechanical properties, flexibility being one of very significant ones. Flexible nanelectronic integrated circuits would make biomedical applications very patient friendly. The in-vivo examination and diagnosis would be less injurious to the body. Also the flexible nature will increase the maneuverability of the device by the operator. It will improve the targeted diagnosis and targeted drug delivery procedures. This would further facilitate system-on- chip (soc) that will integrate multiple biomedical signal acquisition (ECG, EEG, EP, and respiration-related signals) with on-chip digital signal processing.

A Review on Noninvasive Beat-to-Beat Systemic and Pulmonary Blood Pressure Estimation through Surrogate Cardiovascular Signals

Blood pressure monitoring is essential in hospital and home monitoring scenarios, with applications requiring on-line beat-to-beat blood pressure estimation, such as tele-monitoring of neurally mediated syncope. This chapter presents a comprehensive review of investigated approaches and reported performance, using different noninvasive correlates of the circulatory and cardiovascular system. Papers of interest were located in Scopus, IEEE Xplore and PubMed databases. The resulting pool of papers was then methodologically reviewed using 5 thematic taxonomies developed: 1) pulse arrival time and pulse transit time, 2) vascular transit time, 3) RS2 time, 4) heart sound characteristics, 5) PPG characteristics. The status of evidence in the literature demonstrates that cardiovascular signals such as the electrocardiogram, photoplethysmogram, and phonocardiogram contain important information for the estimation of blood pressure. Still, there are open issues regarding the validity, reliability and stability of these methods.

Optimized Clustering Techniques with Special Focus to Biomedical Datasets

The clinical data including clinical test results, MRI images and drug responses of patients are documented and analyzed with machine learning and data mining tools. The scale and complexity of these datasets is a big challenge to machine learning and data mining community as the data is of mixed type. The extraction of meaningful or desired information from these datasets provides knowledge in decision making process which in turn helps for the diagnosis and treatment of the diseases. Biomedical datasets are a collection of data with diverse types as it involves images, clinical studies, statistical reports etc. The recent researches have focused on different clustering and classification methods to manage and analyze the biomedical datasets. The objective of this chapter is to cluster or classify the patterns of interest from Brain MRI images, Liver disorder and Breast cancer datasets using efficient clustering methodologies. Among the different algorithms in data mining for clustering, classification, visualization and interpretation, K Means, Fuzzy C Means and Neural Networks(NN) are frequently used for clustering and classification of biomedical datasets. The performance of these methods are greatly influenced by the initialization of K value and its convergence speed. This chapter discusses about FCM and K Means clustering methods and its optimization with meta heuristics such as Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO). The experimental section of this paper exhibits analysis in terms of Intra cluster distances, elapsed time and Davis Bouldin Index (DBI).