Body Area Networks

Recent technological advances in integrated circuits, wireless networks, and physiological sensing have enabled miniature, lightweight, low power, intelligent monitoring devices to be integrated into a Body Area Network (BAN). This new type of technology hold much promise for future patient health monitoring. BANs promise inexpensive, unobtrusive, and unsupervised ambulatory monitoring during normal daily activities for long periods of time. However, in order for BANs to become ubiquitous and affordable, a number of challenging issues must be resolved, such as integration, standardisation, system design, customisation, security and privacy, and social issues. This paper presents an overview of many of these issues and indeed the background and rationale of body area networks

An Efficient Cardiac Signal enhancement using Time-Frequency Realization of leaky Adaptive Noise Cancelers for Remote heath monitoring systems

Nowadays telecardiology is an important tool in cardiac diagnosis from a remote location. During Electrocardiogram (ECG) or Cardiac Signal acquisition several artifacts strongly affect the ST segment, degrade the signal quality, frequency resolution, produce large amplitude signals in ECG that can resemble PQRST waveforms and mask the tiny features that are important for clinical monitoring and diagnosis. So the extraction of high-resolution cardiac signals from recordings contaminated with artifacts is an important issue to investigate. In this paper, various novel block based time–frequency domain adaptive filter structures for cardiac signal enhancement are presented. These filters estimate the deterministic components of the cardiac signal and remove the noise component. The Block Leaky Least Mean Square (BLLMS) algorithm, being the solution of the steepest descent strategy for minimizing the mean squared error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. To improve the filtering capability some variants of BLLMS, Block Normalized LLMS (BNLLMS) and Block Error Normalized LLMS (BENLLMS) algorithms are implemented in both time domain (TD) and frequency domains (FD). Finally, we have applied these algorithms on real cardiac signals obtained from the MIT-BIH data base and compared their performance with the conventional LLMS algorithm. The results show that the performance of the block based algorithms is superior to the LLMS counterparts in terms of signal to noise ratio improvement (SNRI), excess mean square error (EMSE) and misadjustment (M). Among all the algorithms FDBENLLMS achieves higher SNRI than other techniques. These values are 25.8713 dB, 20.1548 dB, 21.6718 dB and 20.7131 dBs for power line interference (PLI), baseline wander (BW), muscle artifacts (MA) and electrode motion artifacts (EM) removal

Denoising Artifacts from Cardiac Signal Using Normalized Variable Step Size LMS Algorithm

In this paper, an efficient Error Data Normalized Variable Step Size Least Mean Square (EDNVSSLMS) adaptive algorithm is presented to enhance the quality of an Electrocardiogram (ECG) signal. Due to physiological and non-physiological effects, ECG signals usually undergo numerous artifacts such as Baseline Wander, Muscle artifact, Power Line Interference and Electrode Motion artifacts. The proposed EDNVSSLMS algorithm de-noise these artifacts with better Peak Signal to Noise Ratio, misadjustment and convergence rate compared to other LMS based algorithms, while preserving important clinical wave features morphologies. Also, based on o EDNVSSLMS algorithm, we implemented sign and block based EDNVSS algorithms. Finally we have applied these algorithms to ECG signals corrupted with noise. The performance results shows that Block Based EDNVSSLMS algorithm gives better elimination of noises in the ECG signal with less misadjustment and high peak to signal noise ratio. For less computational complexity and for fast communication of the signal is main, then we select Sign Regressor EDNVSS algorithm

Design Simulation and Analysis of U-Shaped and Rectangular MEMS Based Triple Coupled Cantilevers

235-238In this paper, we have proposed a new shape Micro-Electromechanical Systems (MEMS) based triple coupled Cantilever sensor, named as U-Shaped Cantilever. We have designed and simulated a U-Shaped MEMS based micro-cantilever made up of P-Silicon (Polycrystalline, Lightly doped) in COMSOL multiphysics. U-Shaped single beam Cantilever is designed with the dimensions of 100µm*20µm*2µm. U-shaped triple coupled Cantilever is designed with the dimensions of 20µm*120µm*2µm, 100µm*20µm*2µm. The simulation results like displacement, Eigen-frequency, surface stress, temperature, measurements of the U-shaped triple coupled cantilever is compared with rectangular triple coupled cantilever

Synthesis and antidiabetic activity of 2-amino [5'(4-sulphonylbenzylidine)-2,4-thiazolidinedione]-7-chloro-6-fluorobenzothiazole

2404-2408A new series of 2-amino[5'(4-sulphonylbenzylidine)-2,4-thiazolidinedione]-7-chloro-6-fluorobenzothiazole were synthe­sized. The structures of the compounds were confirmed by UV-Vis, IR and NMR spectroscopy. The title compounds were screened for their antidiabetic activity on albino rats

Efficient block processing of long duration biotelemetric brain data for health care monitoring

In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS, which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence