Biomedical signals can arise from one or many sources including heart, brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal like electroencephalogram (EEG), electrocardiogram (ECG), etc. The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. T...

Biomedical signals can arise from one or many sources including heart, brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The analysis of these signals is important both for research and for medical diagnosis and treatment. The applications of Independent Component Analysis (ICA) to biomedical signals is a rapidly ...

Most of existing biomedical signal measurement devices measure and evaluate biomedical signals only in a single device. Also, even if the device shows multi-functional characteristics, those biomedical signals can be measured by the selection of users. In this paper, a wristband-style biomedical signal measurement device for u-healthcare systems is implemented to solve the problem mentioned abo...

RFID is evolving as a major technology enabler for identifying and tracking goods. RFID applications in biomedical area not only need to detect but also require monitoring and transmitting vital signals like heartbeat. The basic building blocks of an RFID tag for biomedical application are studied. This paper mainly focuses on the design and optimization of sensor that could be easily integrate...

This paper presents a digital signal processing tool developed using Matlab, which provides a very lowcost and effective strategy for analog-to-digital conversion of legated paper biomedical maps without requiring dedicated hardware. This software-based approach is particularly helpful for digitalizing biomedical signals acquired from analogical devices equipped with a plottingter. Albeit signa...

The automated analysis of the electrocardiogram (ECG) is an important part of the general problem of interpretation of biomedical signals. First, ECG signal evaluation is known to be one of the most informative and significant tools not only for cardiac diagnostics but also for correlative examination of the state of other systems in the body. Secondly, many approaches developed for analysis of...

Quantiication of individual magnetic resonance spectroscopy (MRS) signals modeled as a sum of exponentially damped sinusoids, is possible using interactive nonlinear least-squares tting methods which provide maximum likelihood parameter estimates or using fully automatic, but statistically suboptimal black-box methods. In kinetic experiments consecutive time series of MRS spectra are measured i...