This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Bo...

It is well known that biomedical signals carry important information about the behavior of the living systems under study. With the analysis of the Electrocardiogram (ECG) signal it may be possible to predict heart problems or monitor patient recovery after a heart intervention. A proper processing of these signals enhances their physiological and clinical information. The quality of biomedical...

Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties these indicate the nature interaction dynamics among maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in structure which makes isolating and ability to differentiate between them, indispensable. Computer aid...

In bio-medical signals, removal of noises is one of the major issues. In recent years, adaptive filtering plays an efficient role in processing and analysis of biomedical signals. An adaptive filter allows to detect time varying potentials and to track out the dynamic variations of the signals and it modify their behavior according to the input. This paper focuses on the enhancement of adaptive...

Time-frequency (TF) representations of time series are intrinsically subject to the boundary effects. As a result, structures signals that highlighted by garbled when approaching boundaries TF domain. In this paper, for purpose real-time information acquisition nonstationary oscillatory series, we propose numerically efficient approach reduction such The solution relies on an extension analyzed...

An analytical model which predicts the attenuation of ultrawide-band (UWB) signals as they traverse various inhomogeneous tissues is presented. The model provides a computationally efficient method of determining the frequency-dependent losses encountered by electromagnetic radio frequency (RF) signals used to communicate with biomedical implants. Classic transmission line theory is employed to...

Electromyographic control is a technique that involve with the detection, processing and classification of the electromyography signal that could be applied in human-assisting robots, prosthesis application or rehabilitation devices. Electromyographic signals can be used in biomedical engineering and/or rehabilitation field, as potential sources of control for prosthetics and orthotics. In such...

Abstract: Biomedical signals are commonly recorded with many kinds of noise. One of these is a waveform of the electrical activity of muscles. This “natural” distortion is usually modelled with a white Gaussian noise. In order to suppress such noise a weighted myriad filter is applied. The weighted myriad filter belongs to a class of non-linear filters and requires knowledge about noise impulsi...

In the healthcare industry, the ability to monitor patients via biomedical signals assists healthcare professionals in detecting early signs of conditions such as blocked arteries and abnormal heart rhythms. Using data clustering, it is possible to interpret these signals to look for patterns that may indicate emerging or developing conditions. This can be accomplished by basing monitoring syst...

The paper analyses the possibility to recover different biomedical signals if limited number of samples is available. Having in mind that monitoring of health condition is done by measuring and observing key parameters such as heart activity through electrocardiogram or anatomy and body processes through magnetic resonance imaging, it is important to keep the quality of the reconstructed signal...