The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. A...

Objective: To investigate a belt-type, biomedical mobile device capable of measuring patients’ biomedical signals and sending the biomedical data to a remote medical server. This device was designed to measure and record ECG and motion signals continuously for a moving subject and, on in the event of an emergent situation, to notify a remote doctor of the situation by transmitting data on the e...

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarit...

This paper analyses the complexity of multivariate electroencephalogram (EEG) signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT). In the field of multivariate entropy analysis, recent studies have performed analysis of biomedica...

QRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs. LabVIEW ( Laboratory Virtual Instrument Engineering Workbench) is a graphical ...

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corres...

In this paper, independent component analysis (ICA) is used for blind source separation of biomedical signals. Visual and quantitative tests of the ability of ICA to separate signals were performed using a fast ICA algorithm. Results obtained from simulated and FECG signals show that the ICA performance using the whitening matrix of the mixed signals was superior to that of random initial weights.

Accurate and efficient analysis of biomedical signals can be facilitated by proper identification based on their dominant dynamic characteristics (deterministic, chaotic or random). Specific analysis techniques exist to study the dynamics of each of these three categories of signals. However, comprehensive and yet adequately simple screening tools to appropriately classify an unknown incoming b...

Biomedical signals are a kind of signals that are measured from a specific part of the body, for example from the hearth (electrocardiography: ECG), muscles (electromyography: EMG) and brain (electroencephalography: EEG). This kind of signals have a no-stationary behavior, it means the behavior through the time is changing every time window. For this reason, the pre-processing, processing, and ...

Advances in information technology have enabled ubiquitous health monitoring at home, which is particularly useful for patients, who have to live alone. We have focused on the automatic and unobtrusive measurement of biomedical signals and activities of patients. We have constructed wireless communication networks in order to transfer data. The networks consist of Bluetooth and Wireless Local A...