No abstract

In biomedical signal processing, it is often the case that many sources are mixed into the measured signal. The goal is usually to analyze one or several of them separately. In the case of multichannel measurements, several blind source separation techniques are available for decomposing the signal into its components [e.g., independent component analysis (ICA)]. However, only a few techniques have been reported for analyses of single-channel recordings. Examples are single-channel ICA (SCICA) and wavelet-ICA (WICA), which all have certain limitations. In this paper, we propose a new method for a single-channel signal decomposition. This method combines empirical-mode decomposition with ICA. We compare the separation performance of our algorithm with SCICA and WICA through simulations, and we show that our method outperforms the other two, especially for high noise-to-signal ratios. The performance of the new algorithm was also demonstrated in two real-life applications.

The cardiac regulation effects of a mental task added to regular office work are described. More insight into the time evolution during the different tasks is created by using time-frequency analysis (TFA). Continuous wavelet transformation was applied to create time series of instantaneous power and frequency in specified frequency bands (LF 0.04-0.15 Hz; HF 0.15-0.4 Hz), in addition to the traditional linear heart rate variability (HRV) parameters. In a laboratory environment, 43 subjects underwent a protocol with three active conditions: a clicking task with low mental load and a clicking task with high mental load (mental arithmetic) performed twice, each followed by a rest condition. The heart rate and measures related to vagal modulation could differentiate the active conditions from the rest condition, meaning that HRV is sensitive to any change in mental or physical state. Differences between physical and mental stress were observed and a higher load in the combined task was observed. Mental stress decreased HF power and caused a shift toward a higher instantaneous frequency in the HF band. TFA revealed habituation to the mental load within the task (after 3 min) and between the two tasks with mental load. In conclusion, the use of TFA in this type of analysis is important as it reveals extra information. The addition of a mental load to a physical task elicited further effect on HRV parameters related to autonomic cardiac modulation.

Spontaneous breathing consists of substantial correlated variability: Parameters characterizing a breath are correlated with parameters characterizing previous and future breaths. On the basis of dynamic system theory, negative emotion states are predicted to reduce correlated variability whereas sustained attention is expected to reduce total respiratory variability. Both are predicted to evoke sighing. To test this, respiratory variability and sighing were assessed during a baseline, stressful mental arithmetic task, nonstressful sustained attention task, and recovery in between tasks. For respiration rate (excluding sighs), reduced total variability was found during the attention task, whereas correlated variation was reduced during mental load. Sigh rate increased during mental load and during recovery from the attention task. It is concluded that mental load and task-related attention show specific patterns in respiratory variability and sigh rate.

Removing artifacts from biomedical signals, such as surface electromyography (sEMG), has become a major research topic in biomedical signal processing. In electromyography signals, a source of contamination is the electrophysiological signal of the heart (ECG signals). This contamination influences features extracted from the sEMG, especially during low-activity measurements of the muscles such as during mental stress. As the heart is a muscle, the frequency content of the heart signals overlaps the frequency content of the muscle signals, so basic frequency filtering is not possible. In this paper, we present the results of a recently developed algorithm: wavelet-independent component analysis. We compare these results with the widely described algorithm of ECG template subtraction for removing ECG contamination.