Joachim Taelman

Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis

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.

Influence of Mental Stress on Heart Rate and Heart Rate Variability

Stress is a huge problem in today’s society. Being able to measure stress, therefore, may help to address this problem. Although stress has a psychological origin, it affects several physiological processes in the human body: increased muscle tension in the neck, change in concentration of several hormones and a change in heart rate (HR) and heart rate variability (HRV). The brain innervates the heart by means of stimuli via the Autonomic Nervous System (ANS), which is divided into sympathetic and parasympathetic branches. The sympathetic activity leads to an increase in HR (e.g. during sports exercise), while parasympathetic activity induces a lower HR (e.g. during sleep). The two circuits are constantly interacting and this interaction is reflected in HRV. HRV, therefore, provides a measure to express the activity of the ANS, and may consequently provide a measure for stress. We therefore explored measures of HR and HRV with an imposed stressful situation. We recorded changes in HR and HRV in a group of 28 subjects at rest, and with a mental stressor. The results suggest that HR and HRV change with a mental task. HR and HRV recordings may have the potential, therefore, to measure stress levels and guide preventive measures to reduce stress related illnesses.

Sigh rate and respiratory variability during mental load and sustained attention

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.

Wavelet-Independent Component Analysis to remove Electrocardiography Contamination in surface Electromyography

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.

Textile Integrated Contactless EMG Sensing for Stress Analysis

Stress has become an important issue in todays society. Forty to fifty percent of the work related illnesses are directly or indirectly related to stress. In the ConText project, a biofeedback shirt for daily use is being developed to register muscle activity. The user receives feedback about muscle fatigue and/or the level of stress in order to lower the risk on musculoskeletal disorders. Comfort is an important factor to lower the acceptance threshold to wear the shirt. To achieve optimal comfort, the project aims for unobtrusive measurements with contactless sensors which are textile integrated. Working with contactless sensors induces new challenges for instance the displacement of the sensor in the shirt relative to the anatomical position of the muscles. This could affect the recorded signal and lead to errors in the signal. In this paper, we present the results of the quantification of this misalignment. Secondly, we present the first tests with the embroidered sensor.

Take a deep breath: The relief effect of spontaneous and instructed sighs

Spontaneous sighing is related to subjective relief of negative emotional states. Whether this also applies to instructed sighing is not known. The present study aimed to investigate sEMG and respiratory variability (1) during recovery from mental stress with and without an instructed sigh; (2) before and after spontaneous sighs throughout the experiment. A spontaneous sigh was preceded by increasing sEMG and increasing random respiratory variability, and followed by decreasing sEMG and increased structured correlated respiratory variability. Following an instructed sigh, a smaller reduction in sEMG and an increase in random respiratory variability during recovery from mental stress were observed. Thus, a spontaneous sigh seemed to induce relief. An instructed sigh appeared to inhibit recovery from mental stress.

Time-frequency heart rate variability characteristics of young adults during physical, mental and combined stress in laboratory environment

The goal of this study was to evaluate the changes in heart rate variability (HRV) parameters due to a specific physical, mental or combined load. More specifically, the difference in effect between mental load and physical activity is studied. In addition, the effect of the combined physical and mental demand on the HRV parameters was examined and compared with the changes during the single task. In a laboratory environment, 28 subjects went through a protocol with different types of load (physical and/or mental), each followed by a period of rest. Continuous wavelet transformation was applied to create time series of instantaneous power and frequency in specified frequency bands (LF and HF). HF could distinguish the active conditions from the rest condition, meaning that HRV is sensitive to any change in mental or physical state. Differences in HRV parameters were observed between physical, mental and the combined load. In conclusion, we were able to distinguish between rest, physical and mental condition by combining different HRV characteristics. The addition of a mental load to a physical task had an extra effect on the HRV characteristics.

Estimation of Muscle Fatigue Using Surface Electromyography and Near-Infrared Spectroscopy

This study looks at various parameters, derived from surface electromyography (sEMG) and Near Infrared Spectroscopy (NIRS) and their relationship in muscle fatigue during a static elbow flexion until exhaustion as well as during a semidynamic exercise.We found a linear increasing trend for a corrected amplitude parameter and a linear decreasing slope for the frequency content of the sEMG signal. The tissue oxygenation index (TOI) extracted from NIRS recordings showed a four-phase response for all the subjects. A strong correlation between frequency content of the sEMG signal and TOI was established. We can conclude that both sEMG and NIRS give complementary information concerning muscle fatigue.

Detection Algorithm for Single Motor Unit Firing in Surface EMG of the Trapezius Muscle

BACKGROUND Work-related musculoskeletal disorders (MSD) of the neck and the shoulders are a growing problem in society. An interesting pattern of spontaneous muscle activity, the firing of a single motor unit, in the trapezius muscle is observed during a laboratory study in a rest state or a state with a mental load. OBJECTIVE In this study, we report on the finding of the single motor unit firing and we present a detection algorithm to localize these single motor unit firings. METHODS A spike train detection algorithm, using a nonlinear energy operator and correlation, is presented to detect burst of highly correlated, high energetic spike-like segments. RESULTS This single motor unit was visible in 65% of the test subjects on one or both trapezius muscles although there was no change in posture of the test subjects. All the segments in the data that were determined as single motor unit firings were detected by the algorithm. DISCUSSION The physiological meaning of this firing pattern is a very low and subconscious contraction of the muscle. A long-term contraction could lead to the exhaustion of the muscle fibers, thus resulting in musculoskeletal disorders. The detection algorithm is able to localize this phenomenon in a sEMG measurement. The ability of detecting these firings is helpful in the research of its origin. CONCLUSION The detection algorithm can be used to gain insight in the physiological origin of this phenomenon. In addition, the algorithm can also be used in a biofeedback system to warn the user for this undesired contraction to prevent MSD.