Time-frequency heart rate variability characteristics of young adults during physical, mental and
combined stress in laboratory environment
Steven Vandeput1§, Joachim Taelman1,2, Arthur Spaepen2, Sabine Van Huffel1
1Department of Electrical Engineering, ESAT-SCD, Kasteelpark Arenberg 10 box 2446, 3001 Leuven, Belgium
2Department of Kinesiology and Rehabilitation Sciences, Tervuursevest 101, 3001 Leuven, Belgium
§Corresponding author
Email addresses:
SV: steven.vandeput@esat.kuleuven.be JT: joachim.taelman@esat.kuleuven.be AS: arthur.spaepen@faber.kuleuven.be SVH: sabine.vanhuffel@esat.kuleuven.be
Abstract
Objective
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.
Methods
In a laboratory environment, 28 subjects went through a protocol with 3 active conditions (45° shoulder abduction, IQ-test and combination) each followed by a rest condition. 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 – 0.4Hz), in addition to the traditional linear HRV parameters.
Results
Almost all measures (mean RR, rMSSD, HF) could distinguish the active conditions from the rest condition, meaning that heart rate variability is sensitive to any change in mental or physical state. Differences in HRV parameters were observed between physical and mental load. The load in the combined task was higher than in the mental or physical task separately, resulting in a significantly higher heart rate (respectively 82.9 bpm vs. 77.6 bpm vs. 75.7 bpm) and lower vagal modulation (respectively 29.5 ms vs. 32.3 ms vs. 32.9 ms). Mental stress decreased HF power (89.6 ms2 vs. 119.8 ms2 in rest) and caused a shift towards a higher instantaneous frequency in HF band (0.217 Hz vs. 0.206 Hz in rest). LF/HF ratio is increasing monotonously during the physical task (29.4 at minute 1 vs. 43.7 at minute 4) indicating the fast increase in sympathetic dominance.
Conclusions
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.
Background
Since the nineties, markers of stress and other psychosocial factors are associated with coronary disease [1]. In contrast with other lifestyle risk factors, no consensus about stress exists with respect to either definition or measurement. Inevitably, stress is subjective and it can encompass several aspects, from external stressors such as adverse life events, financial problems or job stress to potential reactions such as depression, vital exhaustion, sleeping difficulties or anxiety [2-5]. Forty to fifty percent of all work-related absences are related to stress. The European commission states that this problem leads to losses of 0.5 to 2% of GNP per year [6]. Several studies have shown a link between the level of work stress and disease [7-8].
Stress, here defined as a mismatch between perceived demands and perceived capacities to meet those demands [9], is a psychophysiological phenomenon that changes the physiological balance of amongst others the autonomic nervous system (ANS) [10-11]. The ANS is divided into a sympathetic and parasympathetic or vagal branch. Both components operate simultaneously and balance each other dynamically in normal conditions. When a person is exposed to a stressor, the sympathetic system becomes more activated via the locus coeruleus, resulting in the secretion of the neurotransmitter norepinephrine [5]. The activation of the sympathetic system innervates the production of epinephrine in the adrenal medulla. In addition, the parasympathetic system is shut down. This brings the body in an arousal state, called the fight or flight reaction since Walter Cannon's work on the fight-or-flight response in the 1930s [12]. This result in changes in several physiological systems amongst others increased heart rate (HR) via the stimulation of the sinus node of the heart.
When the stressor disappears, the vagal system takes over and the sympathetic activation disappears. A message is send to the medulla, which responds by releasing
a hormone called acetylcholine. This hormone slows downthe heart rate, delaying the heart's muscle contractions.
Heart rate variability (HRV) refers to alternations in heart beat time-intervals and provides quantitative markers of autonomic regulation [13-14]. Moreover, it is a simple and powerful noninvasive methodology having enormous practical advantages with a minimum of technical constraints, which makes it useful in many applications.
Therefore, HRV has been used to examine the responses to mental and physical demands on the ANS. Expressed physical tasks strongly influence HRV indices related to ANS as shown for static handgrip at 25% of maximal voluntary contraction (MVC) [15], bicycle exercise [16] or static leg extension at 30% MVC [17]. Mental stress in laboratory experiments (cognitive demands, mental arithmetic) has been associated with decreased HRV, indicating a disturbed ANS [18-21]. Many studies only focus on either physical or mental load, but only a few consider both [22,23].
The goal of this study was to evaluate the changes in HRV parameters due to a specific physical, mental or combined load. More specifically, the difference in effect between mental load and physical activity, in literature known to be methodologically difficult [24], 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. However the relative contribution of mental and physical stress on HRV parameters is still not completely clear. We hypothesize a bigger change of the HRV parameters, and therefore autonomic modulation of heart rate, in the combined task compared to a single task. While almost all previous studies were either
interested in the relation between HRV and a questionnaire based stress level or the influence of imposed demands on HRV, this study also investigated the time evolution. Time-frequency analysis, as applied here, was rarely used in literature,
although it enables to study trends within the same condition or transitions between several conditions. Moreover it also compares these trends within one condition as well as between the several conditions. After describing the data and the HRV
methods, results are given for every HRV parameter separately. We then discuss these results by focusing on the differences in HRV characteristics during rest, mental load, physical load and the combination of the latter two.
Methods
Subjects
28 participants were monitored, 15 men and 13 women with mean age of 22 ± 1.96 (19-26) years and an average body mass index of 22.2 ± 0.43(18-29) kg/m2. The study population was limited to healthy students and young people working at the
Katholieke Universiteit Leuven from 18 to 26 years old. No other exclusion criteria were used. Before the experiment, information about neck or shoulder complaints, cardiovascular problems and whether subjects were right- or left handed was noted.
The experiment was approved by the Ethics Committees of the Department of Psychology and of the Faculty of Medical Sciences.
Each subject provided written informed consent before participating. The study complies with the Declaration of Helsinki [25].
Instrumentation
Upon arrival, the test subjects were prepared for the measurements. The hair on the left chest was removed if appropriate and the skin was cleaned. Electrodes (Ag-AgCl, 10 mm diameter, Nikomed, Denmark) were placed on the body to measure
simultaneously the electrocardiogram (ECG) and surface electromyography (sEMG) of the M. Trapezius. ECG (sampling frequency: 1000 Hz) was obtained by two electrodes placed around the heart (on the top of the sternum and on the 8th rib) and one reference electrode (on the bottom of the sternum). sEMG was monitored for analysis of the additional effect of a mental task on muscle activity during physical activity, but the results of the analysis are beyond the scope of this paper. The data were registered with EMG preamplifiers (Mega Electronics Ltd, Finland). These analog signals were amplified and low pass filtered (450Hz). The Daqbook 2005
(IoTech, Ohio, USA) was used to digitize the signals at a frequency of 1000 Hz with 16-bit resolution before storage on a personal computer.
Protocol
In a laboratory environment, the subjects were instructed to perform five tasks according to the following protocol made in collaboration with researchers from the Psychology Department at Katholieke Universiteit Leuven. A first task implied sitting at ease with the hands in the laps. During this rest period (R), a series of relaxing photos was shown on the computer screen. To be sure that the test subject is at full ease, the first rest period lasts for 4 minutes, while the others last for 2 minutes. The final rest condition also lasts for four minutes to end with a full recovered HR. A second task was a postural task (PT) in which the subjects performed a shoulder abduction of 45° with stretched arms during 6 minutes. This prolonged static posture was intended to induce muscle fatigue in the shoulder girdle, which we also
investigated via measuring muscle activity (EMG). However, the latter will be described in another study. A third task consisted of a mental stressor (MT). The subjects were sitting with their hands in their laps and performed a complex and challenging mental task, part 1 of the home version of the MENSA test1. Time to complete the test was limited to 10 minutes and subjects were not informed about the expired time, causing an extra stressor. The combination of a mental and postural task formed the fourth condition (MPT). Here, the subjects performed a shoulder
abduction of 45° with stretched arms and had to solve part 2 of the MENSA test.
Time to complete the MENSA test was again limited to 10 minutes. The subjects answered orally to the experimenter sitting next to them. Finally, after each of the described tasks a self rating (SR) took place. The subjects were asked to rate their feelings orally at that moment on the short version of the Spielberger State-Trait
1 http://www.mensa.be
Anxiety Inventory (STAI) [26]. This provided subject perceptions of their stress or anxiety levels at the instant of rating. The scale comprises 6 terms rated on a scale from 1 to 4: 3 of negative affect (tense, nervous, worried) and 3 of positive affect (calm, content, relaxed).
Tasks 2, 3 and 4 are called the active conditions (PT, MT and MPT). Each active condition was followed by a rest condition and the sequence of these active conditions was fully randomized amongst the participants. While the PT condition had a fixed duration of 6 minutes, MT and MPT were of variable length depending on the speed the subjects solved the IQ questions with a maximum of 10 minutes. The participants were instructed to give the answers of the IQ test orally to the experimenter. Apart from this, the participants were asked not to move and to speak. All tasks were performed with the subject in a sitting position facing the screen to ensure similar effects across tasks on HRV. Table 1 gives an overview of the 4 conditions with the duration and the different tasks.
Heart rate variability analysis
Detection of the R peaks in the ECG signal was done by the Pan-Tompkins algorithm [27], resulting in a RR interval time series, often called tachogram. After checking the data manually for missing and ectopic beats, extra ventricular beats were replaced by a 20%-filter. This means that every RR interval that differ more than 20% from the previous one, is replaced by an interpolated value, defined via spline interpolation over the 5 previous and 5 next intervals [28].
Traditional linear HRV parameters were obtained in agreement with the standards of measurement, proposed by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [14]. Mean and standard
deviation (SDNN) of the tachogram, the square root of the mean of squared differences between consecutive RR intervals (rMSSD) and the percentage of intervals that vary more than 50 ms from the previous interval (pNN50) were calculated in the time domain. After resampling the tachogram at 2 Hz, the power spectral density (PSD) was computed by using fast Fourier transformation. In the frequency domain, low frequency power (LF: 0.04 – 0.15 Hz) and high frequency power (HF: 0.15 – 0.40 Hz), as well as the ratio of low over high frequency power (LF/HF), were calculated. In addition, the power can be expressed in absolute values (ms2) or in normalized units (n.u.). LFnu and HFnu are calculated by dividing
respectively LF and HF by the total power (TP) minus the very low frequency (VLF) power (0.00 – 0.04 Hz).
As some HRV parameters, such as SDNN, depend on the recording length, a
comparison between conditions can only be done by calculating each HRV parameter on segments of equal length. Because the conditions have variable duration, the parameters are calculated in windows restricted to two minutes, which is the duration of the shortest condition in the dataset. The first two minutes of each condition are selected.
To overcome possible non-stationarities in the data and to describe the quick changes in HRV spectra during transients, time-frequency representations (TFR) are used.
This study applies the wavelet decomposition technique [29] resulting in a good frequency resolution at the lowest frequencies and a good time resolution at the highest frequencies. A decomposition of a time signal x(s) with wavelets starts from one mother wavelet, t a, ( )s , that can be shifted (time t) and dilated (scale a). The decomposition is given by next formula, where TFR t a( , ; ) is the wavelet
decomposition:
( , ; ) ( ) t a, ( )
TFR t a x s s ds
with t a, ( )s defined as
1/ 2 , ( )
t a s t
s a
a
where t a, ( )s is the mother wavelet and a the scales related to the frequencies 0 – 0.4 Hz. The Morlet wavelet is selected as mother wavelet as literature showed that this is an appropriate function to study HRV [30].
From this TFR, a time course of spectral parameters can be extracted. The spectral bands are chosen similar to the Task Force description [14], so as to keep the physiological interpretation of the results: LF band (0.04 – 0.15 Hz) and HF band (0.15 – 0.40 Hz). The instantaneous frequency of a signal, calculated as the derivative of the phase of its analytical signal, often produces results that, in some ways, may seem paradoxical [34], and which, in any case, make their physical interpretation difficult. This drawback can be avoided by defining the instantaneous frequency as the mean frequency of the spectrum at each instant [31]. The spectrum is obtained as a section of the time–frequency distribution at this instant:
( , ) ( )
( , )
e
b e
b
n
n n
n n
s n
n n n
f TFR t f f t
TFR t f
with nb, ne respectively the beginning and end frequency of a selected frequency band and fn the frequency representation of the scales a. Each mother wavelet has a central frequency, which can be modified by rescaling the mother wavelet. The scale a is thus directly related to a frequency. The instantaneous frequency is calculated in the high
frequency band (fHF), while the analysis in the low frequency band is neglected because no agreement exist on physiological meaning of this frequency. Analogously, in the two frequency bands the power can be calculated by integrating the
spectrogram, expressed in absolute values or normalized units.
( ) b 2( , )
e
n
s n
n n
P t TFR t f
The duration of the different conditions is not consistent, which complicates the TFA.
Therefore, the analyses are limited or to the 4 first minutes or to the length per
condition and per test person. This means that the first minute recordings are available from all the 28 test subjects, but this is not the case for the other minutes. For the rest condition, the mean is taken each minute from the four rest conditions.
To have a fair representation on group level, the standard error around the mean will be used to correct for number of test subjects per minute.
The algorithms were implemented in-house using Matlab R2008a (The MathWorks Inc., Natick, MA, USA).
Statistical analysis
Statistical analysis for the two minute segments was executed using the nonparametric Friedman test. This test adjusts for possible inter-subject effects to test whether the different phases (R, PT, MT and MPT) affected the HRV parameters. Post hoc Tukey contrasts were used afterwards in order to explore further differences between two conditions, taking into account multiple testing. The contrasts were calculated pairwise between the four conditions.
Several analyses are performed for the time frequency analysis. In a first analysis the effect of the executed task within one condition is studied. Therefore, the first and the
fourth minute per condition are compared pairwise with the Wilcoxon Signed rank test. In addition, we look for the effect of the executed task within one condition by analyzing the differences between the conditions in the first and also in the fourth minute. The analysis of the first and the fourth minute is performed analogously as the two minute segments of the traditional HRV measures described in this section.
The Wilcoxon signed rank test is also used to perform statistical analysis for the self rating score. P<0.05 was considered statistically significant.
Results
Performance
The mean score ± standard deviation (range) of the mensa test was 75 ± 12,6 (38-94)
% for the questions during MPT and 63 ± 17,5 (24-88) % during MT, resulting in a mean score of 69 ± 12,7 (45-91) %. The better results during MPT could be caused by easier questions than during MT as the order of questions was not randomised.
Moreover, a time effect was observed. Most participants scored better on their second part of the mensa test (12 out of the14 subjects that started with MT and 8 out of the other14 starting with MPT).
Heart rate
A typical tachogram with indication of the different conditions is given in Figure 1.
During a first visual inspection of the signal, a clear transition between the different conditions can be noticed. The condition had a significant (p<0.0001) effect on mean RR of which the boxplot is shown in Fig. 2a. Contrast analysis revealed a
significantly higher mean RR in R than in the active conditions MT, PT or MPT (all p<0.001) as could be expected from Fig. 1. The heart rate was highest during MPT (p<0.01 vs. PT and p<0.05 vs. MT) followed by MT (p=0.24 vs. PT) and PT.
Heart rate variability
Results on HRV showed that condition had no significant effect on SDNN for the first two minutes (p=0.34). SDNN, shown in Figure 2b, provides information about the total variability of heart rate control.
Statistically significant differences between conditions were found for rMSSD (p<0.0001), pNN50 (p<0.001), LF (p<0.001), HF p<0.0001).
Figure 2c presents the results for rMSSD, which reflects vagal modulation of ANS as pNN50 and HF do according to the literature. These three parameters showed similar
evolutions as expected. The differences are quasi identical to mean RR, namely significantly higher in R compared to PT, MT and MPT. In addition, those parameters were significantly lower in MPT than PT.
LF/HF, characterizing the sympathovagal balance is depicted in Figure 2d. Statistics reveal a significantly increased MPT compared to the other three periods.
Time-frequency analysis
Figure 3a shows a typical time series of one subject for HF power and LF/HF. This figure visualizes changes in these parameters within and between the conditions. The time series giving the instantaneous frequency in the high frequency band of the same subject is presented in Figure 3b reflecting fluctuations between 0.22 and 0.26 Hz.
Especially during MT, an increased HF frequency is observed.
Figure 4 shows the time series of all the subjects for HF, LF, LF/HF and fHF. The mean (± standard error) are given for the 4 minutes per condition to see the time evolution.In addition, Table 2 shows the mean value of the different time-frequency parameters for the 4 conditions at the first and the fourth minute. The results of the statistics are also included in this table. An underlined value indicates a statistically significant difference with the first minute of the condition. In addition to this analysis, the first and the fourth minute per condition are mutually compared and indicated with letters.
The HF time series shows that HF power is statistically significantly higher during the rest period compared to the three active conditions for the complete 4 minutes. In the beginning, there are no differences between the three active conditions, but from minute 3 on the two conditions with physical load have a lower HF power. However
at minute 4, this difference is only statistically significant between PT and MT. Only PT has a significant change within the condition.
The LF power during rest shows in the first minute a significantly higher value compared to the three active conditions. We also see that the LF power during PT is statistically significantly higher compared to the two conditions with the mental load.
Within the condition, these differences disappear. The decrease of LF power during R was statistically significant.
Concerning the sympathovagal balance, LF/HF shows no difference in the beginning of each condition. After two minutes, the conditions with the physical load (PT and MPT) show a strong increase. In minute 3, the LF/HF balance during these two conditions are statistically significantly higher compared to the beginning of the condition and compared to the two other conditions without physical load. In the fourth minute, the LF/HF of PT remains high while the LF/HF of MPT decreases back to the level of R and MT.
The instantaneous frequency in the high frequency band shows a constant frequency during the rest period. During MT, the instantaneous frequency shows a linear decrease, while it shows a linear increase during PT. Both changes are statistically significant from the first till the last minute of the condition. During MPT both effects are combined: a decrease because of the mental load and an increase because of the physical load, resulting in a constant frequency. In the first minute, the instantaneous frequencies of both mental conditions are statistically significantly higher compared to those of the two conditions without the mental load. Within the conditions, the instantaneous frequency shifts towards a higher frequency during the physical conditions compared to that of MT and R in the fourth minute.
Self rating scores
Table 3 shows the scores on subjective experiences (tensed, nervous, worried, calm, happy and relaxed). For three subjective experiences (tensed, happy and relaxed), the test subjects gave significantly different scores during rest compared to the active conditions. They reported to be more happy, less tensed and more relaxed during the rest period compared to the MT, PT and MPT (not significant for happy) condition.
The subjects also revealed to feel significantly more tensed during PT compared to MT.
Discussion
HRV parameters were calculated in several conditions: rest (R), physical task (PT), mental task (MT) and a combination of both tasks (MPT). For almost all described measures (Mean RR, rMSSD, pNN50, HF, fHF) , the active conditions can be
distinguished from the rest condition, meaning that heart rate variability is sensitive to any change in mental or physical state. The combination of a physical and a mental task was expected to result in a higher load compared to one of both tasks separately.
Our hypothesis was confirmed by a significantly higher heart rate and a significantly lower vagal modulation (rMSSD, pNN50, HF) for the tradional heart rate variability parameters. The effect of the physical task (increased HR, decreased vagal
modulation) is superposed to the cardiovascular effect of the single mental load. This suggests an additional effect when multiple tasks are combined. Contrarily, Garde et al. [22] reported that the addition of mental demands to a physical computer task does not elicit any further effect on HRV parameters related to autonomic modulation.
Therefore, they suggested that the physical demands have a major influence on the observed ANS changes whereas the influence of the mental load is insignificant. A mental load as such influences autonomic cardiac modulation, even in combination with a simultaneous physical task. Our study, having a heavier physical task, rejects their hypothesis.
Mental stress decreased HF power of heartbeat interval time series as already mentioned in Zhong et al. [32] and Hjortskov et al. [23].
The changes in instantaneous HF frequency can probably be related to changes in respiration frequency as the main peak in the HF band is normally caused by respiration [33-34]. Assuming this, the tasks with mental load have a significantly higher respiration rate compared to PT and rest. A possible explanation is the effect of
speaking as the subjects answered orally on the MENSA test. However, during MT when the subjects underwent the mental load and answered orally, this frequency had a linear decrease within the condition. This decrease is not to be expected when the change at the beginning of the condition is only related to the influence of speaking.
Therefore a constant frequency during the complete task is expected. The increase in the beginning can be related to the effect of the mental task and the decrease within one condition to habituation. An increased respiration frequency during a 1h low- grade mental stress task in healthy subjects was already found in Nilsen et al. [35].
Vlemincx et al. [36] also reported an increased breathing rate during mental stress, although Bernardi et al. [37] observed oppositely a lower respiration frequency. The linear increase in instantaneous frequency during PT can be related to incoming fatigue, which is an ongoing physiological process that initiates from the beginning of the physical load. During MPT, the effect is the combination of both the physical and the mental effect on the frequency: a linear increase due to fatigue during the physical load and a decrease due to habituation of the mental load, resulting in a constant frequency. The respiration frequency during MPT is at each time instant the highest of the four conditions.
This indication of the fatiguing process of the body during the physical performance can also be seen in other parameters. In particular, the LF/HF ratio is increasing monotonously during PT and MPT (Figure 4), indicating the fast increase in
sympathetic dominance caused by the heavy physical task. Evolutions in time were also observed within other conditions. The decrease in LF during rest condition can indicate recuperation after a physical load [38]. With respect to the sympathovagal balance, there is a decrease during rest (not significant), but an increase during both conditions with mental load showing that the vagal pathways of ANS became
relatively a bit more active in rest while the sympathetic modulation gained importance in case of mental stress.
Seong et al. [39] reported an increase in LF/HF in the beginning and at the end of a mental task, which can also be observed here but not significantly (Figure 3). Another recent study [40] found that HRV patterns showed significantly decreased variances in highly stressed subjects, indicating somewhat disturbed ANS rhythms under the influence of stress. Consequently, sympathetic predominance and vagal withdrawal might represent the autonomic counterpart of the complex psychophysiological changes underlying the increase in cardiovascular risk associated with stress [41-42].
As the subjects were less happy, more tensed and less relaxed during the active conditions compared to rest, the different active tasks brought the participants into another emotional state. The fact that the subjects revealed to feel more tensed during PT compared to MT is probably due to the interpretation of tension as physical tension. MT and MPT being not significantly different with respect to the tension level confirmed this idea. Because of the mental distraction, the perception of tension due to the physical task is reduced. Despite the marked difference in several HRV parameters between the active conditions, this was not visible in the self rating scores.
Therefore, even if subjects do not indicate a difference in stress level, often there are changes in physiology which can be observed via for example heart rate variability as performed in this study.
The ECG, and consequently the tachogram, can be monitored easily. In addition, calculating HRV parameters is computationally inexpensive making the findings of this study useful in daily life. However, two limitations of the study have to be noticed. First, not all conditions are equal in length causing problems in calculating one value for each HRV parameter in each condition. Concerning the frequency
domain parameters, this was solved by applying a detailed time-frequency study showing how those parameters change over time. Second, respiration was not
monitored. Therefore, new measurements are being recorded to gain more insight on these two remarks. In the new study, respiration will be monitored to include
breathing information and its inherent influence on HRV. Moreover, the heavy 45°
shoulder abduction as physical task will be replaced by a clicking task with a computer mouse to focus more on job stress in an office environment.
Conclusions
This study showed clearly that heart rate variability (HRV) is a very useful tool to analyze mental and physical stress. 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 HRV characteristics.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SV did the preprocessing and analysis of the data, interpreted the results and drafted the manuscript. JT was involved in the acquisition of the data , performed the time- frequency analysis, helped with the other analysis and helped with the manuscript. He made the revision of the manuscript. AS contributed substantially to conception and design while SVH supervised the study.
Authors' information
SV and JT received the MD in biomedical engineering in June 2006 from the
department of Electrical Engineering at the Katholieke Universiteit Leuven. Both are Ph.D. students in this department with main research in biomedical signal processing and special interest in EMG and ECG analysis for several applications.
AS graduated in 1973 from the Department of Mechanical Engineering at the Katholieke Universiteit Leuven (Belgium). He worked as a research assistant at the Biomechanics Laboratory and received his Ph.D. in 1980 from the same university.
Since 1983 he has been a fully active member of the Faculty of Physical Education and Physiotherapy at KU Leuven, where he started the Laboratory of Ergonomics and Occupational Biomechanics in 1985. Since then, his main topics of research have been related to the quantitative use of EMG in movement analysis in general and more specifically in occupational tasks.
SVH received the MD in computer science engineering in June 1981, the MD in Biomedical engineering in July 1985 and the Ph.D. in electrical engineering in June
1987, all from K.U.Leuven, Belgium. She is full professor at the department of Electrical Engineering from the Katholieke Universiteit Leuven, Leuven, Belgium.
One of her main research interests is biomedical data processing (linear and nonlinear signal analysis and classification) with special attention to the numerical aspects and to the design of reliable algorithms and their practical evaluation in medical
diagnostics. She is author or coauthor of 3 books, editor of 2 books and 5 special issues. She has also authored and co-authored 200 papers in International Journals, 4 book chapters, and more than 200 conference contributions.
Acknowledgements
We thank the European Commission for funding part of this work under contract IST- 027291 (ConText).
Research supported by
Research Council KUL: GOA-AMBioRICS, GOA MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), IDO 05/010 EEG-fMRI, IDO 08/013 Autism, IOF-KP06/11 FunCopt, several PhD/postdoc & fellow grants;
Flemish Government:
o FWO: PhD/postdoc grants, projects, G.0519.06 (Noninvasive brain oxygenation), FWO-G.0321.06 (Tensors/Spectral Analysis), G.0302.07 (SVM), G.0341.07 (Data fusion), research communities (ICCoS, ANMMM);
o IWT: TBM070713-Accelero, TBM070706-IOTA3, PhD Grants;
Belgian Federal Science Policy Office IUAP P6/04 (DYSCO, `Dynamical systems, control and optimization', 2007-2011);
EU: ETUMOUR (FP6-2002-LIFESCIHEALTH 503094), Healthagents (IST–
2004–27214), FAST (FP6-MC-RTN-035801), Neuromath (COST-BM0601)
ESA: Cardiovascular Control (Prodex-8 C90242)
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Figures
Figure 1 - Typical tachogram of a subject during the protocol
This figure shows the time series of a tachogram of a typical subject during the test.
The vertical lines indicate the start and end of each experimental condition (R = rest, PT = physical task, MT = mental task, MPT = mental and physical task combined).
RR interval time [s] is plotted on the y-axis versus the time [s] on the x-axis.
Figure 2 - Boxplots of several HRV parameters in different conditions
Boxplots of (a) the mean RR intervals, (b) SDNN, (c) RMSSD and (d) LF/HF for the different conditions (R = rest, PT = physical task, MT = mental task, MPT = mental and physical task combined). Below the figure, the mean values are given.
Figure 3 – Time frequency analysis: typical evolution
Typical evolution of (a) instantaneous HF power (grey line) and LF/HF ratio (black line) over time and (b) instantaneous frequency in the HF band with indication of the different conditions (R = rest, PT = physical task, MT = mental task, MPT = mental and physical task combined).
Figure 4 – Time frequency analysis: group analysis
Mean + standard error of the first four minutes of (a) instantaneous power in HF, (b) instantaneous power in LF, (c) instantaneous LF/HF ratio and (d) instantaneous frequency in the HF band. The four conditions are colored: PT = physical task (black); MT = mental task (red), MPT = mental and physical task combined (green) and R = rest (blue).
Tables
Table 1 – overview of the different conditions with their duration and different tasks
acronym MT PT MPT R
Full name Mental task Physical task Mental and
physical task Rest Mental load MENSA test
(part 1) none MENSA test
(part 2) none
Physical load none 45° shoulder
abduction 45° shoulder
abduction none
Duration Max. 10 min 6 min Max 10 min In order:
4 min – 2min – 2min – 4min
Table 2 - Mean for time frequency measures of minute 1 and 4 during different conditions
R = rest, PT = physical task, MT = mental task, MPT = mental and physical task combined. Number of subjects is 28.
a Significant difference with PT (p<0.05); b Significant difference with MT (p<0.05); c Significant difference with MPT (p<0.05); underlined: significant difference with the first minute during the condition.
PT MT MPT R
min 1 4 1 4 1 4 1 4
HF [ms2] 86.51 56.87 89.64 78.33a 85.88 70.73 119.80 a,b,c 96.57 a,b,c LF [ms2] 604.32 603.25 422.85a 416.41 387.34a 547.54 935.92a,b,c 529.88 LF/HF 29.44 43.71 20.15 20.34 21.42 19.377 28.01b,c 20.41a fHF [Hz] 0.205 0.216 0.217a 0.210a 0.218a 0.219b 0.206b 0.205a,b,c
Table 3 - Mean (SD) for scores on subjective experiences during different conditions
R = rest, PT = physical task, MT = mental task, MPT = mental and physical task combined. Scales ranged from 1 to 4 (1 = not and 4 =extremely). Number of subjects is 28.
a Significant difference with R (p<0.05); b Significant difference with MT (p<0.05)
Variable and condition MT PT MPT R
Tensed 2.14 (0.65) a 2.79 (0.74) a,b 2.54 (0.79) a 1.66 (0.56)
Nervous 1.64 (0.73) 1.68 (0.82) 1.79 (0.69) 1.52 (0.44)
Worried 1.46 (0.74) 1.39 (0.69) 1.36 (0.73) 1.23 (0.55)
Calm 2.50 (0.75) 2.21 (0.79) 2.25 (0.75) 3.16 (0.61)
Happy 2.46 (0.79) a 2.25 (0.89)a 2.57 (0.74) 2.88 (0.50) Relaxed 2.36 (0.62) a 1.79 (0.92) a 1.68 (0.77) a 2.95 (0.61)
