Matthias Weippert

Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment

Mental stress is accompanied by dynamic changes in autonomic nervous system (ANS) activity. Heart rate variability (HRV) analysis is a popular tool for assessing the activities of autonomic nervous system. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1) online monitoring of heart rate signals, 2) signal processing (e.g., using the continuous wavelet transform to extract the local features of HRV in time-frequency domain), 3) exploiting fuzzy clustering and fuzzy identification techniques to render robustness in HRV analysis against uncertainties due to individual variations, and 4) monitoring the functioning of autonomic nervous system under different stress conditions. Our experiments involved 38 physically fit subjects (26 male, 12 female, aged 18-29 years) in air traffic control task simulations. The subjective rating scores of mental workload were assessed using NASA task load index. Fuzzy clustering methods have been used to model the experimental data. Further, a robust fuzzy identification technique has been used to handle the uncertainties due to individual variations for the assessment of mental stress.

Heart Rate Variability and Blood Pressure during Dynamic and Static Exercise at Similar Heart Rate Levels

Aim was to elucidate autonomic responses to dynamic and static (isometric) exercise of the lower limbs eliciting the same moderate heart rate (HR) response. Method: 23 males performed two kinds of voluntary exercise in a supine position at similar heart rates: static exercise (SE) of the lower limbs (static leg press) and dynamic exercise (DE) of the lower limbs (cycling). Subjective effort, systolic (SBP) and diastolic blood pressure (DBP), mean arterial pressure (MAP), rate pressure product (RPP) and the time between consecutive heart beats (RR-intervals) were measured. Time-domain (SDNN, RMSSD), frequency-domain (power in the low and high frequency band (LFP, HFP)) and geometric measures (SD1, SD2) as well as non-linear measures of regularity (approximate entropy (ApEn), sample entropy (SampEn) and correlation dimension D2) were calculated. Results: Although HR was similar during both exercise conditions (88±10 bpm), subjective effort, SBP, DBP, MAP and RPP were significantly enhanced during SE. HRV indicators representing overall variability (SDNN, SD 2) and vagal modulated variability (RMSSD, HFP, SD 1) were increased. LFP, thought to be modulated by both autonomic branches, tended to be higher during SE. ApEn and SampEn were decreased whereas D2 was enhanced during SE. It can be concluded that autonomic control processes during SE and DE were qualitatively different despite similar heart rate levels. The differences were reflected by blood pressure and HRV indices. HRV-measures indicated a stronger vagal cardiac activity during SE, while blood pressure response indicated a stronger sympathetic efferent activity to the vessels. The elevated vagal cardiac activity during SE might be a response mechanism, compensating a possible co-activation of sympathetic cardiac efferents, as HR and LF/HF was similar and LFP tended to be higher. However, this conclusion must be drawn cautiously as there is no HRV-marker reflecting “pure” sympathetic cardiac activity.

Caffeine-induced increase in voluntary activation and strength of the quadriceps muscle during isometric, concentric and eccentric contractions

This study investigated effects of caffeine ingestion (8 mg/kg) on maximum voluntary torque (MVT) and voluntary activation of the quadriceps during isometric, concentric and eccentric contractions. Fourteen subjects ingested caffeine and placebo in a randomized, controlled, counterbalanced, double-blind crossover design. Neuromuscular tests were performed before and 1 h after oral caffeine and placebo intake. MVTs were measured and the interpolated twitch technique was applied during isometric, concentric and eccentric contractions to assess voluntary activation. Furthermore, normalized root mean square of the EMG signal was calculated and evoked spinal reflex responses (H-reflex evoked at rest and during weak isometric voluntary contraction) as well as twitch torques were analyzed. Caffeine increased MVT by 26.4 N m (95%CI: 9.3-43.5 N m, P = 0.004), 22.5 N m (95%CI: 3.1-42.0 N m, P = 0.025) and 22.5 N m (95%CI: 2.2-42.7 N m, P = 0.032) for isometric, concentric and eccentric contractions. Strength enhancements were associated with increases in voluntary activation. Explosive voluntary strength and voluntary activation at the onset of contraction were significantly increased following caffeine ingestion. Changes in spinal reflex responses and at the muscle level were not observed. Data suggest that caffeine ingestion induced an acute increase in voluntary activation that was responsible for the increased strength regardless of the contraction mode.

Fuzzy Filtering for Physiological Signal Analysis

This study suggests the use of fuzzy-filtering algorithms to deal with the uncertainties associated to the analysis of physiological signals. The signal characteristics, for a given situation or physiological state, vary for an individual over time and also vary among the individuals with the same state. These random variations are due to the several factors related to the physiological behavior of individuals, which cannot be taken into account in the interpretation of signal characteristics. Our approach is to reduce the effect of random variations on the analysis of signal characteristics via filtering out randomness or uncertainty from the signal using a nonlinear fuzzy filter. A fuzzy-filtering algorithm, which is based on a modification of filtering algorithm of Kumar et al. [M. Kumar, N. Stoll, and R. Stoll, IEEE Trans. Fuzzy Syst., vol. 17, no. 1, pp. 150-166, Feb. 2009], is proposed for an improved performance. The method is illustrated by studying the effect of head-up tilting on the heart-rate signal of 40 healthy subjects.

Sample Entropy and Traditional Measures of Heart Rate Dynamics Reveal Different Modes of Cardiovascular Control During Low Intensity Exercise

Abstract: Nonlinear parameters of heart rate vari ability (HRV) have proven their prognostic value in clinical settings, but their physiological background is not very well established. We assessed the effects of low intensity isometric (ISO) and dynamic (DYN) exercise of the lower limbs on heart rate matched intensity on traditional and entropy measures of HRV. Due to changes of afferent feedback under DYN and ISO a distinct autonomic response, mirrored by HRV measures, was hypothesized. Five-minute inter-beat interval measurements of 43 healthy males (26.0 ± 3.1 years) were performed during rest, DYN and ISO in a randomized order. Blood pressures and rate pressure product were higher during ISO vs . DYN ( p < 0.001). HRV indicators SDNN as well as low and high frequency power were significantly higher during ISO ( p < 0.001 for all measures). Compared to DYN, sample entropy (SampEn) was lower during ISO ( p < 0.001). Concluding, contraction mode itself is a significant modulator of the autonomic cardiovascular response to exercise. Compared to DYN, ISO evokes a stronger blood pressure response and an enhanced interplay between both autonomic branches. Non-linear HRV measures indicate a more regular behavior under ISO. Results support the view of the reciprocal antagonism being

Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals

Quantifying stress levels of an individual based on a mathematical analysis of real-time physiological data measurements is challenging. This study suggests a stochastic fuzzy analysis method to evaluate the short time series of R-R intervals (time intervals between consecutive heart beats) for a quantification of the stress level. The 5-min-long series of R-R intervals recorded under a given stress level are modeled by a stochastic fuzzy system. The stochastic model of heartbeat intervals is individual specific and corresponds to a particular stress level. Once the different heartbeat interval models are available for an individual, an analysis of the given R-R interval series generated under an unknown stress level is performed by a stochastic interpolation of the models. The stress estimation method has been implemented in a mobile telemedical application employing an e-health system for an efficient and cost-effective monitoring of patients while at home or at work. The experiments involve 50 individuals whose stress scores were assessed at different times of the day. The subjective rating scores showed a high correlation with the values predicted by the proposed analysis method.

A mixture of fuzzy filters applied to the analysis of heartbeat intervals

This study provides a stochastic modeling of the heartbeat intervals using a mixture of Takagi–Sugeno type fuzzy filters. The model parameters are inferred under variational Bayes (VB) framework. The model of the heartbeat intervals is in the form of a history-dependent probability density. The parameters, characterizing the heartbeat intervals probability density, include the estimated parameters of different fuzzy filters and may serve as the features of the heartbeat interval series. The features of the heartbeat intervals provide a description of the physiological state of an individual. A novelty of our analysis method is that the physiological state is predicted as a part of the features extraction procedure. This is done via deriving, using VB paradigm, an analytical expression for the posterior distribution that the observed heartbeat intervals have been generated by the stochastic model of the physiological state. The method is illustrated with the data of 40 healthy subjects studied in a tilt-table experiment.

Cardiac troponin T and echocardiographic dimensions after repeated sprint vs. moderate intensity continuous exercise in healthy young males.

Regular physical exercise can positively influence cardiac function; however, investigations have shown an increase of myocardial damage biomarkers after acute prolonged endurance exercises. We investigated the effect of repeated sprint vs. moderate long duration exercise on markers of myocardial necrosis, as well as cardiac dimensions and functions. Thirteen healthy males performed two different running sessions (randomized, single blinded cross-over design): 60 minutes moderate intensity continuous training (MCT, at 70% of peak heart rate (HRpeak)) and two series of 12 × 30-second sprints with set recovery periods in-between (RST, at 90% HRpeak). Venous blood samples for cardiac troponin T (cTnT), creatine kinase (CK) and MB isoenzyme (CK-MB) were taken 1 and 4 hours after exercise sessions. After each session electrocardiographic (ECG) and transthoracic echocardiographic (TTE) data were recorded. Results showed that all variables - average heart rate, serum lactate concentration during RST, subjective exertion and cTnT after RST - were significantly higher compared to MCT. CK and CK-MB significantly increased regardless of exercise protocol, while ECG and TTE indicated normal cardiac function. Our results provide evidence that RST contributes significantly to cTnT and CK release. This biomarker increase seems to reflect a physiological rather than a pathological phenomenon in healthy, exercising subjects.

Effects of breathing patterns and light exercise on linear and nonlinear heart rate variability

Despite their use in cardiac risk stratification, the physiological meaning of nonlinear heart rate variability (HRV) measures is not well understood. The aim of this study was to elucidate effects of breathing frequency, tidal volume, and light exercise on nonlinear HRV and to determine associations with traditional HRV indices. R-R intervals, blood pressure, minute ventilation, breathing frequency, and respiratory gas concentrations were measured in 24 healthy male volunteers during 7 conditions: voluntary breathing at rest, and metronome guided breathing (0.1, 0.2 and 0.4 Hz) during rest, and cycling, respectively. The effect of physical load was significant for heart rate (HR; p < 0.001) and traditional HRV indices SDNN, RMSSD, lnLFP, and lnHFP (p < 0.01 for all). It approached significance for sample entropy (SampEn) and correlation dimension (D2) (p < 0.1 for both), while HRV detrended fluctuation analysis (DFA) measures DFAα1 and DFAα2 were not affected by load condition. Breathing did not affect HR but affected all traditional HRV measures. D2 was not affected by breathing; DFAα1 was moderately affected by breathing; and DFAα2, approximate entropy (ApEn), and SampEn were strongly affected by breathing. DFAα1 was strongly increased, whereas DFAα2, ApEn, and SampEn were decreased by slow breathing. No interaction effect of load and breathing pattern was evident. Correlations to traditional HRV indices were modest (r from -0.14 to -0.67, p < 0.05 to <0.01). In conclusion, while light exercise does not significantly affect short-time HRV nonlinear indices, respiratory activity has to be considered as a potential contributor at rest and during light dynamic exercise.

Tri-axial high-resolution acceleration for oxygen uptake estimation: Validation of a multi-sensor device and a novel analysis method.

We validated a multi-sensor chest-strap against indirect calorimetry and further introduced the total-acceleration-variability (TAV) method for analyzing high-resolution accelerometer data. Linear regression models were developed to predict oxygen uptake from the TAV-processed multi-sensor data. Individual correlations between observed and TAV-predicted oxygen uptake (V̇O2) were strong (mean r = 0.94) and bias low (1.5 mL·min(-1)·kg(-1), p < 0.01; 95% confidence interval: 8.7 mL·min(-1)·kg(-1); -5.8 mL·min(-1)·kg(-1)); however, caution should be taken when a single-model value is used as a surrogate for V̇O2.