Efficient solution for frequency band decomposition problem using wavelet packet in HRV

Heart rate variability (HRV) is a very significant noninvasive tool for assessment of sympathovagal balance (SB) that reflects variation of parasympathetic and sympathetic activities in autonomic nervous system (ANS). Low frequency/high frequency (LF/HF) power ratio provides information about these activities. Because of nonstationary characteristic of HRV, analyses based on wavelet transform were typically preferred in previous studies. There is an important problem that required frequency ranges for LF and HF cannot be obtained using discrete wavelet transform (DWT). Different sampling frequencies do not remove this problem. In this study, a solution based on wavelet packet (WP) is presented for removing this problem. In addition, effect of WP on SB values is investigated. Method was applied to spontaneous ventricular tachyarrhythmia database and variation of energy values and LF/HF energy ratios were compared for DWT and WP. WP provides absolutely excellent approximation to required frequency bands and exposes different and impressive SB results.     

The effects of physical vibration on heart rate variability as a measure of drowsiness

We investigated the effects of low frequency whole body vibration on heart rate variability (HRV), a measure of autonomic nervous system activation that differentiates between stress and drowsiness. Fifteen participants underwent two simulated driving tasks for 60?min each: one involved whole-body 4–7?Hz vibration delivered through the car seat, and one involved no vibration. The Karolinska Sleepiness Scale (KSS), a subjective measure of drowsiness, demonstrated a significant increase in drowsiness during the task. Within 15–30?min of exposure to vibration, autonomic (sympathetic) activity increased (p?p?Practitioner summary: The effects of physical vibration on driver drowsiness have not been well investigated. This laboratory-controlled study found characteristic changes in heart rate variability (HRV) domains that indicated progressively increasing neurological effort in maintaining alertness in response to low frequency vibration, which becomes significant within 30?min.

Abbreviations: ANS: autonomic nervous system; Ctrl: control; EEG: electroencephalography; HF: the power in high frequency range (0.15 Hz-0.4Hz) in the PSD relected parasympathetic activity only; HRV: heart rate variability; KSS: karolinska sleepiness scale; LF: the power in low frequency range (0.04 Hz-0.15Hz) in the PSD reflected both sympathetic and parasympathetic activity of the autonomic nervous system; LF/HF ratio: the ratio of LF to HF indicated the balance between sympathetic and parasympathetic activity; RMSSD: the root mean square of difference of adjacent RR interval; pNN50: the number of successive RR interval pairs that differed by more than 50 ms divided by the total number of RR intervals; RR interval: the differences between successive R-wave occurrence times; PSD: power spectral density; RTP: research training program; SD: standard deviation; SEM: standard error of the Mean; Vib: vibration     

Time-frequency analysis of the RT and RR variability to stratify hypertrophic cardiomyopathy patients.

The RT interval is a measure of the ventricular repolarization and is partially influenced by the sympathovagal balance. The analysis of the variation of the duration of the RT and RR intervals might bring new information about the arrhythmogenic vulnerability and autonomic imbalance. The RR signal and its spectral density (SD) are characterized by two different patterns during the sleep period. On the basis of this information, RT and RR sequences have been automatically classified into two patterns, R and N. In this work, we propose a methodology to define new variables that are able to distinguish patients with hypertrophic cardiomyopathy (HCM) who later developed sudden cardiac death (SCD) from HCM patients without such episode during the follow-up. These variables are based on the instantaneous frequency calculation using time-frequency representation of the RT and RR signals previously classified into R and N patterns. In this study, three spectral bands have been considered: low-frequency band (LF, 0-0.07 Hz), mid-frequency band (MF, 0.07-0.15 Hz), and high-frequency band (HF, 0.15-0.45 Hz). Then a suitable combination of mean energy and mean frequency of the RT and RR signals in the MF and HF bands has allowed HCM patients with SCD to be discriminated from HCM patients without SCD (P < 0.001).     

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5–30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8–12 Hz) frequency band is weakened, and the beta (13–30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake–fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.     

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5-30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8-12 Hz) frequency band is weakened, and the beta (13-30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake-fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.     

基于经验模态分解的油气两相流流型状态监测

基于经验模态分解技术,采用能量评估和阈值概率统计手段,提出了一种油气两相流流型状态监测的新方法.油气两相流差压信号是一典型的非平稳多组分信号,经验模态分解首先将差压信号分解成9阶本征模函数,按照频率范围可以划分为3个子带:高频带(30～50 Hz),中频带(5～30 Hz)和低频带(0～5 Hz).在不同流型下,中频带的能量变化很显著,跟踪捕捉中频带能量变化可以监测两相流流型的跃迁.首先确定不同流型下对应的归一化能量阈值,阈值概率统计技术通过移动时间窗扫描中频带子信号的方式来监测流型状态变化.油气两相流的实验结果表明该方法是有效的,为两相流流型状态监测提供了新途径.     

Mechanical impedance of the human hand-arm system

The mechanical impedance of the human hand-arm system was measured within the frequency range of 20–1500 Hz. A handle, specially designed for such measurements, was used. The studies were carried out on eight healthy male subjects during different experimental conditions defined by three different hard-arm postures, hand grip forces (25–75 N) adopted by the subjects, the amplitude (27–53 mm/ s_rms; 1.4–2.8 g at 80 Hz) and direction of the vibration stimuli. The outcome shows that the mechanical impedance of the hand-arm system depends on the frequency of the vibration stimuli. Above 200 Hz, the impedance, in general, increases quite rapidly, from about 150 Ns/m up to about 500 Ns/m at 1500 Hz, with the frequency. At lower frequencies, however, various shapes of the impedance curves were found which were most pronounced between different hand-arm postures. For the transverse direction, the impedance increased from about 50 Ns/m at 20 Hz to maximum about 100 Hz followed by a slight decrease. For the proximal-distal direction the impedance decreased from about 150 Ns/m at 20 Hz to minimum at about 100 Hz. More firm hand grips, as well, as higher vibration levels, resulted in higher impedance magnitudes for frequencies above about 100 Hz. Remarkably enough, for lower frequencies an almost opposite relationship was found. Furthermore, the results indicate a non-linear relationship between mechanical impedence and the studied experimental variables. Therefore, prior to setting up future standards, the mechanical properties of the hand-arm system should be taken into careful consideration.     

基于多元经验模态分解-传递熵的脑肌电信号耦合分析

皮层肌肉功能耦合是大脑皮层和肌肉组织间的相互作用,脑肌电信号的多尺度耦合特征可以体现皮层-肌肉间多时空的功能联系.将多元经验模态分解(MEMD)与传递熵(TE)结合,构建出MEMD-TE模型,应用于脑、肌间耦合分析.首先对同步采集的脑电(EEG)和肌电(EMG)信号进行预处理,然后采用多元经验模态分解算法对信号进行时-频尺度化,最后计算不同尺度上的传递熵值,分析各个尺度不同耦合方向(EEG→EMG及EMG→EEG)上的非线性耦合特征.采集了10名受试者静态握力(5 kg、10 kg、20 kg)下脑、肌电信号,实验结果表明:脑电对肌电的MEMD-TE值在高频段(40 Hz~75 Hz)上高于肌电对脑电的MEMD-TE值,皮层肌肉功能耦合具有双向性,且不同方向和频段上的耦合强度有所差异,显著性校验反映了不同力度下脑电对肌电的MEMD-TE值没有显著性差别.     

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.     

Discomfort of seated persons exposed to low frequency lateral and roll oscillation: Effect of seat cushion

The discomfort caused by lateral oscillation, roll oscillation, and fully roll-compensated lateral oscillation has been investigated at frequencies between 0.25 and 1.0 Hz when sitting on a rigid seat and when sitting on a compliant cushion, both without a backrest. Judgements of vibration discomfort and the transmission of lateral and roll oscillation through the seat cushion were obtained with 20 subjects. Relative to the rigid seat, the cushion increased lateral acceleration and roll oscillation at the lower frequencies and also increased discomfort during lateral oscillation (at frequencies less than 0.63 Hz), roll oscillation (at frequencies less than 0.4 Hz), and fully roll-compensated lateral oscillation (at frequencies between 0.315 and 0.5 Hz). The root-sums-of-squares of the frequency-weighted lateral and roll acceleration at the seat surface predicted the greater vibration discomfort when sitting on the cushion. The frequency-dependence of the predicted discomfort may be improved by adjusting the frequency weighting for roll acceleration at frequencies between 0.25 and 1.0 Hz.     

Like/dislike analysis using EEG: Determination of most discriminative channels and frequencies

In this study, we have analyzed electroencephalography (EEG) signals to investigate the following issues, (i) which frequencies and EEG channels could be relatively better indicators of preference (like or dislike decisions) of consumer products, (ii) timing characteristic of “like” decisions during such mental processes. For this purpose, we have obtained multichannel EEG recordings from 15 subjects, during total of 16 epochs of 10 s long, while they were presented with some shoe photographs. When they liked a specific shoe, they pressed on a button and marked the time of this activity and the particular epoch was labeled as a LIKE case. No button press meant that the subject did not like the particular shoe that was displayed and corresponding epoch designated as a DISLIKE case. After preprocessing, power spectral density (PSD) of EEG data was estimated at different frequencies (4, 5, …, 40 Hz) using the Burg method, for each epoch corresponding to one shoe presentation. Each subject's data consisted of normalized PSD values (NPVs) from all LIKE and DISLIKE cases/epochs coming from all 19 EEG channels. In order to determine the most discriminative frequencies and channels, we have utilized logistic regression, where LIKE/DISLIKE status was used as a categorical (binary) response variable and corresponding NPVs were the continuously valued input variables or predictors. We observed that when all the NPVs (total of 37) are used as predictors, the regression problem was becoming ill-posed due to large number of predictors (compared to the number of samples) and high correlation among predictors. To circumvent this issue, we have divided the frequency band into low frequency (LF) 4–19 Hz and high frequency (HF) 20–40 Hz bands and analyzed the influence of the NPV in these bands separately. Then, using the p-values that indicate how significantly estimated predictor weights are different than zero, we have determined the NPVs and channels that are more influential in determining the outcome, i.e., like/dislike decision. In the LF band, 4 and 5 Hz were found to be the most discriminative frequencies (MDFs). In the HF band, none of the frequencies seemed offer significant information. When both male and female data was used, in the LF band, a frontal channel on the left (F7-A1) and a temporal channel on the right (T6-A2) were found to be the most discriminative channels (MDCs). In the HF band, MDCs were central (Cz-A1) and occipital on the left (O1-A1) channels. The results of like timings suggest that male and female behavior for this set of stimulant images were similar.     

Estimation and evaluation of sub-bands on LF and HF base-bands in HRV for Ventricular Tachyarrhythmia patients

Heart Rate Variability (HRV) is an efficient tool for assessment of Sympathovagal Balance (SB) and classification of cardiac disturbances. However, its index may be not enough for classification and evaluation of some disease. This study presents 32 new sub-bands over LF and HF base-bands that are accepted in the literature. Moreover, it determines dominant sub-bands over both base-bands in VTA database. These sub-bands are obtained using Wavelet Packet Transform (WPT) and evaluated using Multilayer Perceptron Neural Networks (MLPNN). Results are compared with obtained results from normal datasets. The domination effects of these sub-bands are assessed according to comparison of each other related to MLPNN training and test accuracy percentages by selecting different width of windows. As a result, obtained results showed that the LF zone including LF₁, LF₂ and LF₃ sub-bands on 0.0390625–0.0859375 Hz frequency range is the most dominant over the LF base-band and, the HF zone including HF1, HF2 and HF3 on 0.1953125–0.28125 Hz frequency range is the most dominant over the HF base-band. In normal datasets, distinctive domination effect has not been determined.     

Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique

This paper presents the evaluation of mental stress assesment using heart-rate variability (HRV). The activity of the autonomic nervous system (ANS) is studied by means of time-frequency analysis (TFA) of the heart-rate variability signal. Spectral decomposition of the heart-rate variability before smoking and after smoking was obtained. Mental stress is accompanied by dynamic changes in ANS activity. HRV analysis is a popular tool for assessing the activities of autonomic nervous system. The approach consists of (1) monitoring of heart rate signals, (2) signal processing using wavelet transform (WT) (different wavelets), (3) neuro fuzzy evaluation techniques to provide robustness in HRV analysis, (4) monitoring the function of ANS under different stress conditions. Our experiment involves 20 physically fit persons under different times (before smoking and after smoking). Nero fuzzy technique have been used to model the experimental data.     

Characterisation of the human-seat coupling in response to vibration

Characterising the coupling between the occupant and vehicle seat is necessary to understand the transmission of vehicle seat vibration to the human body. In this study, the vibration characteristics of the human body coupled with a vehicle seat were identified in frequencies up to 100 Hz. Transmissibilities of three volunteers seated on two different vehicle seats were measured under multi-axial random vibration excitation. The results revealed that the human-seat system vibration was dominated by the human body and foam below 10 Hz. Major coupling between the human body and the vehicle seat-structure was observed in the frequency range of 10–60 Hz. There was local coupling of the system dominated by local resonances of seat frame and seat surface above 60 Hz. Moreover, the transmissibility measured on the seat surface between the human and seat foam is suggested to be a good method of capturing human-seat system resonances rather than that measured on the human body in high frequencies above 10 Hz.Practitioner Summary: The coupling characteristics of the combined human body and vehicle seat system has not yet been fully understood in frequencies of 0.5–100 Hz. This study shows the human-seat system has distinctive dynamic coupling characteristics in three different frequency regions: below 10 Hz, 10–60 Hz, and above 60 Hz.     

Fore-and-aft and dual-axis vibration of the seated human body: Nonlinearity,cross-axis coupling,and associations between resonances in the transmissibility and apparent mass

This study examined how the apparent mass and transmissibility of the human body depend on the magnitude of fore-and-aft vibration excitation and the presence of vertical vibration. Fore-and-aft and vertical acceleration at five locations along the spine, and pitch acceleration at the pelvis, were measured in 12 seated male subjects during fore-and-aft random vibration excitation (0.25–20 Hz) at three vibration magnitudes (0.25, 0.5 and 1.0 ms⁻² r.m.s.). With the greatest magnitude of fore-and-aft excitation, vertical vibration was added at 0.25, 0.5, or 1.0 ms⁻² r.m.s. Forces in the fore-and-aft and vertical directions on the seat surface were measured to calculate apparent masses. Transmissibilities and apparent masses during fore-and-aft excitation showed a principal resonance around 1 Hz and a secondary resonance around 2–3 Hz. Increasing the magnitude of fore-and-aft excitation, or adding vertical excitation, decreased the magnitudes of the resonances. At the primary resonance frequency, the dominant mode induced by fore-and-aft excitation involved bending of the lumbar spine and the lower thoracic spine with shear deformation of tissues at the ischial tuberosities. The relative contributions to this mode from each body segment (especially the pelvis and the lower thoracic spine) varied with vibration magnitude. The nonlinearities in the apparent mass and transmissibility during dual-axis excitation indicate coupling between the principal mode of the seated human body excited by fore-and-aft excitation and the cross-axis influence of vertical excitation.Relevance to industryUnderstanding movements of the body during exposure to whole-body vibration can assist the optimisation of seating dynamics and help to control the effects of the vibration on human comfort, performance, and health. This study suggests cross-axis nonlinearity in biodynamic responses to vibration should be considered when optimising vibration environments.     

How to avoid misinterpretation of heart rate variability power spectra?

Spectral analysis of R-R Interval time series is increasingly used to determine periodic components of heart rate variability (HRV). Particular diagnostic relevance is assigned to a low-frequency (LF) component, associated with blood pressure regulation, and a high-frequency (HF) component, also referred to as respiratory sinus arrhythmia (RSA) in the HRV power spectra. Frequency ranges for parametrisation of power spectra have been defined for either component in numerous publications.Results obtained from examinations with standardised psychic load in which ECG and respiratory signal are continuously recorded and adequately processed have shown that the true individual frequency range of the HF component can be reliably determined only by means of characteristics of respiration (respiratory rate (RR), range and median value of RR, tidal depth). Respiratory rhythms are interindividually extremely differentiated and of individual-specific nature. In many cases LF and HF components may be totally superimposed on each other and, consequently, cannot be diagnostically evaluated.     

Experimental study on band merging of non-linear multi-band piezoelectric energy harvester into single broadband using magnetic coupling

The operational bandwidth of Vibration Energy Harvesters (VEH) is area of concern due to stochastic, time-varying, random and multi-frequency nature of available environmental vibrating sources. Most of the VEH have narrow bandwidth providing usable power at specific frequencies. Efforts have been made to increase the frequency range by introducing non-linear structures and techniques. In this paper, multi-band output of the non-linear Piezoelectric Energy Harvester (PEH) is transformed into single wider band output using additional non-linear phenomenon. Dual region operation of PEH results into two separate band output. First region is the outcome of beam resonance and Centre of Gravity (CoG) shift whereas second region is due to the non-linear behaviour of cylinders. In this work, these separate bands are merged to form a single wider band. For merging these two bands and enhancing the bandwidth of PEH, additional phenomenon is introduced using two permanent magnets. A varying magnetic field by changing the distance between magnets changes stiffness of the cantilever beam and that leads to a change in the resonant frequency of band-I. Thus, the overall process shifts band-I towards band-II. In this work, the two separate bands are merged to have one wider band providing 53.22% more frequency coverage than our previous work with a bandwidth of 47.5 Hz. This band includes vibrational frequency range of 25.65–73.15 Hz at 1.4 g acceleration. Cylinder material and its effect with magnetic interaction is also studied. The magnetic force between two permanent magnets is measured experimentally. Effect of magnetic force on centre resonant frequency of beam is compared with experimental and simulated results. Effect of magnetic force on bandwidth of the device is studied.

     

Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system

Heart rate variability (HRV) parameters can be used as specific indicator of autonomic nervous system (ANS) behavior. ANS, with its main two branches, sympathetic and parasympathetic, may be considered as a coordinated neuronal network which controls heart rate continually. Many parameters define heart rate variability in different domains such as time, frequency or nonlinear. An excessively high computational complexity can occur when developing models for medical applications when the best set of inputs to use is not known. To build a model that can predict a specific process output, it is desirable to select a subset of variables that are truly relevant or the most influential to this output. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study an architecture for modeling complex systems in function approximation and regression was used, based on using adaptive neuro-fuzzy inference system (ANFIS). Variable searching using the ANFIS network was performed to determine how the ANS branches affect the most relevant HRV parameters. The method utilized may work as a basis for examination of ANS influence on HRV activity.     

斜对称轴结构的差分电容式地震计研制

基于斜对称轴的设计结构,提出了一种采用差分电容式位移型换能器的设计方法。利用加速度计电路耦合积分电路的方法实现差容式力平衡地震计的研制。实验结果表明:地震计在0.01—25HZ的频率范围内具有平坦的幅频特性曲线;线性度小于1%;动态范围大于120dB。