Frequency Domain Parameters Research Articles

EFFECT OF YOGA THERAPY ON FREQUENCY DOMAIN PARAMETERS OF HEART RATE VARIABILITY IN PREDIABETICS

Introduction: Prediabetes is the budding stage of type 2 diabetic mellitus and imposes the elevated risk for cardiovascular diseases (CVDs) with significant morbidity and mortality. Non pharmacological strategies such as yoga therapy have marked impact in the mitigation of prediabetes. In this backdrop, the present study was conducted to study the effect of six months yoga intervention on heart rate variability in patients diagnosed with prediabetes. Further, it has been used as tool for the maintenance of autonomic nervous system and to reduce the progression of diabetes. Material and Method: The study was conducted on 250 prediabetic adults aged ranging from 30-50 years attending the OPD of Jaipuria Hospital and from different yoga centers. Then the effect of yoga intervention on frequency domain parameters of heart rate variability such as Low frequency (LF),High frequency (HF), Ratio of LFto HF. The patients were divided into two groups as follows, Group A (n=125) were subjected to intervention with yoga exercises and Group B (n=125) were not advised for any yoga exercises. Data analysis was done by Oneway ANOVA for comparison between the groups and significance was analysed post hoc analysis followed by Tukeys and Schaffes tests. Results : In group A, yoga intervention leads to significant (p <0.001) decrease in LF component with an substantial elevation in HF component and the LF/HFratio shifted towards the parasympathetic dominance as compared to group Bat baseline, 3 and6 months. Conclusion: Thus the study reveals that yoga intervention for six months significantly shifted ANS system towards the parasympathetic dominance in prediabetic patients. Thus, routine yoga session could alleviate the cardiometabolic risk factors in individuals affected with prediabetes.

Effect of Different Local Vibration Frequencies on the Multiscale Regularity of Plantar Skin Blood Flow.

Local vibration has shown promise in improving skin blood flow (SBF). However, there is no consensus on the selection of the best vibration frequency. An important reason may be that previous studies utilized time- and frequency-domain parameters to characterize vibration-induced SBF responses. These parameters are unable to characterize the structural features of the SBF response to local vibrations, thus contributing to the inconsistent findings seen in vibration research. The objective of this study was to provide evidence that nonlinear dynamics of SBF responses would be an important aspect for assessing the effect of local vibration on SBF. Local vibrations at 100 Hz, 35 Hz, and 0 Hz (sham vibration) with an amplitude of 1 mm were randomly applied to the right first metatarsal head of 12 healthy participants for 10 min. SBF at the same site was measured for 10 min before and after local vibration. The degree of regularity of SBF was quantified using a multiscale sample entropy algorithm. The results showed that 100 Hz vibration significantly increased multiscale regularity of SBF but 35 Hz and 0 Hz (sham vibration) did not. The significant increase of regularity of SBF after 100 Hz vibration was mainly attributed to increased regularity of SBF oscillations within the frequency interval at 0.0095–0.15 Hz. These findings support the use of multiscale regularity to assess effectiveness of local vibration on improving skin blood flow.

Reliability of low electrocardiogram sampling frequencies for short-term heart rate variability analysis to estimate transient psychophysiological stress induced by car driving

Abstract Background Heart rate variability analysis (HRVa) is increasingly used to evaluate stress-induced adaptation of the autonomic nervous system (ANS), basing on electrocardiogram (ECG) recorded with novel wearable wireless devices (WWD) for physiological monitoring. However, in order to transfer ECG data to mobile phones efficiently, WWD use low sampling frequency (SF), usually ∼100 Hz, thus much lower than the 0.5–1 kHz, previously suggested to optimize R–R intervals detection, precise enough for HRVa in the time-domain (TD), frequency-domain (FD) and with nonlinear (NL) methods. Moreover, the latter are sensitive to SF as a function of data length. Aim Aim of this study was to quantify the relative error (RE%) of HRVa, when calculated from ECG downsampled at 100 Hz, compared with that digitized at 1 kHz, considering different data lengths. Methods ECG of four healthy professional pilots was continuously recorded with a WWD (Vi-grade, Udine), at 1 kHz SF, while driving a professional simulator, inducing different degree of psychophysiological stress by changing suddenly the vehicle's behavior without advising the driver. TD, FD and NL HRV parameters were calculated (Kubios 3.0.2, Finland), from time intervals of 300, 120, 60 and 30 seconds, of ECG downsampled at 100 Hz SF from original recordings acquired at 1 kHz SF (assumed as goldstandard). The RE% and the intraclass correlation coefficient (ICC) were calculated. Results A good correlation (ICC ≥0.79–0.84) was found for the majority of HRV parameters, in both driving conditions, for all selected intervals lengths. The average RE% ranged between zero and 3%, increasing if the length of the interval selected for HRVa was lower than 60 second (Table 1). However higher RE was occasionally found. In Figure 1, an example of HRVa calculated from 2 minutes tachogram's segments, at 100 Hz (left) and at 1 kHz SF (right) is shown. Conclusions Compared to 1 kHz SF, downsampling ECG at 100 Hz doesn't affect significantly HRVa for data lengths between 5 and 1 minutes. However, for shorter time intervals the RE increases. This must be taken into account if HRVa is used to track transient short-lasting changes of ASN modulation induced by acute stress. Figure 1 Funding Acknowledgement Type of funding source: None

Optimizing Intersession Reliability of Heart Rate Variability-The Effects of Artifact Correction and Breathing Type.

Gisselman, AS, D'Amico, M, and Smoliga, JM. Optimizing intersession reliability of heart rate variability-the effects of artifact correction and breathing type. J Strength Cond Res 34(11): 3199-3207, 2020-This study aimed to comprehensively investigate the reliability of multiple heart rate variability (HRV) parameters and to explore the influence of artifact removal and breathing condition on HRV reliability. Resting HRV was collected using Polar Team monitors on 41 participants (age: 19.9 ± 1.2 years; 28 females and 13 males) during 2 separate days. Within each session, participants performed 10 minutes each of spontaneous and controlled breathing (randomized order). Kubios HRV analysis software was used to analyze 180-second data epochs using "low" or "strong" artifact removal. Relative reliability was assessed using intraclass correlation coefficient (ICC2,1), and absolute reliability was quantified using mean-normalized SEM%. Time-domain and nonlinear parameters produced poor to good intersession agreement (ICC = 0.34-0.68; SEM% = 11.0-39.0) with "low" artifact removal, regardless of breathing condition. Frequency-domain parameters demonstrated fair intersession agreement during controlled breathing (ICC = 0.40-0.45; SEM% = 26.0-70.0) but poor agreement during spontaneous breathing (ICC = 0.07-0.13; SEM% = 32.0-81.0). Minimal differences in ICCs were observed between "low" and "strong" artifact removal. Thus, this study provides 3 practical applications: (a) HRV monitoring is most reliable when using time-domain and nonlinear parameters, regardless of breathing or filtering condition, but no single parameter is especially reliable. The large variation and poor intersession reliability of frequency indices during spontaneous breathing are improved by controlling breathing rate; (b) "Low" artifact removal seems superior to more aggressive artifact removal; and (c) intersession differences in HRV measurements <30% may be indicative of normal daily variation rather than true physiologic changes.

A Pilot Study of the Reliability and Agreement of Heart Rate, Respiratory Rate and Short-Term Heart Rate Variability in Elite Modern Pentathlon Athletes.

Research on reliability of heart rate variability (HRV) parameters in athletes has received increasing attention. The aims of this study were to examine the inter-day reliability of short-term (5 min) and ultra-short-term (1 min) heart rate (HR), respiratory rate (RespRate) and HRV parameters, agreement between short-term and ultra-short-term parameters, and association between differences in HR, RespRate and HRV parameters in elite modern pentathletes. Electrocardiographic recordings were performed in stable measurement conditions with a week interval between tests. Relative reliability was evaluated by intra-class correlation coefficients, absolute reliability was evaluated by within-subject coefficient of variation, and agreement was evaluated using Bland–Altman (BA) plot with limits of agreement and defined a priori maximum acceptable difference. Short-term HR, RespRate, log transformed (ln) root mean square of successive normal-to-normal interval differences (lnRMSSD), ln high frequency (lnHF) and SD2/SD1 HRV indices and ultra-short-term HR, RespRate and lnRMSSD presented acceptable, satisfactory inter-day reliability. Although there were no significant differences between short-term and ultra-short-term HR, RespRate and lnRMSSD, no parameter showed acceptable differences with BA plots. Differences in time-domain and non-linear HRV parameters were more correlated with differences in HR than with differences in RespRate. Inverse results were observed for frequency-domain parameters. Short-term HR, RespRate, lnRMSSD, lnHF, and SD2/SD1 and ultra-short-term HR, RespRate and lnRMSSD could be used as reliable parameters in endurance athletes. However, practitioners should interpret changes in HRV parameters with regard to concomitant differences in HR and RespRate and caution should be taken before considering 5 min and 1 min parameters as interchangeable.

Computerized dynamic pupillometry as a screening tool for evaluation of autonomic activity

ObjectivesDynamic pupillometry is a simple screening tool for quantifying pupillary light reflex (PLR), to indicate autonomic nervous system (ANS) activity. Heart rate variability (HRV) is the gold standard method for assessing ANS effects on the heart. The present study aimed to compare ANS activity as measured using dynamic pupillometry (DP) with short term HRV in healthy volunteers. MethodsThe study includes 200 participants aged between 20–60 years. PLR was measured using infrared videography and categorized into different quantitative parameters that reflect ANS activity. Simultaneously, Lead II ECG was recorded for 5 min to evaluate the short term HRV of time and frequency domain parameters. The data obtained from the two methods were compared with each other to provide the relationship between PLR and HRV. ResultsStudy participants’ mean age was 36.95 ± 9.45 years. The different pupillary indices of PLR and all the HRV parameters were within the range of normative data. Several PLR indicators of the parasympathetic function include mean constriction velocity (MCV; r = 0.60, P < 0.001), absolute constriction amplitude (ACA; r = 0.57, P < 0.001) baseline pupil diameter (BPD; r = 0.44, p < 0.001), and minimum pupil diameter (MPD; r = −0.35, p < 0.001) were significantly correlated with high-frequency power (HF in normalized units) of HRV signal. ConclusionPLR variables showed a significant relationship to HRV indices. Dynamic pupillometry can be a complementary tool to HRV for evaluating ANS activity.

Effectiveness of a Manual Therapy Program as Adjuvant Treatment for School-Age Children With Attention-Deficit/Hyperactivity Disorder: A Randomized Pilot Study

Attention-deficit/hyperactivity disorder (ADHD) is a neurobiological disorder. Common symptoms are inattention, hyperactivity, impulsivity, and executive functions deficit, often with comorbidities. Main treatment involves pharmacological and non-pharmacological therapies (cognitive-behavioral therapy, psycho-pedagogical programs). Emerging non-pharmacological treatments include manual therapies (MTs) in mental health. Several MT techniques have shown effectiveness in ADHD. The objectives of this study are as follows: (a) to assess short-term effects and persistence of an MT program on neuropsychological (hyperactivity index through the Conners’ Global Index [CGI]) and neurophysiological (time–frequency domain parameters of heart rate variability [HRV]) variables in ADHD children, and (b) to test the feasibility of the design. This study was approved by the Parc Tauli Corporation Clinical Research Ethics Board (#2017311). Pilot, controlled, multidisciplinary study of eight children with ADHD randomly assigned to control group (CG; n = 4) and intervention group (IG; n = 4). Both followed multimodal treatment, and IG also received an MT program per week for 4 weeks. A physiotherapist and a psychologist analyzed short-term effects (1-week post-program) and persistence (36 weeks). Eight children (boys, 62.5%; girls, 37.5%) with an average age of 10.375 ± 0.74 years were included. Baseline HRV parameters showed basal sympathetic predominance. The MT program increased the parasympathetic activity, which was significantly maintained at 1 to 36 weeks; there were no changes in CG. One-week post-program IG showed significant CGI reduction (–7 points, p = .016*), CG without changes ( p = .41), with significant differences CG – IG ( p = .0097*), and very large effect size ( d = 2.235); 36 weeks post-program showed no effects (IG, p = .293; CG, p = .247), without differences CG – IG ( p = .369). The MT program reduced the hyperactivity index with a 1-week persistence and increased the parasympathetic activity with a 36-week persistence. More investigations are needed to generalize the outcomes. Additional research is planned to further explore the possibilities of MT in psychiatric disorders with autonomic imbalances.

Reduced heart rate variability is associated with vulnerability to depression

• Resting heart rate variability was investigated in dysphoria and past depression. • Time- and frequency-domain parameters were assessed. • Dysphoria and past depression are characterized by reduced HF and SDNN parameters. • Using HRV to early identify people at risk for depression may be promising. Background: Heart rate variability (HRV) mirrors cardiac autonomic modulation, an index of well-being. Reduced HRV has been reported in depression, but few studies investigated HRV in individuals at-risk of or remitted from a full-blown depressive episode. The present study aimed at examining whether reduced HRV could be a potential indicator of vulnerability to depression. Methods: Self-reported psychological measures and three-minute resting-state ECG were collected in two at-risk populations [group with dysphoria ( n = 27), group with past depression ( n = 16)] and in a control group ( n = 25). Time- and frequency-domain HRV parameters were computed. Analysis of covariance was conducted to detect between-groups differences for each measure. Results: Standard Deviation of Normal to Normal intervals (SDNN) and High Frequency (HF) power of HRV were found to be reduced both in individuals with dysphoria and in those with past depression as compared with controls. Whereas psychological measures did not significantly differ among individuals with past depression and controls, HRV was capable of discriminating between the two groups. Limitations: Past depression was assessed retrospectively with self-reported information. The inclusion of a group with depression would provide an overview about HRV during the illness course. Conclusions: The findings suggest that reduced HRV is likely to be implicated in the risk of developing full-blown depression, rather than being a mere correlate of current depressive state. The results suggest that HRV may improve clinicians’ ability to early identify people at risk for depression who can benefit from targeted prevention by psychiatric and psychological interventions.

Binary symbolic dynamics analysis to detect stress-associated changes of nonstationary heart rate variability

Psychological stress may have harmful physiological effects and result in deteriorating health. Acute psychological stress acts also on cardiac autonomic regulation and may lead to nonstationarities in the interbeat interval series. We address the requirement of stationary RR interval series to calculate frequency domain parameters of heart rate variability (HRV) and use binary symbolic dynamics derived from RR interval differences to overcome this obstacle. 24 healthy subjects (12 female, 20–35 years) completed the following procedure: waiting period, Trier Social Stress Test to induce acute psychological stress, recovery period. An electrocardiogram was recorded throughout the procedure and HRV parameters were calculated for nine 5-min periods. Nonstationarities in RR interval series were present in all periods. During acute stress the average RR interval and SDNN decreased compared to rest before and after the stress test. Neither low frequency oscillations (LF), high frequency oscillations (HF) nor LF/HF could unambiguously reflect changes during acute stress in comparison to rest. Pattern categories derived from binary symbolic dynamics clearly identified acute stress and accompanying alterations of cardiac autonomic regulation. Methods based on RR interval differences like binary symbolic dynamics should be preferred to overcome issues related to nonstationarities.

Gearbox fault diagnosis through quantum particle swarm optimization algorithm and kernel extreme learning machine

Gearbox is the key component of mechanical transmission system. Accurate fault diagnosis of gearbox is of great significance to ensure the operation of rotating machinery. Based on the comprehensive simulation test-bed in the laboratory, a gearbox fault diagnosis method based on QPSO-KELM is proposed. Firstly, the fault pre planting experiments of gear fault, bearing fault and gear bearing mixed fault are carried out on the comprehensive simulation test-bed. Then, the vibration signals collected are preprocessed by TSA to eliminate noise. The time domain, frequency domain and NASA feature parameters of the preprocessed signals are taken as training samples and test samples of QPSO-KELM. The experimental results show that the proposed method can effectively solve the problem of gearbox fault pattern recognition, and the fault diagnosis accuracy is higher than traditional methods, so the research has certain reference significance and engineering application value.

Prediction of the subjective impression of passenger car roll dynamics on the driver based on frequency-domain characteristic values

Characteristic values are essential for the design and assessment of driving dynamics during the early stages of the development process of passenger cars. Compared to other aspects of vehicle dynamics however, the relationship between measurable parameters and the subjective perception of vehicle roll dynamics has not been researched extensively. In this paper, a study is presented in which several variants of a vehicle with an electronically controlled suspension were rated by test subjects regarding its roll dynamics and measured in a standardised driving manoeuvre. The subjective ratings and objective characteristic values were then used to derive models to predict the subjective liking of several roll dynamics aspects based on objective frequency-domain parameters which could be achieved with relatively high accuracy. The resulting prediction models were validated using measurements of additional vehicles even though this only allowed for a rough validation as no subjective ratings were available for the validation set. Nevertheless, the predictions for the validation variants seemed plausible and reflected the basic expectations regarding the vehicle characters of the validation set. The results of this investigation thus laid a good foundation for follow-up studies to further increase the reliability and robustness of the prediction models.

An Ultra-Wide Band Antenna System for Pulsed Sources Direction Finding

Electric discharges in high-voltage power distribution systems can be localized through their electromagnetic signature in the radio-frequency range. Since discharges produce series of short pulses, the corresponding spectrum usually covers wide frequency ranges, typically up to 1 GHz. In this paper, we propose an ultra-wide band (UWB) antenna system and a direction-finding (DF) approach based on using energy-based descriptors, instead of classical frequency-domain parameters. As an antenna system, we propose a dual-padlock configuration with a suitable pulse-matched response, featuring two unbalanced outputs. The proposed antenna system was successfully validated, both by simulations and measurements.

Structural condition monitoring and identification of laser cladding metallic panels based on an acoustic emission signal feature optimization algorithm

Laser cladding technology has become a central issue in industrial research and application recently. Due to the complexity of the processing and the difficulty of quantitative analysis, it is particularly important to effectively and reliably detect the forming quality of the cladding layer. Therefore, the laser cladding processing condition of metallic panels is monitored and identified based on acoustic emission technology in this article. Consequently, laser cladding acoustic emission signal of 316L stainless steel plate is first collected, and a multi-domain acoustic emission signal feature extraction method based on time–frequency domain and waveform parameters is constructed by integrating time–frequency, empirical, and inter-relationship diagraph analysis. Moreover, a feature optimization approach based on t-distributed stochastic neighbor embedding algorithm is proposed, which combines with correlation analysis to realize the de-redundancy and optimization of acoustic emission signal features. In addition, on the basis of optimizing the characteristics of acoustic emission signal, laser cladding acoustic emission signals under three technological parameters are collected. Second, a laser cladding condition identification method, which is a least square support vector machine algorithm based on niche particle swarm optimization, is proposed. The optimal parameter combination of niche particle swarm optimization algorithm is mainly selected to improve the accuracy of identification and classification. The results demonstrate the proposed t-distributed stochastic neighbor embedding feature optimization method can effectively extract the sensitive information related to the processing condition in the feature space, and the optimization of least square support vector machine parameters through niche particle swarm optimization can significantly improve the identification rate of classification. Specifically, the feasibility of the proposed t-distributed stochastic neighbor embedding–niche particle swarm optimization–least square support vector machine model to analyze laser cladding acoustic emission signal characteristics is verified, and the effectiveness of acoustic emission–based structural condition monitoring and identification of laser cladding plate-like structures is also demonstrated.

New Reconstruction Method for Needle Contrast Optimization in B-Mode Ultrasound Image by Extracting RF Signal Parameters in Frequency Domain

Ultrasound-guided needle insertion has become standard in medical interventional procedures. Regardless of its advantages, it still has crucial problems related to needle visibility. Some technical factors affect the visibility with non-linear characteristic, i.e. frequency, insertion angle and depth. Here, backscattered signal parameters from measurement were compared to a simulation of a resonance scattering model. Raw radio frequency (RF) data were reconstructed with a new method to represent unique information on total backpropagation from the needle, which consists of non-resonance and resonance scattering components. The result suggests that reconstruction of the needle in B-mode images should be derived from the maximum power spectral density and the energy spectral density to optimize the contrast of the needle. In measurements with the center frequency at 1.87 MHz, the effect of resonance scattering on the total backpropagation around critical angles could be observed more clearly with this method than with standard reconstruction based on the signal envelope. The simulation showed that the fractional bandwidth of the spectrum of the backscattered pressure field centered at 1.87 MHz was relatively optimal at 40% to 100%. So that the simulation of the resonance scattering model can be used to predict the backscattered response from the needle, it must be able to confirm it to the real conditions of RF data with random characteristics. Therefore, extraction of the backscattered pressure field in a simulation with fractional bandwidth should be a concern.

Fine particulate matter constituents and heart rate variability: A panel study in Shanghai, China

BackgroundShort-term exposure to fine particulate matter (PM2.5) has been associated with reduced heart rate variability (HRV), an established indicator of cardiac autonomic function, but it remains uncertain which specific constituents of PM2.5 had key impacts. ObjectiveTo examine the short-term associations between various PM2.5 constituents and HRV measures. MethodsWe conducted a retrospective panel study among 78 participants who received repeated 24-h electrocardiogram testing in Shanghai, China from 2015 to 2019. We obtained daily concentrations of 14 main chemical constituents of PM2.5 from a fixed-site monitor. During 3 or 4 rounds of follow-ups, we measured 6 HRV parameters, including 3 frequency-domain parameters (power in very low frequency, low frequency and high frequency) and 3 time-domain parameters (standard deviation of normal-to-normal intervals, root mean square successive difference and percent of adjacent normal R-R intervals with a difference ≥50 msec). We used linear mixed-effects models to analyze the data after controlling for time trends, environmental and individual risk factors. ResultsThe average daily PM2.5 exposure was 45.8 μg/m3 during the study period. The present-day exposure to PM2.5 had the strongest negative influences on various HRV indicators. These associations attenuated greatly on lag 1 d or lag 2 d. Elemental carbon, organic carbon, nitrate, sulfate, arsenic, cadmium, chromium and nickel were consistently associated with reduced HRV parameters in both single-constituent models and constituent-PM2.5 models. ConclusionOur study highlighted the key roles of traffic-related components of PM2.5 in inhibiting cardiac autonomic function.

Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses.

Introduction: Heart Rate Variability (HRV) and Pulse Rate Variability (PRV), are non-invasive techniques for monitoring changes in the cardiac cycle. Both techniques have been used for assessing the autonomic activity. Although highly correlated in healthy subjects, differences in HRV and PRV have been observed under various physiological conditions. The reasons for their disparities in assessing the degree of autonomic activity remains unknown.Methods: To investigate the differences between HRV and PRV, a whole-body cold exposure (CE) study was conducted on 20 healthy volunteers (11 male and 9 female, 30.3 ± 10.4 years old), where PRV indices were measured from red photoplethysmography signals acquired from central (ear canal, ear lobe) and peripheral sites (finger and toe), and HRV indices from the ECG signal. PRV and HRV indices were used to assess the effects of CE upon the autonomic control in peripheral and core vasculature, and on the relationship between HRV and PRV. The hypotheses underlying the experiment were that PRV from central vasculature is less affected by CE than PRV from the peripheries, and that PRV from peripheral and central vasculature differ with HRV to a different extent, especially during CE.Results: Most of the PRV time-domain and Poincaré plot indices increased during cold exposure. Frequency-domain parameters also showed differences except for relative-power frequency-domain parameters, which remained unchanged. HRV-derived parameters showed a similar behavior but were less affected than PRV. When PRV and HRV parameters were compared, time-domain, absolute-power frequency-domain, and non-linear indices showed differences among stages from most of the locations. Bland-Altman analysis showed that the relationship between HRV and PRV was affected by CE, and that it recovered faster in the core vasculature after CE.Conclusion: PRV responds to cold exposure differently to HRV, especially in peripheral sites such as the finger and the toe, and may have different information not available in HRV due to its non-localized nature. Hence, multi-site PRV shows promise for assessing the autonomic activity on different body locations and under different circumstances, which could allow for further understanding of the localized responses of the autonomic nervous system.

Speeding up the tracking and updating of the convolutional residual tracking networks

The convolutional residual tracking networks (CREST) uses a single-layer convolutional network as the implicit correlation filter, which can perform end-to-end training and has the advantages of simple model structure and high tracking accuracy. However, the forward and backward convolution computation in its base layer is too excessive, resulting in its slow tracking speed. In addition, the back-propagation algorithm is adopted as the fine-tuning method during online updating, thus the parameter updating efficiency is quite slow. When the object appearance changes rapidly, there is a large risk of losing the tracked object. In this paper, we improve the residual convolutional network model and its training method. The improved model explicitly uses the base layer as a correlation filter and uses the frequency domain parameters to describe the base layer. When updating online, the base layer is pre-trained using the standard learning method of the correlation filter to efficiently learn the main information of the object appearance, and then the back-propagation algorithm is utilized to optimize the entire model. Thus, both the forward and backward convolution computation in the improved base layer can be conducted efficiently in the frequency domain, which significantly reduces the amount of computation and improves the tracking speed and online update efficiency. Extensive experiments on the evaluation data sets validate the effectiveness of the improved method.

Correlation of Sympathovagal Imbalance with Disease Activity and Inflammatory Markers in South Indian Patients with Rheumatoid Arthritis

Background and Aim: Rheumatoid arthritis (RA) patients have higher risk of cardiovascular disease and mortality and that is not linked to traditional cardiovascular risk factors. Altered sympathovagal balance is associated with cardiovascular disease. In RA sympathovagal balance and inflammatory markers were reported to be increased, but there is inadequacy of data on its assessment and association with disease activity. This study was conducted to find the correlation of sympathovagal balance with disease activity and inflammatory markers in South Indian patients with RA. Methods: RA patients (diagnosis of RA made as per 2010 ACR/EULAR classification criteria) were enrolled from Department of Clinical Immunology, JIPMER. Participants were sub grouped as low disease activity (LDA), moderate disease activity (MDA) and severe disease activity (HDA) using disease activity score 28 with ESR. Frequency and time domain parameters of heart rate variability and inflammatory markers like Interleukin-1α (IL-1α), Interleukin-6 (IL-6), tumor necrosis factor-α (TNFα) and cortisol were measured for all the participants. Results: A total of 143 RA patients (23 in LDA group, 69 in MDA group and 51 in HDA group) were assessed during the study. LF-HF ratio (ratio of low frequency power to high frequency power) a marker of sympathovagal balance showed significant increase in MDA and HDA group compared to LDA group. LF-HF ratio correlated positively with DAS28 (r=0.473, p<0.001), cortisol (r=0.363, p<0.001), IL -1α (r=0.379, p<0.05). Conclusion: Sympathovagal imbalance was significantly increased in the MDA and HDA group compared to LDA group and positively correlated with disease activity, IL-1α and cortisol in the patients of RA. Assessment of sympathovagal balance may help to find the autonomic dysfunction and cardiovascular risk in RA.

Automatic intelligibility assessment of dysarthric speech using glottal parameters

Objective intelligibility assessment of dysarthric speech can assist clinicians in diagnosis of speech disorders as well as in medical treatment. This study investigates the use of glottal parameters (i.e. parameters that describe the acoustical excitation of voiced speech, the glottal flow) in the automatic intelligibility assessment of dysarthric speech. Instead of directly predicting the intelligibility of dysarthric speech using a single-stage system, the proposed method utilizes a two-stage framework. In the first stage, two-class severity classification of dysarthria is performed using support vector machines (SVMs). In the second stage, intelligibility estimation of dysarthric speech is computed using a linear regression model. Two sets of glottal parameters are explored: (1) time-domain and frequency-domain parameters and (2) parameters based on principal component analysis (PCA). Acoustic parameters proposed in a similar intelligibility prediction study by Falk et al. (2012) are used as baseline features. Evaluation results show that the two-stage framework leads to improvement in the intelligibility assessment measures (correlation and root mean square error) compared to the single-stage framework. The combination of the glottal parameters sets results in better performance in the severity classification and intelligibility estimation tasks compared to the baseline features.

A feasibility study towards instrumentation of the Sport Concussion Assessment Tool (iSCAT).

The Sports Concussion Assessment Tool (SCAT) is a pen and paper-based evaluation tool for use by healthcare professionals in the acute evaluation of suspected concussion. Here we present a feasibility study towards instrumented SCAT (iSCAT). Traditionally, a healthcare professional subjectively counts errors according to SCAT marking criteria matrix. It is hypothesized that an instrumented version of the test will be more accurate while providing additional digital-based parameters to better inform player management. The feasibility study focuses on the SCAT physical functioning tasks only: double leg stance, single-leg stance, tandem stance and tandem gait. Amateur university rugby players underwent iSCAT testing and data were recorded with 8 inertial units attached at different anatomical locations. Video data were gathered simultaneously as reference. An iSCAT algorithm was used to detect errors and quantify additional concussion-based time and frequency domain parameters to assess participant stability during balance and gait tasks. Future work aims to instrument other SCAT features such as hand-eye coordination while deploying methods within a large concussion project.