Power Spectral Density Values Research Articles

Changes in postural stability after cerebrospinal fluid tap test in patients with idiopathic normal pressure hydrocephalus.

In patients with idiopathic normal pressure hydrocephalus (iNPH), the characteristics of balance disturbance are not as well understood as those related to gait. This study examined changes in postural stability in quiet standing after the cerebrospinal fluid tap test (CSFTT) in these patients. Furthermore, the study explored the relationship between the amount of spontaneous body sway and both gait and executive function. All patients diagnosed with iNPH underwent CSFTT. We evaluated their center of pressure (COP) measurements on a force plate during quiet standing, both pre- and post-CSFTT. Following the COP measurements, we calculated COP parameters using time and frequency domain analysis and assessed changes in these parameters after CSFTT. At pre-CSFTT, we assessed the Timed Up and Go (TUG) and the Frontal Assessment Battery (FAB). We investigated the relationship between COP parameters and the TUG and FAB scores at pre-CSFTT. A total of 72 patients with iNPH were initially enrolled, and 56 patients who responded positively to CSFTT were finally included. Post-CSFTT, significant improvements were observed in COP parameters through time domain analysis. These included the velocity of COP (vCOP), root-mean-square of COP (rmsCOP), turn index, torque, and base of support (BOS), compared to the pre-CSFTT values (p < 0.05). In the frequency domain analysis of COP parameters post-CSFTT, there was a decrease in both the peak and average of power spectral density (PSD) values in both the anteroposterior (AP) and mediolateral (ML) directions below 0.5 Hz (p < 0.05). In addition, the TUG scores showed a positive correlation with vCOP, rmsCOP, turn index, torque, BOS, and both the peak and average PSD values in the AP and ML directions below 0.5 Hz (p < 0.05). The FAB scores demonstrated a negative correlation with vCOP, rmsCOP, turns index, BOS, and both peak and average PSD values in the AP direction below 0.5 Hz (p < 0.05). In patients with iNPH who responded to CSFTT, there was an improvement in spontaneous body sway during quiet standing after CSFTT. Increased spontaneous sway is associated with impaired gait and frontal lobe function. This may be linked to impaired cortico-cortical and cortico-subcortical circuits in patients with iNPH.

Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography.

The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets. Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared. PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area. Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.

Threshold of anthropogenic sound levels within protected landscapes in Kerala, India, for avian habitat quality and conservation.

Anthrophony is an important determinant of habitat quality in the Anthropocene. Acoustic adaptation of birds at lower levels of anthrophony is known. However, threshold anthrophony, beyond which biophony starts decreasing, is less explored. Here, we present empirical results of the relationship between anthrophony and biophony in four terrestrial soundscapes. The constancy of the predicted threshold vector normalised anthropogenic power spectral density (~ 0.40 Watts/Hz) at all the study sites is intriguing. We propose the threshold value of anthropogenic power spectral density as an indicator of the avian acoustic tolerance level in the study sites. The findings pave the way to determine permissible sound levels within protected landscapes and directly contribute to conservation planning.

Pre-processing data and window function testing on wave spectrum analysis

Pre-processing data in oceanographic data research is an important step in processing data into the required information. This phase often takes longer and more rigorous time to present the correct data. Data pre-processing involves the selection of data, and the modification of data so that it can be readable in a computational algorithm. The data selection process includes selection of sample numbers, correction and filling of data gaps.The data used in spectrum analysis is the recording of the water surface profile of the wave in the data resolution of 1 second. The data needs to be corrected by the process of detrending against the tidal height which become the still water level. The spectral analysis is processed using the pwelch function on MATLAB. The frequency spectrum curve of the measurement is obtained from the calculation of the power spectral density value. In order to obtain a stable power spectral density curve, the Hann window function is used with the number of window elements, nfft/16.

Electroencephalographic power spectrum changes in cerebral small vessel disease combined with cognitive dysfunction and its relationship with neutrophil/lymphocyte ratio and its clinical value - a pilot study.

The study aimed to explore the changes in the electrical power spectrum of the brain and its correlation with neutrophil/lymphocyte ratio (NLR) in patients with cognitively impaired cerebral small vessel disease (CSVD) and to explore its clinical application. A total of 61 patients with CSVD who attended the People's Hospital of Shaanxi Province from September 2021 to September 2022 were divided into the group with cognitive impairment (cerebral small vascular with cognitive impairment, CSVCI group, n = 29) and the group without cognitive impairment (CSVD group, n = 32) based on the Montreal Cognitive Assessment Scale (MoCA) score, while 20 healthy subjects were recruited as the control group (healthy control, HC group). EEG was performed in the three groups, and the difference in whole brain quantitative EEG power spectral density (PSD) was calculated and compared between the three groups. The PSD values in the δ and θ bands of the CSVCI group were higher than those of the CSVD group, while the PSD values in the α band were lower than those of the CSVD and HC groups. In addition, PSD values in the δ-band in the CSVD group were lower than those in the HC group (all p < 0.05). Multifactorial logistic regression showed that reduced α-band global average PSD and low years of education were independent risk factors for cognitive impairment in patients with CSVD (p < 0.05). In patients with cerebral small-vessel disease, α-band PSD was positively and δ-band PSD negatively correlated with MoCA score, and paraventricular, deep white matter, and total Fazekas scores were negatively correlated with MoCA score. Furthermore, θ-band PSD is positively correlated with NLR (all p < 0.05). EEG activity was slowed down in patients with CSVD with cognitive impairment. The α-band global mean PSD values independently affected the occurrence of cognitive impairment in CSVD patients beyond the Fazekas score. NLR may be one of the mechanisms leading to the slowing down of the EEG, which can be used as an objective indicator for the early prediction of cognitive impairment but still needs to be clarified by further studies.

Electroencephalography-based biological and functional characteristics of spinal cord injury patients with neuropathic pain and numbness.

To identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI patients with NP or numbness. The EEG signals during rest, as well as left- and right-hand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the θ (4-8 Hz), α (8-12 Hz), and β (13-30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation. The specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower θ and α bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all p < 0.05), but higher β band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all p < 0.05). During left-hand and feet MI, in the lower frequency bands (θ and α bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (β band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group. The differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness.

A wavelet based PSD approach for fault detection and classification in grid connected inverter interfaced microgrid

The recent progress in the area of microgrid protection has led to the application of data-driven and signal processing techniques for fault detection in microgrids. The deployment of inverter interfaced distributed generators makes the traditional protection schemes ineffective. In this paper a wavelet based power spectral density (PSD) has been utilized to detect fault, identify faulty phases and locate fault in the microgrid. Wavelet based PSD decouples the frequency information from the time information and it is the optimal technique to reveal the change of distribution of harmonics in non-stationary faulty signals as for fault analysis it can consider the faulty frequency band instead of one particular harmonic. The proposed technique can detect fault and identify faulty phases by analyzing current signals from the substation only. The normalized value of PSD of three phase current signals has been considered for fault detection and probabilistic distribution of PSD has been considered for faulty phase identification. The normalized value of PSD of the current signals collected from both ends of the main feeders and one end of the non-DG sub-feeder have been used to locate the faulty feeder and sub-feeder. Appropriate threshold values have been considered to detect fault and to locate the faulty feeder. To locate faults, the technique does not require synchronized signals and thus provides a promising and economical solution for inverter interfaced microgrid. It does not require huge amount of data set for training and testing as it is essentially a threshold based technique and also does not require collection of data at high sampling rate. The proposed scheme has been extensively tested for high impedance fault (HIF), unbalanced loading, load switching, variation of DG parameters, measurement uncertainty and presence of noise in the signal with signal to noise ratio up to 10 dB. This technique provides accurate results for fault resistance from 0 Ω to 300 Ω and up to 10% measurement error. The performance of the proposed technique has been verified for feeder length variation up to 20%. Half cycle of current signal only after fault is required and three levels of wavelet decomposition has been considered. The simulation results and comparative assessment demonstrate the efficacy of the proposed scheme.

Caregiver-child interaction as an effective tool for identifying autism spectrum disorder: evidence from EEG analysis

BackgroundAutism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects individuals across their lifespan. Early diagnosis and intervention are crucial for improving outcomes. However, current diagnostic methods are often time-consuming, and costly, making them inaccessible to many families. In the current study, we aim to test caregiver-child interaction as a potential tool for screening children with ASD in clinic.MethodsWe enrolled 85 preschool children (Mean age: 4.90 ± 0.65 years, 70.6% male), including ASD children with or without developmental delay (DD), and typical development (TD) children, along with their caregivers. ASD core symptoms were evaluated by Childhood Autism Rating Scale (CARS) and Autism Diagnostic Observation Schedule-Calibrated Severity Scores (ADOS-CSS). Behavioral indicators were derived from video encoding of caregiver-child interaction, including social involvement of children (SIC), interaction time (IT), response of children to social cues (RSC), time for caregiver initiated social interactions (GIS) and time for children initiated social interactions (CIS)). Power spectral density (PSD) values were calculated by EEG signals simultaneously recorded. Partial Pearson correlation analysis was used in both ASD groups to investigate the correlation among behavioral indicators scores and ASD symptom severity and PSD values. Receiver operating characteristic (ROC) analysis was used to describe the discrimination accuracy of behavioral indicators.ResultsCompared to TD group, both ASD groups demonstrated significant lower scores of SIC, IT, RSC, CIS (all p values < 0.05), and significant higher time for GIS (all p values < 0.01). SIC scores negatively correlated with CARS (p = 0.006) and ADOS-CSS (p = 0.023) in the ASD with DD group. Compared to TD group, PSD values elevated in ASD groups (all p values < 0.05), and was associated with SIC (theta band: p = 0.005; alpha band: p = 0.003) but not IQ levels. SIC was effective in identifying both ASD groups (sensitivity/specificity: ASD children with DD, 76.5%/66.7%; ASD children without DD, 82.6%/82.2%).ConclusionOur results verified the behavioral paradigm of caregiver-child interaction as an efficient tool for early ASD screening.

The effects of non-Newtonian fluid material midsole footwear on tibial shock acceleration and attenuation.

Introduction: Given the possibility of higher ground temperatures in the future, the pursuit of a cushioning material that can effectively reduce sports injuries during exercise, particularly one that retains its properties at elevated temperatures, has emerged as a serious concern. Methods: A total of 18 man recreational runners were recruited from Ningbo University and local clubs for participation in this study. Frequency analysis was employed to investigate whether there is a distinction between non-Newtonian (NN) shoes and ethylene vinyl acetate (EVA) shoes. Results: The outcomes indicated that the utilization of NN shoes furnished participants with superior cushioning when engaging in a 90° cutting maneuver subsequent to an outdoor exercise, as opposed to the EVA material. Specifically, participants wearing NN shoes exhibited significantly lower peak resultant acceleration (p = 0.022) and power spectral density (p = 0.010) values at the distal tibia compared to those wearing EVA shoes. Moreover, shock attenuation was significantly greater in subjects wearing NN shoes (p = 0.023) in comparison to EVA shoes. Performing 90° cutting maneuver in NN shoes resulted in significantly lower peak ground reaction force (p = 0.010), vertical average loading rate (p < 0.010), and vertical instantaneous loading rate (p = 0.030) values compared to performing the same maneuvers in EVA shoes. Conclusion: The study found that the PRA and PSD of the distal tibia in NN footwear were significantly lower compared to EVA footwear. Additionally, participants exhibited more positive SA while using NN footwear compared to EVA. Furthermore, during the 90° CM, participants wearing NN shoes showed lower PGRF, VAIL, and VILR compared to those in EVA shoes. All these promising results support the capability of NN footwear to offer additional reductions in potential injury risk to runners, especially in high-temperature conditions.

Numerical Study on the Unusual Vibration Load Characteristics and Mechanisms of the Front Landing Gear Compartment

Civilian aircraft can experience noticeable vibrations in the cockpit and cabin due to mechanical faults during flight. To address this issue, a hybrid approach was utilized to investigate fluid-induced vibration load characteristics in the front landing gear compartment under different hatch opening angles. The results reveal that the root mean square (RMS) of cumulative pressure loads on both small and large hatches under different opening angles is largest at a 15°. For all the simulated cases (0°, 5°, 10°, 15°, 20°), the power spectral density (PSD) results of the chosen monitoring points on the inner wall of the large hatch exhibit broadband frequency characteristics, and the peak PSD values for the chosen monitoring points on the outer wall of the small hatch exhibit a significant concentration of energy at approximately 75 Hz. The peak PSD values for the selected monitoring points on the inner wall of the small hatch demonstrate a more uniform distribution of energy. Utilizing the iso-surface of Q-criterion, spatial streamlines, and streamlines at different cross-sections to analyze flow characteristics, the study investigates the fluctuating load mechanisms of the compartments. The results indicate that unsteady loads stem from the blunt edges of the hatches, which induce unsteady flow and spanwise flow. Geometric gaps between different locations cause flow separation, and the flows inside the compartment exhibit characteristics similar to those of a clean cavity. Furthermore, the mutual interference can be described using circulating flow and spanwise flow, resulting in flow unsteadiness. The flow separation zones enlarge and vortex intensity increases with the increase of the hatch opening angle from 0° to 15°; then, their values decrease as the hatch opening angle increases from 15° to 20°. These variations explain the maximum RMS of cumulative pressure loads at 15°.

Legendre pairs of lengths ℓ ≡ 0 (mod 5)

Abstract By assuming a type of balance for length ℓ = 87 \ell =87 and nontrivial subgroups of multiplier groups of Legendre pairs (LPs) for length ℓ = 85 \ell =85 , we find LPs of these lengths. We then study the power spectral density (PSD) values of m m compressions of LPs of length 5 m 5m . We also formulate a conjecture for LPs of lengths ℓ ≡ 0 \ell \equiv 0 (mod 5) and demonstrate how it can be used to decrease the search space and storage requirements for finding such LPs. The newly found LPs decrease the number of integers in the range ≤ 200 \le 200 for which the existence question of LPs remains unsolved from 12 to 10.

Experimental study on vibration suppression for robotic milling using an MRE absorber

During the robotic milling process, vibration is one of the main factors that affect the machining accuracy and surface quality due to the low stiffness of the robot structure. Intelligent material, magneto-rheological elastomer (MRE), owns the features of adjustable stiffness, reversible and fast response, has been gradually used for vibration suppression. Therefore, to suppress the forced vibration for robotic milling, this paper used the principle of vibration absorption and combined it with MRE material to design a snubber. A series of excitation and milling experiments were carried out to evaluate its vibration attenuation characteristics. In the excitation experiment, sine signals with frequencies of 40 Hz, 42.02 Hz and 60 Hz with the same amplitude of 5 N were applied to the MRE absorber. In the milling experiment, the dominant frequencies under speeds of 800 RPM, 840 RPM and 1200 RPM were analyzed first. Then, the vibration mitigation characteristics of the MRE absorber during robotic milling of aluminum alloy were explored by adjusting the current. Experimental results showed that under the action of MRE absorber, the acceleration responses are suppressed effectively. In detail, after tuning the current, the amplitude and the root mean square of vibration acceleration in three directions of sinusoidal excitation could be suppressed by more than 19.23% and 7.35% correspondingly. The power spectral density value of the peak frequency could be reduced by more than 69.13%. In milling experiments, the MRE absorber also achieved certain vibration absorption effects. Furthermore, the surface roughness of the workpiece was also fallen by more than 16.51% which provides support for further engineering application in the field of robotic milling. Undoubtedly, the absorber needs further optimization to achieve better vibration suppression ability and a larger working bandwidth.

Quantification and Analysis of Carrier-to-Interference Ratio in High-Throughput Satellite Systems

Recently, the demand for high data traffic transmission is experiencing unstoppable growth, and multi-beam high-throughput satellite (HTS) systems have proven to be effective solutions. However, how to manage and eliminate the co-channel interference caused by frequency reuse in multi-beam HTS systems is still a challenging issue. Motivated by this background, this paper begins with discussing the interference mechanism in the uplink and downlink of HTS systems and then uses the quantification method of carrier-to-interference ratio (CIR), whereby the influencing factors of the CIR are accordingly analyzed. In addition, the common CIR selection strategies are summarized and compared with each other in terms of their advantages and disadvantages. Based on the proposed CIR quantification and statistical selection methods, two frequency plan cases are simulated for the scenario of 19 beams with 4-color reuse. The results show that the proposed quantification and selection methods are rather practical and offer a feasible approach for frequency plan adjustment based on CIR optimization. Finally, a simulation analysis of the CIR under different power spectral density (PSD) values is conducted, and the results show that the PSD has a substantial impact on the CIR.

Mapping EEG Alpha Activity: Assessing Concentration Levels during Player Experience in Virtual Reality Video Games

This work presents a study in which the cognitive concentration levels of participants were evaluated using electroencephalogram (EEG) measures while they were playing three different categories of virtual reality (VR) video games: Challenging Puzzlers, Casual Games, and Exergames. Thirty-one voluntary participants between the ages of 17 and 35 were recruited. EEG data were processed to analyze the brain’s electrical activity in the alpha band. The values of power spectral density (PSD) and individual alpha frequency (IAF) of each participant were compared to detect changes that could indicate a state of concentration. Additionally, frontal alpha asymmetry (FAA) between the left and right hemispheres of the brain was compared. The results showed that the Exergame category of video games elicited higher average cognitive concentration in players, as indicated by the IAF and FAA values. These findings contribute to understanding the cognitive effects of VR video games and their implications for designing and developing VR experiences to enhance cognitive abilities.

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective and accurate diagnosis is difficult. The disease is complex, progressive, and very common. There are more than 10 million people worldwide suffering from PD. In this study, an EEG-based deep learning model was proposed for the automatic detection of PD to support experts. The EEG dataset comprises signals recorded by the University of Iowa from 14 PD patients and 14 healthy controls. First of all, the power spectral density values (PSDs) ​​of the frequencies between 1 and 49Hz of the EEG signals were calculated separately using periodogram, welch, and multitaper spectral analysis methods. 49 feature vectors were extracted for each of the three different experiments. Then, the performances of support vector machine, random forest, k-nearest neighbor, and bidirectional long-short-term memory (BiLSTM) algorithms were compared using the PSDs feature vectors. After the comparison, the model integrating welch spectral analysis and the BiLSTM algorithm showed the highest performance as a result of the experiments. The deep learning model achieved satisfactory performance with 0.965 specificity, 0.994 sensitivity, 0.964 precision, 0.978 f1-score, 0.958 Matthews correlation coefficient, and 97.92% accuracy. The study is a promising attempt to detect PD from EEG signals and it also provides evidence that deep learning algorithms are more effective than machine learning algorithms for EEG signal analysis.

Application of Transcranial Magnetic Stimulation with Electroencephalography in the Evaluation of Brain Function Changes after Stroke.

Based on transcranial magnetic stimulation (TMS) with electroencephalography technology, this study analyzed the rehabilitation mechanism of patients' motor function reconstruction and nerve remodeling after stroke. It revealed the function of the cerebral cortex network at a deeper level and established a set of prognostic marker evaluation indicators for the reconstruction of motor function after stroke. Twenty-one patients treated at the Beijing Rehabilitation Hospital of Capital Medical University because of ischemic stroke in the territory supplied by the middle cerebral artery were selected as the experimental group. Neurophysiological evaluation, motor function evaluation, and clinical evaluation were performed 30 and 180 d after the onset of ischemic stroke. In the control group, neurophysiological evaluation was also performed as a reference index to evaluate the changes in cortical patterns after stroke. The brain topographic map showed the changes in energy or power spectral density (PSD) at 1,000 ms after stimulation as compared with before stimulation, but no difference was detected in these patients. The time-frequency analysis showed that when the left primary motor cortex (M1) area was stimulated using TMS, the PSD values of the left and right M1 and posterior occipital cortex areas produced an 8-40 Hz wave band in patients S1-S11. There was no significant energy change in patients S12-S16. For patients with different injury types, degrees of injury, and different onset periods, individualized intervention methods should be adopted. The evaluation methods should be as diverse as possible, and the rehabilitation effects of patients should be assessed from multiple perspectives to avoid the limitations of single factors. Possible mechanism: After brain injury, the nervous system can change its structure and function through different ways and maintain it for a certain period of time. This plasticity change will change with the course of the disease.

Characteristics of changes in the functional status of the brain before and after 1,000 m all-out paddling for different levels of dragon boat athletes.

Dragon boat is a traditional sport in China, but the brain function characteristics of dragon boat athletes are still unclear. Our purpose is to explore the changing characteristics of brain function of dragon boat athletes at different levels before and after exercise by monitoring the changes of EEG power spectrum and microstate of athletes before and after rowing. Twenty-four expert dragon boat athletes and 25 novice dragon boat athletes were selected as test subjects to perform the 1,000 m all-out paddling exercise on a dragon boat dynamometer. Their resting EEG data was collected pre- and post-exercise, and the EEG data was pre-processed and then analyzed using power spectrum and microstate based on Matlab software. Post-Exercise, the Heart Rate peak (HR peak), Percentage of Heart Rate max (HR max), Rating of Perceived Exertion (RPE), and Exercise duration of the novice group were significantly higher than expert group (p < 0.01). Pre-exercise, the power spectral density values in the δ, α1, α2, and β1 bands were significantly higher in the expert group compared to the novice group (p < 0.05). Post-exercise, the power spectral density values in the δ, θ, and α1 bands were significantly lower in the expert group compared to the novice group (p < 0.05), the power spectral density values of α2, β1, and β2 bands were significantly higher (p < 0.05). The results of microstate analysis showed that the duration and contribution of microstate class D were significantly higher in the pre-exercise expert group compared to the novice group (p < 0.05), the transition probabilities of A → D, C → D, and D → A were significantly higher (p < 0.05). Post-exercise, the duration, and contribution of microstate class C in the expert group decreased significantly compared to the novice group (p < 0.05), the occurrence of microstate classes A and D were significantly higher (p < 0.05), the transition probability of A → B was significantly higher (p < 0.05), and the transition probabilities of C → D and D → C were significantly lower (p < 0.05). The functional brain state of dragon boat athletes was characterized by expert athletes with closer synaptic connections of brain neurons and higher activation of the dorsal attention network in the resting state pre-exercise. There still had higher activation of cortical neurons after paddling exercise. Expert athletes can better adapt to acute full-speed oar training.

Does the patellar tendon reflex affect the postural stability in stroke patients with blocked vision?

Stroke patients often show postural instability. The patellar tendon reflex is a basic physical examination for stroke patients. This study aimed to explore the correlation between patellar tendon reflex grade and postural stability among stroke patients. A total of 37 elderly stroke patients, each with the same quadriceps muscle strength but different patellar tendon reflex levels, were tested on a force platform under eyes-open (EO) and eyes-closed (EC) conditions. Parametric analysis, detrended fluctuation analysis (DFA), and power spectral density (PSD) analysis were used in centre of pressure (COP) signal processing. The correlation between the results of measured data processing and the level of patellar tendon reflex was analysed. All three parameters of COP (the length of the sway trajectory, the mean range of the sway trajectory in the mediolateral [ML] direction [R x ], and the mean range of the sway trajectory in the anterior-posterior [AP] directions [R y ]) were negatively correlated with the patient's patellar tendon reflex grade under the EC condition. The DFA results showed that a higher grade of patellar tendon reflex was associated with a smaller value of the crossover point in the AP direction. Only the PSD values of each frequency band in the AP direction were negatively correlated with patellar tendon reflex grade with EO and became negatively correlated in both AP and ML directions with EC. Overall, the results showed a strong correlation between patellar tendon reflex and postural stability in stroke patients when vision was blocked. The strong correlation with EC may provide insights into clinic evaluation and treatment for rehabilitation or fall risks of stroke patients.

Sleep duration at night affects daytime wake EEG spectra

BackgroundSleep deprivation leads to decreased performance, alertness and degradation in the health status of a person. Often the person remains unaware of the reduced alertness and may end up taking inaccurate decisions. There was a need to study the sleep duration of college goers and to study the effect of total night-time sleep duration on daytime Electroencephalogram (EEG) characteristics. MethodsA total of 30 asymptomatic volunteers were enrolled in a cross-sectional study. A baseline sleep diary for fourteen days was taken and one daytime recording was done while carrying out their normal daytime activity. EEG, electrooculography (EOG) and electromyography (EMG) were recorded, which were used for the scoring of their sleep-wake using the American Academy of Sleep Medicine criteria. EEG data were analysed using fast Fourier transform. Fast Fourier transform was done for sleep and wake durations using DOMINO software (©SOMNOMEDICS, Germany). A Python library was used to calculate power spectral density (PSD) in EEG frequency bands. ResultsA beta band was significantly reduced in the first 45 min of the recording as compared to the baseline. K means cluster showed clustering of two groups with a significant reduction of daytime sleep delta PSD values with reduced total sleep time and time in bed the previous night. The study concluded that subjects with reduced night-time sleep duration generated significantly lesser sleep delta PSD values. ConclusionReduced duration of night-time sleep was associated with worse nap time sleep delta PSD values and vice versa. Interestingly, good quality sleep at night correlated better with delta PSD values of daytime recording.

Efficient method based on recurrent neural networks for pavement evenness detection

Surface evenness is an essential indicator for pavement evaluation; however, there is a lack of economical, efficient, and end-to-end assessment methods. This study proposes a pavement evenness estimation framework based on advanced recurrent neural networks. First, the method uses a 3D acceleration sensor to acquire the unsprung mass acceleration signal. The network is trained using a combination of acceleration and velocity inputs. Second, the trained model outputs the power spectral density (PSD) value. The R2 of the results reached 0.9880, and the classification accuracies were all above 91.7%, as evaluated by the simulation tests. Transfer learning is then used to transfer the pre-learned knowledge between similar vehicles. Finally, experiments were conducted in the field to estimate the PSD and to categorize six different pre-selected roads. The experimental results demonstrate that the method is robust to speed. The proposed framework achieves a cost-effective and efficient detection of pavement evenness.