A vibration sensor approach to detect intra-articular needle tip placement in the knee joint: a proof-of-concept study

Abstract Background Intra-articular injection in the dry knee joint is technically challenging particularly for the beginners. The aim of this study was to investigate the possible use of the vibration sensor to detect if the needle tip was at the knee intra-articular position by characterizing the frequency component of the vibration signal during empty syringe air injection. Methods Two milliliters of air were injected supero-laterally at extra- and intra-articular positions of a cadaveric knee joint, using needles of size 18, 21 and 24 gauge (G). Ultrasonography was used to confirm the positions of needle tip. A piezoelectric accelerometer was mounted medially on the knee joint to collect the vibration signals which were analyzed to characterize the frequency components of the signals during injections. Results The vibration frequency band power in the range of 500–1500 Hz was visually observed to potentially localize the needle tip placement during air injection whether they were at the knee extra-articular or intra-articular positions, as demonstrated by the higher band power (over − 40 dB or dB) for all the needle sizes. The differences of frequency band power between extra- and intra-articular positions were 18.1 dB, 26.4 dB and 39.2 dB for the needle size 18G, 21G and 24G respectively. The largest difference in spectral power was found in the smallest needle diameter (24G). Conclusions A vibration sensor approach was preliminarily proved to distinguish the intra-articular from extra-articular needle placement in the knee joint. This study demonstrated a possible implementation of an alternative electronic device based on this technique to detect the intra-articular knee injection.

Download Full-text

A Vibration Sensor Approach to Detect Intra-Articular Needle Tip Placement in the Knee Joint

10.21203/rs.3.rs-509027/v1 ◽

2021 ◽

Author(s):

Rit Apinyankul ◽

Kritsada Siriwattanasit ◽

Kakanand Srungboon ◽

Witchaporn Witayakom ◽

Weerachai Kosuwan

Keyword(s):

Knee Joint ◽

Frequency Band ◽

Electronic Device ◽

Vibration Signal ◽

Frequency Component ◽

Air Injection ◽

Vibration Sensor ◽

Needle Size ◽

Band Power ◽

Needle Diameter

Abstract Background: Intra-articular injection in the dry knee joint is technically challenging particularly for the beginners. The aim of this study was to investigate the possible use of the vibration sensor to detect if the needle tip was at the knee intra-articular position by characterizing the frequency component of the vibration signal during empty syringe air injection.Methods: Two milliliters of air were injected supero-laterally at extra- and intra-articular positions of a cadaveric knee joint, using needles of size 18, 21 and 24 gauge. Ultrasonography was used to confirm the positions of needle tip. A piezoelectric accelerometer was mounted medially on the knee joint to collect the vibration signals which were analyzed to characterize the frequency components of the signals during injections. Results: The vibration frequency band power in the range of 500-1,500 Hertz was visually observed to potentially localize the needle tip placement during air injection whether they were at the knee extra-articular or intra-articular positions, as demonstrated by the higher band power (over -40 decibel or dB) for all the needle sizes. The differences of frequency band power between extra- and intra-articular positions were 18.1 dB, 26.4 dB and 39.2 dB for the needle size 18, 21 and 24 gauge respectively. The most obvious difference was found in the smallest needle diameter.Conclusions: A vibration sensor approach was preliminarily proved to distinguish the intra-articular from extra-articular needle placement in the knee joint. This study demonstrated a possible alternative electronic device implementation of this technique to detect the intra-articular knee injection.

Download Full-text

Source-level Cortical Power Changes for Xenon and Nitrous Oxide–induced Reductions in Consciousness in Healthy Male Volunteers

Anesthesiology ◽

10.1097/aln.0000000000003169 ◽

2020 ◽

Vol 132 (5) ◽

pp. 1017-1033 ◽

Cited By ~ 1

Author(s):

Andria Pelentritou ◽

Levin Kuhlmann ◽

John Cormack ◽

Steven Mcguigan ◽

Will Woods ◽

...

Keyword(s):

Nitrous Oxide ◽

Frequency Band ◽

Spectral Power ◽

Spectral Band ◽

Brain State ◽

Alpha Power ◽

Source Power ◽

Band Power ◽

Source Level ◽

Oxide Anesthesia

Abstract Background Investigations of the electrophysiology of gaseous anesthetics xenon and nitrous oxide are limited revealing inconsistent frequency-dependent alterations in spectral power and functional connectivity. Here, the authors describe the effects of sedative, equivalent, stepwise levels of xenon and nitrous oxide administration on oscillatory source power using a crossover design to investigate shared and disparate mechanisms of gaseous xenon and nitrous oxide anesthesia. Methods Twenty-one healthy males underwent simultaneous magnetoencephalography and electroencephalography recordings. In separate sessions, sedative, equivalent subanesthetic doses of gaseous anesthetic agents nitrous oxide and xenon (0.25, 0.50, and 0.75 equivalent minimum alveolar concentration–awake [MACawake]) and 1.30 MACawake xenon (for loss of responsiveness) were administered. Source power in various frequency bands were computed and statistically assessed relative to a conscious/pre-gas baseline. Results Observed changes in spectral-band power (P < 0.005) were found to depend not only on the gas delivered, but also on the recording modality. While xenon was found to increase low-frequency band power only at loss of responsiveness in both source-reconstructed magnetoencephalographic (delta, 208.3%, 95% CI [135.7, 281.0%]; theta, 107.4%, 95% CI [63.5, 151.4%]) and electroencephalographic recordings (delta, 260.3%, 95% CI [225.7, 294.9%]; theta, 116.3%, 95% CI [72.6, 160.0%]), nitrous oxide only produced significant magnetoencephalographic high-frequency band increases (low gamma, 46.3%, 95% CI [34.6, 57.9%]; high gamma, 45.7%, 95% CI [34.5, 56.8%]). Nitrous oxide—not xenon—produced consistent topologic (frontal) magnetoencephalographic reductions in alpha power at 0.75 MACawake doses (44.4%; 95% CI [−50.1, −38.6%]), whereas electroencephalographically nitrous oxide produced maximal reductions in alpha power at submaximal levels (0.50 MACawake, −44.0%; 95% CI [−48.1,−40.0%]). Conclusions Electromagnetic source-level imaging revealed widespread power changes in xenon and nitrous oxide anesthesia, but failed to reveal clear universal features of action for these two gaseous anesthetics. Magnetoencephalographic and electroencephalographic power changes showed notable differences which will need to be taken into account to ensure the accurate monitoring of brain state during anaesthesia. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Download Full-text

Estimation of Flow Rate Through Analysis of Pipe Vibration

Acta Mechanica et Automatica ◽

10.2478/ama-2018-0045 ◽

2018 ◽

Vol 12 (4) ◽

pp. 294-300 ◽

Cited By ~ 1

Author(s):

Santhosh K. Venkata ◽

Bhagya R. Navada

Keyword(s):

Neural Network ◽

Fluid Flow ◽

Flow Rate ◽

Back Propagation ◽

Vibration Signal ◽

Vibration Sensor ◽

Percentage Error ◽

Fluid Flow Rate ◽

Bee Colony ◽

Neural Network Algorithm

Abstract In this paper, implementation of soft sensing technique for measurement of fluid flow rate is reported. The objective of the paper is to design an estimator to physically measure the flow in pipe by analysing the vibration on the walls of the pipe. Commonly used head type flow meter causes obstruction to the flow and measurement would depend on the placement of these sensors. In the proposed technique vibration sensor is bonded on the pipe of liquid flow. It is observed that vibration in the pipe varies with the control action of stem. Single axis accelerometer is used to acquire vibration signal from pipe, signal is passed from the sensor to the system for processing. Basic techniques like filtering, amplification, and Fourier transform are used to process the signal. The obtained transform is trained using neural network algorithm to estimate the fluid flow rate. Artificial neural network is designed using back propagation with artificial bee colony algorithm. Designed estimator after being incorporated in practical setup is subjected to test and the result obtained shows successful estimation of flow rate with the root mean square percentage error of 0.667.

Download Full-text

Emotional face recognition and EEG measures

Neuropsychological Trends ◽

10.7358/neur-2007-001-mahl ◽

2007 ◽

Author(s):

Walter Mahler ◽

Sandra Reder

Keyword(s):

Face Recognition ◽

Selective Attention ◽

Frequency Band ◽

Cognitive Processes ◽

Stimulus Type ◽

Face Stimulus ◽

Emotional Stimuli ◽

Relevant Stimulus ◽

Band Power ◽

Emotional Face

Twenty one adults looked at emotional (sad, happy, fearful) or neutral faces. EEG measures showed that emotional significance of face (stimulus type) modulated the amplitude of EEG, especially for theta and delta frequency band power. Also, emotional discrimination by theta was more distributed on the posterior sites of the scalp for the emotional stimuli. Thus, this frequency band variation could represent a complex set of cognitive processes whereby selective attention becomes focused on an emotional-relevant stimulus.

Download Full-text

Research on Control Technologies for a High-Precision Multi-Source Vibration Simulation System

Energies ◽

10.3390/en11112956 ◽

2018 ◽

Vol 11 (11) ◽

pp. 2956

Author(s):

Xibin Ma ◽

Zhangwei Chen ◽

Huinong He ◽

Yugang Zhao

Keyword(s):

Reference Point ◽

Narrow Band ◽

Vibration Signal ◽

Frequency Component ◽

Filter Method ◽

Control Technology ◽

Vibration Signals ◽

Simulation Control ◽

Vibration Simulation ◽

Tracking Filter

Vehicles commonly suffer from the narrow-band noises and vibrations, usually a superposition of multiple sinusoidal signals, due to the excitations of engines, electrical motors, gear boxes, and other rotating mechanical parts. These excitations are transmitted to a reference point of some structure with certain transmission paths. The vibration signal measured at the reference point can be used for power system monitoring, fault diagnosis, modal analysis, noise analysis, etc. For convenience, researchers in a laboratory usually use shakers to generate expected narrow-band vibration signals acting on the vehicle structure reference points to simulate the vibration signals. However, there is a prominent difficulty in ensuring the amplitude and phase accuracy of each sub-frequency component simultaneously. In order to improve the accuracy of generating the expected vibration signal, this paper presents a multi-source vibration simulation control technology based on the tracking filter method. The main idea is to use the tracking filter to estimate the amplitude and phase of the target sub-frequency component accurately. Further, on the target sub-frequency, the drive signal of shakers is then corrected based on the amplitude and phase errors to achieve a more accurate target vibration signal. The amplitude and phase of each sub-frequency component in the excitation signal can be controlled independently. Compared with other Fast Fourier Transform (FFT)-based frequency domain analysis algorithms and numerical methods by solving the equations, the tracking filter method has a higher frequency resolution and higher accuracy. It can be easily realized in real time applications due to its simplicity. Finally, verification experiments are completed. The experimental results show that the multi-source vibration simulation control technology presented in this paper can achieve high-precision amplitude and phase on each sub-frequency component of the target vibration signals, which contain up to eight sub-frequency components.

Download Full-text

Narrow Band Power Line Communication (NB-PLC) frequency band review under residential load noise

Journal of Physics Conference Series ◽

10.1088/1742-6596/1402/3/033102 ◽

2019 ◽

Vol 1402 ◽

pp. 033102

Author(s):

K G H Mangunkusumo ◽

N W Priambodo ◽

K M Tofani ◽

G Supriyadi

Keyword(s):

Frequency Band ◽

Narrow Band ◽

Power Line ◽

Power Line Communication ◽

Band Power ◽

Load Noise

Download Full-text

Reduced Variability of Auditory Alpha Activity in Chronic Tinnitus

Neural Plasticity ◽

10.1155/2014/436146 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 29

Author(s):

Winfried Schlee ◽

Martin Schecklmann ◽

Astrid Lehner ◽

Peter M. Kreuzer ◽

Veronika Vielsmeier ◽

...

Keyword(s):

Auditory Cortex ◽

Frequency Band ◽

Resting State ◽

Spectral Power ◽

Alpha Activity ◽

Chronic Tinnitus ◽

Alpha Frequency ◽

Alpha Frequency Band ◽

History Of ◽

The Moment

Subjective tinnitus is characterized by the conscious perception of a phantom sound which is usually more prominent under silence. Resting state recordings without any auditory stimulation demonstrated a decrease of cortical alpha activity in temporal areas of subjects with an ongoing tinnitus perception. This is often interpreted as an indicator for enhanced excitability of the auditory cortex in tinnitus. In this study we want to further investigate this effect by analysing the moment-to-moment variability of the alpha activity in temporal areas. Magnetoencephalographic resting state recordings of 21 tinnitus subjects and 21 healthy controls were analysed with respect to the mean and the variability of spectral power in the alpha frequency band over temporal areas. A significant decrease of auditory alpha activity was detected for the low alpha frequency band (8–10 Hz) but not for the upper alpha band (10–12 Hz). Furthermore, we found a significant decrease of alpha variability for the tinnitus group. This result was significant for the lower alpha frequency range and not significant for the upper alpha frequencies. Tinnitus subjects with a longer history of tinnitus showed less variability of their auditory alpha activity which might be an indicator for reduced adaptability of the auditory cortex in chronic tinnitus.

Download Full-text

Feature extraction of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy

Journal of Vibration and Control ◽

10.1177/1077546320925665 ◽

2020 ◽

pp. 107754632092566 ◽

Cited By ~ 1

Author(s):

HongChao Wang ◽

WenLiao Du

Keyword(s):

Feature Extraction ◽

Frequency Band ◽

Vibration Signal ◽

Rolling Bearing ◽

Band Pass Filter ◽

Pass Filter ◽

Sparse Decomposition ◽

Vibration Data ◽

Fault Features ◽

Band Pass

As the key rotating parts in machinery, it is crucial to extract the latent fault features of rolling bearing in machinery condition monitoring to avoid the occurrence of sudden accidents. Unfortunately, the latent fault features are hard to extract by using the traditional signal processing method such as envelope demodulation because the effect of envelope demodulation is influenced strongly by the degree of background noise. Sparse decomposition, as a new promising method being able of capturing the latent fault feature components buried in the vibration signal, has attracted a lot of attentions, especially the predefined dictionary-based sparse decomposition methods. However, the feature extraction effect of the predefined dictionary-based sparse decomposition depends on whether the prior knowledge of the analyzed signal is sufficient or not. To overcome the above problems, a feature extraction method of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy is proposed in the article. First, a self-learned sparse atomics method is applied on the early weak vibration signal of rolling bearing and several self-learned atomics are obtained. Then, the self-learned atomics owing bigger kurtosis values are selected and used to reconstruct the vibration signal to remove the other interference signals. Subsequently, the frequency band entropy method is used to analyze the reconstructed vibration signal, and the optimal parameter of band-pass filter could be calculated. At last, the reconstructed vibration signal is filtered using the optimal band-pass filter, envelope demodulation on the filtered signal is applied, and better fault feature is extracted. The feasibility and effectiveness of the proposed method are verified through the vibration data of the accelerated fatigue life test of rolling bearing. Besides, the analysis results of the same vibration data using Autogram and spectral kurtosis methods are also presented to highlight the superiority of the proposed method.

Download Full-text

0789 Decreased Sigma Band Power During NREM Sleep in REM Sleep Behavior Disorder

SLEEP ◽

10.1093/sleep/zsaa056.785 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A300-A300

Author(s):

Y Lee ◽

B Lee

Keyword(s):

Rem Sleep ◽

Spectral Power ◽

Rem Sleep Behavior Disorder ◽

Behavior Disorder ◽

Nrem Sleep ◽

Control Group ◽

Sleep Behavior ◽

Band Power ◽

Potential Biomarker ◽

Obstructive Sleep

Abstract Introduction REM sleep Behavior Disorder (RBD) is characterized by dream enacting behaviors and a loss of atonia during REM sleep. Early detection of RBD is important because it is considered premonitory symptoms neurodegenerative disorders. In this study, we investigated the slow and fast sigma band power of patients with RBD using frequency analysis. Methods Twenty patients who were diagnosed as RBD according to the ICSD-3 criteria and 20 age-matched controls who underwent polysomnography (PSG) for other sleep disorders (insomnia, snoring) and showed normal to mild obstructive sleep apnea (OSA). NREM sleep EEG data was extracted and N1 sleep data was excluded to minimize arousal artifact. Fast Fourier transform-based spectral power analysis was used to compute the power spectral densities of the EEG in the MATLAB environment. The sigma bands were divided into 2 discrete bands: slow sigma (11 to 13 Hz) and- fast sigma (13 to 15 Hz). Mann-Whitney U test by SPSS was used. Results RBD patients (61.9 ± 7.1 years old; 12 men) had a significantly lower sigma band power than the control group (61.5 ± 1.1 years old; 11 men) in central region (p = 0.028). Particularly, the slow sigma band power showed a bigger difference in all regions except O1 (F3 = 0.017, F4 = 0.027, C3 = 0.004, C4 = 0.009, O2 = 0.017). Conclusion Sigma power was lower in the RBD patients than in the control. It suggests that RBD has impaired cortical activity. Thus, decreased spindle activity during NREM sleep may be a potential biomarker of RBD. Support

Download Full-text

Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

Shock and Vibration ◽

10.1155/2016/3952325 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Weigang Wen ◽

Robert X. Gao ◽

Weidong Cheng

Keyword(s):

Fault Diagnosis ◽

Frequency Band ◽

Manifold Learning ◽

Planetary Gear ◽

Vibration Signal ◽

Planetary Gearbox ◽

Characteristic Extraction ◽

Gear Fault ◽

Planet Gear ◽

Fault Characteristic

The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.

Download Full-text