scholarly journals Transformada wavelet na análise do efeito da terapia LED sobre a atividade do músculo masseter em mulheres com disfunção temporomandibular

2018 ◽  
Vol 28 (2) ◽  
pp. 29045 ◽  
Author(s):  
Davidson Ribeiro Costa ◽  
David Ribeiro Costa ◽  
Giovanni Arnaldo Pacetti ◽  
Renata Amadei Nicolau
Keyword(s):  
Wavelet Transform ◽  
Masseter Muscle ◽  
Light Emitting Diode ◽  
Temporomandibular Disorder ◽  
Maximum Energy ◽  
Physiological Factors ◽  
Time Frequency ◽  
Before And After ◽  
Led Therapy ◽  
Frequency Relationship

AIMS: To present the wavelet transform as an alternative tool in the evaluation of the masseter muscle electrical activity in women with temporomandibular disorder after therapy with Light Emitting Diode (LED).METHODS: Five volunteers with temporomandibular disorder underwent four sessions of LED therapy. Electromyography of the masseter muscle was performed bilaterally before and after treatment. For analysing the electromyographic signals, the wavelet transform was applied in the Morlet function. RESULTS: In the scalogram, a decrease in the activation of the high-frequency fibers in the rest protocol and its increase in the isometric movement protocol were observed. In the analysis based on the RGB color system, we observed that in the right masseter muscle resting protocol, the moments of maximum energy intensities were reduced by 82% for frequencies of 256-512 Hz and by 42% for frequencies above 512 Hz. In the left masseter muscle the reduction was 42% in the frequency band of 256-512 Hz.CONCLUSIONS: Analysis by the wavelet transform allowed identification of physiological factors related not only to the activation of the masseter muscle, but also to the intensity and time / frequency relationship, as well as the main types of fibers activated during the protocols before and after LED therapy in patients with dysfunction temporomandibular.

Spectral Estimation of Noisy Seismogram using Time-Frequency Analyses

2016 ◽  
Vol 7 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Vaneeta Devi ◽  
M. L. Sharma
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Spectral Estimation ◽  
Window Size ◽  
Frequency Component ◽  
Maximum Energy ◽  
Gabor Transform ◽  
Time Frequency ◽  
Signal Features

Time–Frequency analyses have the advantage of explaining the signal features in both time domain and frequency domain. This paper explores the performance of Time–Frequency analyses on noisy seismograms acquired from seismically active region in NW Himalayan. The Short Term Fourier Transform, Gabor Transform, Wavelet Transform and Wigner-Ville Distribution have been used in the present study to carry out Time-Frequency analyses. Parametric study has been carried out by varying basic parameters viz. sampling, window size and types. Wavelet analysis (Continuous Wavelet Transform) has been studied with different type of wavelets. The seismograms have been stacked in time-frequency domain using Gabor Transform and have been converted using Discrete Gabor Expansion techniques. The Spectrograms reveals better spectral estimation in time-frequency domain than Fourier Transform and hence recommended to estimate dominate frequency components, phase marking and timings of phase. The time of occurrence of frequency component corresponding to maximum energy burst can be identified on seismograms

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency

Image encryption based on Fractional wavelet transform, Arnold transform with the double random phases in HSV color domain

2020 ◽  
Vol 13 ◽  
Author(s):  
Aarushi Shrivastava ◽  
Janki Ballabh Sharma ◽  
Sunil Dutt Purohit
Keyword(s):  
Wavelet Transform ◽  
Image Encryption ◽  
Fractional Fourier Transform ◽  
Color Image ◽  
Random Phase ◽  
Arnold Transform ◽  
Time Frequency ◽  
Key Space ◽  
Secret Keys ◽  
Fractional Wavelet Transform

Objective: In the recent multimedia technology images play an integral role in communication. Here in this paper, we propose a new color image encryption method using FWT (Fractional Wavelet transform), double random phases and Arnold transform in HSV color domain. Methods: Firstly the image is changed into the HSV domain and the encoding is done using the FWT which is the combination of the fractional Fourier transform with wavelet transform and the two random phase masks are used in the double random phase encoding. In this one inverse DWT is taken at the end in order to obtain the encrypted image. To scramble the matrices the Arnold transform is used with different iterative values. The fractional order of FRFT, the wavelet family and the iterative numbers of Arnold transform are used as various secret keys in order to enhance the level of security of the proposed method. Results: The performance of the scheme is analyzed through its PSNR and SSIM values, key space, entropy, statistical analysis which demonstrates its effectiveness and feasibility of the proposed technique. Stimulation result verifies its robustness in comparison to nearby schemes. Conclusion: This method develops the better security, enlarged and sensitive key space with improved PSNR and SSIM. FWT reflecting time frequency information adds on to its flexibility with additional variables and making it more suitable for secure transmission.

Evaluation of Changes in Depression Levels of Bruxism Patients Treated With Botulinum Toxin-A

2021 ◽  
pp. 232020682110065
Author(s):  
Deniz Erdil ◽  
Nilsun Bagis ◽  
Hakan Eren ◽  
Melike Camgoz ◽  
Kaan Orhan
Keyword(s):  
Botulinum Toxin ◽  
Botulinum Toxin A ◽  
Age Groups ◽  
Treatment Method ◽  
Definitive Treatment ◽  
Toxin A ◽  
Physiological Factors ◽  
Before And After ◽  
The Mean ◽  
Post Hoc

Aim: Bruxism is defined as the involuntary recurrent masticatory muscle activity characterized by gnashing, grinding, clenching of teeth, and/or pushing the mandible. Factors creating its etiology are peripheral (morphological) or central (physiopathological and physiological), and exogenous. Recently, among physiological factors, depression and bruxism were considered to be related. A definitive treatment method does not exist for bruxism; however, botulinum toxin-A (BT-A) application is an up-to-date and effective way of treatment. The present study is aimed to evaluate the levels of depression in bruxism patients treated with BT-A application. Materials and Methods: A total of 25 individuals (23 females and 2 males) who were diagnosed as bruxism patients were included in the study. 25 U of BT-A for each masseter muscle was injected into the patients. Patients were prospectively observed for a possible change in depression levels by using Beck’s Depression Inventory. The inventory was implemented before and six months after the BT-A application. Depression levels before and six months after the injection were compared. A paired t-test was used to compare “before” and “after” treatment values. One-way analysis of variance and post-hoc Tukey tests were used to evaluate the change in Beck’s Depression Inventory scores according to age groups. Results: The mean total score was 7.80 ± 8.10 before the treatment and 7.16 ± 6.52 six months after the treatment. The decrease in the mean score was not statistically significant ( P > .05). Conclusion: In conclusion, despite the decrease in the mean Beck’s Depression Inventory scores, a statistically significant decrease in the depression levels of patients was not observed.

scholarly journals Nerve growth factor and glutamate increase the density and expression of substance P-containing nerve fibers in healthy human masseter muscles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdelrahman M. Alhilou ◽  
Akiko Shimada ◽  
Camilla I. Svensson ◽  
Peter Svensson ◽  
Malin Ernberg ◽  
...  
Keyword(s):  
Nerve Growth Factor ◽  
Growth Factor ◽  
Nmda Receptors ◽  
Masseter Muscle ◽  
Temporomandibular Disorder ◽  
Nerve Fibers ◽  
Receptor Subtype ◽  
Nerve Growth ◽  
Afferent Fibers ◽  
Pain Characteristics

AbstractNocifensive behavior induced by injection of glutamate or nerve growth factor (NGF) into rats masseter muscle is mediated, in part, through the activation of peripheral NMDA receptors. However, information is lacking about the mechanism that contributes to pain and sensitization induced by these substances in humans. Immunohistochemical analysis of microbiopsies obtained from human masseter muscle was used to investigate if injection of glutamate into the NGF-sensitized masseter muscle alters the density or expression of the NMDA receptor subtype 2B (NR2B) or NGF by putative sensory afferent (that express SP) fibers. The relationship between expression and pain characteristics was also examined. NGF and glutamate administration increased the density and expression of NR2B and NGF by muscle putative sensory afferent fibers (P < 0.050). This increase in expression was greater in women than in men (P < 0.050). Expression of NR2B receptors by putative sensory afferent fibers was positively correlated with pain characteristics. Results suggest that increased expression of peripheral NMDA receptors partly contributes to the increased pain and sensitivity induced by intramuscular injection of NGF and glutamate in healthy humans; a model of myofascial temporomandibular disorder (TMD) pain. Whether a similar increase in peripheral NMDA expression occurs in patients with painful TMDs warrants further investigation.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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