High-frequency atrial electrical activity

1961 ◽  
Vol 16 (2) ◽  
pp. 300-304 ◽  
Author(s):  
Cesar A. Caceres ◽  
George A. Kelser ◽  
Juan Calatayud
Keyword(s):  
High Frequency ◽  
Electrical Potential ◽  
P Wave ◽  
Right Atrial ◽  
High Frequencies ◽  
Pr Interval ◽  
Frequency Components ◽  
Left And Right ◽  
Electronic Filters ◽  
High Frequency Content

Left and right atrial intracavitary and conventional surface leads were used to study electrocardiographic activity during the PR interval. Electronic filters were employed for analysis of wave frequency and harmonic content from 1.7 to 1700 cps. Amplifiers permitting standardization sensitivity to 500 mm/mv were used to obtain oscilloscopic tracings recorded at a paper speed of 75 mm/sec. Frequency analysis of the electrical potential recorded during P wave inscription demonstrated the presence of high-frequency content that is excluded by conventional electrocardiographic amplifiers. The high-frequency components are associated with the time of inscription of the electrocardiographic intrinsic deflection and have a relationship to the characteristics of the pressure-pulse curve. These relationships suggest that intracavitary high frequencies and the electrocardiographic intrinsic deflection originate from electrical discharges associated with initiation of contractile events. Submitted on June 6, 1960

scholarly journals Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study

Heart Rhythm ◽  
2014 ◽  
Vol 11 (9) ◽  
pp. 1584-1591 ◽  
Author(s):  
Miguel Rodrigo ◽  
María S. Guillem ◽  
Andreu M. Climent ◽  
Jorge Pedrón-Torrecilla ◽  
Alejandro Liberos ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Body Surface ◽  
High Frequency ◽  
Computational Study ◽  
Right Atrial ◽  
Surface Localization ◽  
Left And Right

Cable Fault Recognition Based on the Wavelet Energy Spectrum of Mode Components

2013 ◽  
Vol 427-429 ◽  
pp. 834-837
Author(s):  
Mei Wang ◽  
Jing Wu
Keyword(s):  
Energy Spectrum ◽  
High Frequency ◽  
Transient Current ◽  
Power Cable ◽  
Two Phase ◽  
High Frequencies ◽  
Wavelet Energy ◽  
Fault Recognition ◽  
Frequency Components ◽  
Independent Mode

When a fault appeared in a power cable transmission line, the transient current with high frequencies would be produced in the system. Three independent mode components could be obtained by applying the phase mode transformation to the transient current. For different types of the faults, the three independent mode components have different features. Based on wavelet energy spectrum of mode components, a method for cable fault recognition is developed in this paper. First, the fault current is decomposed by using Karenbaue transformation matrix. Then, wavelet transformation is uses to obtain the coefficients of the high frequency components which reflect the original signal high frequency energy. Finally, based on the wavelet energy spectrum method and the detailed coefficient manipulation, the equivalent norms of the mode components are obtained. Compared with the traditional fault recognition method, the new method depends less on zero mode component in two-phase short to ground state, and it can recognize the fault class in the cases of different fault positions, different fault path resistances and different inception angles.

scholarly journals Validation of Multi-Frame PIV Image Interrogation Algorithms in the Spectral Domain

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Steven Beresh ◽  
Douglas Neal ◽  
Andrea Sciacchitano
Keyword(s):  
High Frequency ◽  
Spectral Space ◽  
Data Set ◽  
Spectral Content ◽  
Time Resolved ◽  
High Frequencies ◽  
Low Pass ◽  
Velocity Spectra ◽  
Filtering Effect ◽  
High Frequency Content

Multi-frame correlation algorithms for time-resolved PIV have been shown in previous studies to reduce noise and error levels in comparison with conventional two-frame correlations. However, none of these prior efforts tested the accuracy of the algorithms in spectral space. Even should a multi-frame algorithm reduce the error of vector computations summed over an entire data set, this does not imply that these improvements are observed at all frequencies. The present study examines the accuracy of velocity spectra in comparison with simultaneous hot-wire data. Results indicate that the high-frequency content of the spectrum is very sensitive to choice of the interrogation algorithm and may not return an accurate response. A top-hat-weighted sliding sum-of-correlation is contaminated by high-frequency ringing whereas Gaussian weighting is indistinguishable from a low-pass filtering effect. Some evidence suggests the pyramid correlation modestly increases bandwidth of the measurement at high frequencies. The apparent benefits of multi-frame interrogation algorithms may be limited in their ability to reveal additional spectral content of the flow.

scholarly journals Rethink the Connections among Generalization, Memorization, and the Spectral Bias of DNNs

2021 ◽  
Author(s):  
Xiao Zhang ◽  
Haoyi Xiong ◽  
Dongrui Wu
Keyword(s):  
High Frequency ◽  
Deep Neural Networks ◽  
Random Noise ◽  
Experimental Setup ◽  
Training Set ◽  
Trade Off ◽  
High Frequencies ◽  
Test Error ◽  
Frequency Components ◽  
Bias Variance

Over-parameterized deep neural networks (DNNs) with sufficient capacity to memorize random noise can achieve excellent generalization performance, challenging the bias-variance trade-off in classical learning theory. Recent studies claimed that DNNs first learn simple patterns and then memorize noise; some other works showed a phenomenon that DNNs have a spectral bias to learn target functions from low to high frequencies during training. However, we show that the monotonicity of the learning bias does not always hold: under the experimental setup of deep double descent, the high-frequency components of DNNs diminish in the late stage of training, leading to the second descent of the test error. Besides, we find that the spectrum of DNNs can be applied to indicating the second descent of the test error, even though it is calculated from the training set only.

High-frequency oscillations in human electromyograms during voluntary contractions

1986 ◽  
Vol 56 (2) ◽  
pp. 542-553 ◽  
Author(s):  
E. N. Bruce ◽  
L. M. Ackerson
Keyword(s):  
High Frequency ◽  
Human Subjects ◽  
Power Spectra ◽  
Respiratory Muscles ◽  
Muscular Contraction ◽  
Voluntary Activation ◽  
High Frequency Oscillations ◽  
Frequency Components ◽  
Left And Right ◽  
Voluntary Contractions

Spectral analysis was used to identify correlated sinusoidal frequency components in left and right side diaphragm electromyographic (EMG) recordings from human subjects during voluntary deep inspirations. In 31 of 33 subjects bilaterally correlated high-frequency oscillations were found in broad or narrow bands in the range of 60-84 and 16-40 Hz. To determine if such oscillations were associated also with bilaterally symmetric, phasic, voluntary activation of nonrespiratory muscles, we obtained EMG signals from left and right masseter muscles during clenching of the jaw; left and right sternomastoid muscles during lifting of the head against gravity; and left and right biceps muscles during lifting of a weighted bar. Weakly correlated frequency components, mainly at frequencies below 60 Hz, were found in the left and right masseter EMGs on at least one trial from 12 of 17 subjects. No bilaterally correlated frequency components were found during phasic contraction of biceps and sternomastoid muscles. Power spectra of biceps EMGs, however, sometimes exhibited peaks indicative of oscillations that were not bilaterally correlated. In nine subjects, correlated frequency components in the 60-84 Hz range were found in intercostal EMGs from the axillary region of the fifth interspace during voluntary deep inspirations but not during postural contractions. We conclude that high-frequency oscillations in the range of 60-84 Hz in diaphragm and intercostal EMGs are associated particularly with respiratory activation of respiratory muscles. These results support the hypothesis that high-frequency oscillations may be a manifestation of control of muscular contraction via a central pattern generator.

Left and Right Atrial Contribution to the P-wave in Realistic Computational Models

2015 ◽  
pp. 439-447 ◽  
Author(s):  
Axel Loewe ◽  
Martin W. Krueger ◽  
Pyotr G. Platonov ◽  
Fredrik Holmqvist ◽  
Olaf Dössel ◽  
...  
Keyword(s):  
Computational Models ◽  
P Wave ◽  
Right Atrial ◽  
Left And Right ◽  
Atrial Contribution

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

Prenatal ultrasound diagnosis of pulmonary atresia in combination with left atrial isomerism

2017 ◽  
Author(s):  
M Medvedev, M.V. Kubrina, O.S. Zarubina et all
Keyword(s):  
Left Atrial ◽  
Reverse Flow ◽  
Ductus Arteriosus ◽  
Pulmonary Atresia ◽  
Prenatal Ultrasound ◽  
Ultrasound Diagnosis ◽  
Right Atrial ◽  
Second Trimester ◽  
Left And Right ◽  
Atrial Isomerism

Two cases of prenatal ultrasound diagnosis of left atrial isomerism in the second trimester of gestation is presented. These two cases were in combination with pulmonary atresia and right aortic arch. Left atrial isomerism was identify by the digit-like shape of the left and right atrial appendages. The pulmonary atresia was identified on the basis of reverse flow in small pulmonary artery. A right aortic was identified by “U”-shaped confluence of aorta and ductus arteriosus in view of three vessels and trachea. The trachea was located between the vessels. The pregnancies were terminated and prenatal diagnosis was conformed at autopsy

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

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