Given that people can live without atrial depolarization, do you think people can live without ventricular depolarization? No, because ventricular depolarization triggers the ventricular myocardium to contract, and without ventricular contraction there would be no blood pumped out of the heart. Unfortunately, the accuracy of diagnosing blocked arteries further from the heart when using an ECG decrease, so your cardiologist may recommend an ultrasound, which is a non-invasive test, like a carotid ultrasound, to check for blockages in the extremities or neck.
AF may be detected first during a routine vital signs check. If the patient has a new irregular heart rate or an abnormally fast or slow heart rate, obtain a lead ECG and look for an irregularly irregular rhythm and fibrillation f waves, the two hallmarks of AF.
The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. Strawberries, blueberries, blackberries and raspberries are jam-packed with important nutrients that play a central role in heart health. Berries are also rich in antioxidants like anthocyanins, which protect against the oxidative stress and inflammation that contribute to the development of heart disease Begin typing your search term above and press enter to search.
Press ESC to cancel. Skip to content Home Sociology Where does atrial repolarization occur? Ben Davis July 13, Where does atrial repolarization occur? Where does the repolarization of the atria occur quizlet? What is atrial repolarization? What is atrial repolarization quizlet? Normal P wave is no more than 2. It is either far right or far left axis deviation if it lies between o and o. The method of determining QRS axis will be explained in a later section.
ST segment reflects the current flow associated with phase 2 of ventricular repolarization. Since there is no current flow during this plateau phase of repolarization, the ST segment is normally isoelectric with the baseline. The T wave represents the current of rapid phase 3 ventricular repolarization see diagram above. The polarity of this wave normally follows that of the main QRS deflection in any lead. The ventricles are electrically unstable during that period of repolarization extending from the peak of the T wave to its initial downslope.
Revised 03 Dec Accepted 12 Dec Published 18 Jan Abstract We present a case of atrial repolarization waves from an ectopic atrial rhythm mimicking inferior ST segment elevation myocardial infarction in a year-old male patient who presented with left sided chest wall and shoulder pain. Introduction Clinical conditions other than myocardial ischemia can affect the ST segment resulting in either ST segment elevation or depression. Case Presentation A man in his late 70s presented with a two-week history of constant nonexertional left sided chest pain and neck pain.
Figure 1. Figure 2. References G. Slavich, D. Tuniz, R. Fregolent, and M. View at: Google Scholar F. Holmqvist, J. Carlson, and P. Puletti, M. Curione, F. Pozzar, G. The algorithm only requires long observation periods to perform self-adaptive and reliable statistical inferences for unsure parameters.
The morphologies of P waves and hidden Ta waves are typically considered important among healthcare professionals. In [ 1 ] and [ 11 ], Ta waves were suggested to be an early sign of inferior injury, such as acute atrial infarction or arrhythmia.
One example is atrial fibrillation if the waves can be observed during conduction system malfunction, such as long QT interval or atrioventricular block. P waves alone are important in justifying heart diseases in both sinus rhythm and fibrillation [ 12 ].
However, P waves are relatively weak, and thus, current understanding on atria activities are generally insufficient. Few studies have investigated activities from the atria part of myocytes. In [ 13 ], the problem was initially addressed via computer simulation. Without involving inverse problems, [ 13 ] facilitated a forward model, which mapped current dipoles onto atrial mid-myocardium to surface ECGs under a set of predetermined parameters to understand the contributions of right and left atria activities to the observed P waves during sinus rhythm and atrial fibrillation.
In clinical situations, Ta waves are not only hidden, but P waves are also tangled with the adjacent QRS complex. In [ 7 ] and [ 8 ], statistical and signal processing methods were, respectively used to single out P waves from the rest of the QRS part.
Surface signals are complex combinations of current stimuli from millions of cardiomyocytes; thus, the signal separation task must be performed at the level of myocardium cells, and solving an ill-posed inverse problem is inevitable.
Pioneer source models, such as those in [ 14 , 15 , 16 ], have been integrated into and advanced for contemporary computational electrocardiology models that establish models for various processes ranging from cellular bioelectrical activities to body surface potential distribution [ 17 , 18 , 19 ].
The forward problems involve mapping from inter- to intra-cellular currents onto body surface potential distributions [ 20 , 21 ]. Given the nature of complexity in the biological field, the large degree of freedom poses a challenge in evaluating inverse problems. When dealing with the inverse problem, most regulation methods can only condition numerical difficulty from the mathematical point of view; however, the problem of multiple solutions must be addressed by restoring possible missing constraints.
In [ 22 ], this difficulty was solved by reducing the inverse problem in limited mapping from epicardial to body surfaces. In [ 23 ], the inverse problem was addressed by proposing a special equipment that could collect thousands of potentials on the body surface instead of the standard lead ECG.
Activation time sequencing or imaging can be evaluated for primitive diagnosis without retrieving detailed cellular activities [ 24 , 25 , 26 , 27 , 28 ]. However, electric current constraints from the ionic behavior of individual cells, such as in [ 17 ], impose additional computational challenge given the millions of myocytes. Moreover, additional constraints introduce other unknown parameters, and the degree of freedom remains high.
Therefore, advanced statistical methods should be applied to obtain reliable solutions. Existing studies continuously contribute to addressing the problem of physiological models, and specific body surface electrical data from patients are always being corrupted by noise and the incorrect construction of organ geometries. In [ 29 ], spatial covariance in a volume conductor was facilitated for maximum a posteriori MAP equation.
In [ 30 ], temporal and spatial covariances were estimated under certain mathematical assumptions based on structures that were inherent in the space—time correlation matrix. In [ 31 ], the facilitation of multiple information sources to improve the efficiency of Bayesian MAP formulation was suggested. In [ 32 ], TMPs were constrained using a diffusion—reaction model from cellular activation dynamics, which limited the inverse problem in both spatial and temporal dimensions.
This work suggested relying on a statistical method to address both model and data errors in terms of prior knowledge on cell current dynamics and evidence for surface potential data. In [ 33 ], the progress of statistical identification from the perspective of systems biology was reviewed.
As mentioned earlier, the extraction of P waves should be conducted at the electric current level in myocardial sources. The model for the cardiac computational system comprises two parts according to the component guideline in [ 34 ]. The first part involves mapping between body surface potentials and intra-cellular TMPs. Evaluating TMPs is considered a difficult inverse problem given a potential map of a body surface [ 35 , 36 ].
The second part aims to constrain the inverse problem, in which the constraint describes changes in TMPs in terms of electrical propagation between myocardia. Most electrophysiological models are diffusion—reaction systems [ 17 , 36 , 37 , 38 ]. We first consider the forward problem from equivalent current—dipole sources to body surface potentials. The sources of bioelectric currents across cell membranes excite the movement of cardiomyocytes and induce potential fields, which can be detected via surface electrodes.
To model equivalent current density, the entire myocardium is divided into grid meshes. Following the suggestion in [ 39 ], boundary element methods are applied. By tessellating and vectorizing all cardiac and thorax surfaces, a discrete matrix Eq. The geometric coordinates are segmented and discretized via magnetic resonance imaging MRI or computed tomography for a specific patient. Given numerical sensitivity and unavoidable movement, the forward model may suffer from geometric errors and should be incorporated as a part of modeling [ 9 , 41 ].
In [ 42 ], geometric errors were suggested to be overcome by using Bayesian MAP estimation or Kalman filtering with Gaussian geometric errors. In the present study, we do not rely on the accuracy of geometry and conductivity.
We estimate the parameters along with the process of estimating TMPs [ 43 , 44 ]. Bayesian estimation in error covariance enables performance analysis to statistically characterize solutions. Phenomenological models focus at the macroscopic level and ranges from 2-variable equations [ 14 , 37 ] to the complicated variable Luo—Rudy model [ 45 ]. Resolution is not a concern in extracting P waves. Electrical propagation is captured using the reaction—diffusion system [ 37 ] with the same setting as that in [ 46 , 47 ].
Considering the balance between precision and computation, a simple system is sufficient to constrain the ill-posed inverse problem. Therefore, we adopt the system from [ 37 ] as follows:. By converting the equation into finite element meshes [ 47 ], the reaction—diffusion system can then be used as an effective constraint in solving the inverse problem.
Our problem contains a large number of uncertainties, and thus, advanced Bayesian statistics can be a viable approach [ 44 ]. When 1 and 2 are combined, we obtain the data model as follows 3 :. To deal with a large number of parameters, the guideline in [ 46 ] and [ 47 ] indicates that the complicated joint distribution in data model 3 can be formulated as a hierarchical model and factorized into a series of conditional distributions.
Therefore, the joint posterior distribution can be written in a hierarchical form as follows:. Following the suggestion in [ 47 ], a Monte Carlo Markov chain MCMC slice sampler [ 48 ] is applied in the Bayesian computation model because of the high dimension in our complex problem. A full Bayesian analysis of this problem is achieved by sampling the joint posterior distribution 13 using an MCMC technique called slice sampling [ 49 ]. Another potential solution for reducing the constraining effects of prior knowledge is the simultaneous estimation of the TMP dynamics and electrophysiological properties of the myocardium.
This method has the advantage that the constraining models can be modified according to the collected data of patients with filtering of unknown parameters. To conduct the following experiments, 3D geometric models of a complete heart and torso are necessary. Cardiac geometric data were adopted from the ECGSim data set, which described a healthy normal young male using complete atria and ventricles Fig. Given that a 3D imaging will not be constructed on the epicardial surface, the requirement for grid size is low.
Resolution is further reduced to prevent the introduction of excessive numerical difficulties from the source of the standard lead ECG. The geometry of a torso was adopted from the PhysioNet data archive, which also originated from the body surface mapping data of Dalhousie University [ 51 , 52 , 53 ].
Although accuracy is not a concern, mapping between surface nodes to the electrode positions of standard leads should be specified. Given the well-prepared recording and documentation in the data set, the detailed mapping from the surface nodes to the 15 standard leads was elaborated. The signals were preprocessed to eliminate electromagnetic interference, baseline wandering e. This study develops a model that retrieves hidden atrial repolarization waves by solving an inverse problem from surface ECG to cardiac TMPs Fig.
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