Conversely, when monitoring for infrequent events e. Volume 41 , Issue 2. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Wayne O. Robin K. Funding: Dr.
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Learn more Check out. Abstract Introduction Diagnostic ambulatory electrocardiogram AECG monitoring is widely used for evaluating syncope and collapse, and practice guidelines provide recommendations regarding optimal AECG device selection. Citing Literature. Volume 41 , Issue 2 February Pages Related Information.
Close Figure Viewer. Browse All Figures Return to Figure. Previous Figure Next Figure. Employing a simulation model in which the slopes of a template QRS are altered by different techniques, it is found that the slope changes observed during PTCA are mostly due to a widening of the QRS complex or a decrease of its amplitudes, but not a reduction of its high-frequency content or a combination of this and the previous effects.
It is concluded that QRS slope information can be used as an adjunct to the conventional ST segment analysis in the monitoring of myocardial ischemia. Three mechanisms underlie the initiation and maintenance of ventricular tachycardia : automaticity, triggered activity, and reentry. As straightforward as these mechanisms are, assessing which mechanism is operative in a particular patient's ventricular tachycardia can be difficult.
The optimal treatment strategy for ventricular tachycardia in a given patient can be influenced by the mechanism underlying the ventricular tachycardia.
Appropriately counseling patients, choosing the optimal pharmacologic agent that maximizes efficacy while minimizing undesirable side effects, risks, and toxicities, as well as recommending and timing ablative therapy all hinge on identifying the probable mechanism of ventricular tachycardia. Much has been published regarding invasive electrophysiologic maneuvers that allow for correct diagnosis of ventricular tachycardia mechanism.
The aim of this clinical review is to provide insight into VT mechanisms based on ECG clues of spontaneous arrhythmia events and the response to pharmacologic manipulation prior to invasive electrophysiologic evaluation. Continuous ECG monitoring is an important device for nursing surveillance and is useful in decreasing adverse events.
Therefore, the EASI system might be advantageous for long-term patient monitoring. Furthermore, at least one study has reported that Holter monitoring could not always corroborate initial electrocardiographic ECG detection of AF suggesting underestimation of AF by Holter. The number of ECGs conducted within the first 3 days up to the detection of AF as well as the time interval for Holter "hookup" and subsequent reporting of AF was documented.
Holter monitoring was performed in cases and in this subgroup, there was no statistically significant difference in the rate of AF detection with ECG or Holter. The discordance regarding the corroboration of AF by Holter in ECG-positive patients with AF supports previous observations and suggests a high incidence of paroxysmal AF as a cause of ischemic stroke. The performance of the classifier is normally presented in terms of sensitivity, specificity or other metrics describing the proportion of correct versus incorrect beat classifications.
From the clinician's point of view, such metrics are however insufficient to rate the performance of a classifier. Our proposition lets the investigators report their findings in terms of beat-by-beat comparisons, and defers the role of assessing the utility of the classifier to the statistician. Evaluation of the classifier's utility must be undertaken in conjunction with the set of relative costs applicable to the clinicians' application. Such evaluation produces a metric more tuned to the specific application, whilst preserving the information in the results.
RESULTS: By way of demonstration, we propose a set of costs, based on clinical data from the literature, and examine the results of two published classifiers using our method. We make recommendations for reporting classifier performance, such that this method can be used for subsequent evaluation. Performance reports should include a table of beat-by-beat comparisons, showing not-only the number of misclassifications, but also the identity of the classes involved in each inaccurate classification.
However, atrial enlargement may not correlate with clinical measures such as electrocardiographic ECG criteria. Past studies correlating ECG criteria with anatomic measures mainly used inferior M-mode or two-dimensional echocardiographic data. We sought to determine the accuracy of the ECG to predict anatomic atrial enlargement as determined by volumetric cardiovascular magnetic resonance CMR. Atrial volume index was computed using the biplane area-length method.
Individual ECG P wave changes do not reliably both detect and predict anatomic atrial enlargement. Interference of the monitored or recorded electrocardiogram is common within operating room and intensive care unit environments. Artifactual signals, which corrupt the normal cardiac signal, may arise from internal or external sources.
Electrical devices used in the clinical setting can induce artifacts by various different mechanisms. Newer diagnostic and therapeutic modalities may generate artifactual changes. These artifacts may be nonspecific or may resemble serious arrhythmia. Clinical signs, along with monitored waveforms from other simultaneously monitored parameters, may provide the clues to differentiate artifacts from true changes on the electrocardiogram.
Simple measures, such as proper attention to basic principles of electrocardiographic measurement, can eliminate some artifacts. However, in persistent cases, expert help may be required to identify the precise source and minimize interference on the electrocardiogram. Technological advancements in processing the electrocardiographic signal may be useful to detect and eliminate artifacts. Ultimately, an improved understanding of the artifacts generated by equipment, and their identifying characteristics, is important to avoid misinterpretation, misdiagnosis, and iatrogenic complication.
The electrocardiogram ECG is widely used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena.
However, in real situations, ECG recordings are often corrupted by artifacts. Two dominant artifacts present in ECG recordings are: 1 high-frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes ; 2 baseline wander BW that may be due to respiration or the motion of the patients or the instruments.
These artifacts severely limit the utility of recorded ECGs and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG enhancement.
In this paper, we propose a new ECG enhancement method based on the recently developed empirical mode decomposition EMD. The proposed EMD-based method is able to remove both high-frequency noise and BW with minimum signal distortion. Both quantitative and qualitative results are given. The simulations show that the proposed EMD-based method provides very good results for denoising and BW removal. Patients older than 21 years and those with previously documented arrhythmia were excluded.
Five patients met exclusion criteria leaving 54 subjects mean age Monitors continuously store, analyze, and transmit the electrocardiogram through cellular and land telephone networks to a central station. MCOT was performed for consecutive days mean The diagnostic yield of MCOT was superior to that expected from traditional event and Holter monitors in this pediatric population. The details of digital recording and computer processing of a lead electrocardiogram ECG remain a source of confusion for many health care professionals.
A better understanding of the design and performance tradeoffs inherent in the electrocardiograph design might lead to better quality in ECG recording and better interpretation in ECG reading. This paper serves as a tutorial from an engineering point of view to those who are new to the field of ECG and to those clinicians who want to gain a better understanding of the engineering tradeoffs involved.
The problem arises when the benefit of various electrocardiograph features is widely understood while the cost or the tradeoffs are not equally well understood. An electrocardiograph is divided into 2 main components, the patient module for ECG signal acquisition and the remainder for ECG processing which holds the main processor, fast printer, and display. The low-level ECG signal from the body is amplified and converted to a digital signal for further computer processing.
The Electrocardiogram is processed for display by user selectable filters to reduce various artifacts. A high-pass filter is used to attenuate the very low frequency baseline sway or wander. A low-pass filter attenuates the high-frequency muscle artifact and a notch filter attenuates interference from alternating current power. Although the target artifact is reduced in each case, the ECG signal is also distorted slightly by the applied filter. The low-pass filter attenuates high-frequency components of the ECG such as sharp R waves and a high-pass filter can cause ST segment distortion for instance.
Good skin preparation and electrode placement reduce artifacts to eliminate the need for common usage of these filters. We analyzed the lead electrocardiogram ECG in SVT of pediatric patients with different mechanisms of SVT to determine if there is a consistent optimal lead for rhythm identification. The tachycardia mechanism was determined either by intracardiac or transesophageal recording, or after cardioversion analysis of atrial flutter or fibrillation.
Blinded analysis of each separate lead of the lead ECG was done to determine the best lead to diagnose the mechanism of tachycardia. For statistical analysis, chi 2 or Fisher exact test was used. Lead V1 was the most useful lead to determine the tachycardia mechanism. Furthermore, a combination of V1 and lead III increases the number of patients in whom the mechanism could be identified. Therefore, we recommend that V1 should be combined with an inferior limb lead during cardiac monitoring for optimal identification of the mechanism of SVT in children.
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