Abstract-The goals of electrocardiographic (ECG) monitoring in hospital settings have expanded from simple heart rate and basic rhythm determination to the diagnosis of complex arrhythmias, myocardial ischemia, and prolonged QT interval. Whereas computerized arrhythmia analysis is automatic in cardiac monitoring systems, computerized ST-segment ischemia analysis is available only in newer-generation monitors, and computerized QT-interval monitoring is currently unavailable. Even in hospitals with ST-monitoring capability, ischemia monitoring is vastly underutilized by healthcare professionals. Moreover, because no computerized analysis is available for QT monitoring, healthcare professionals must determine when it is appropriate to manually measure QT intervals (eg, when a patient is started on a potentially proarrhythmic drug). The purpose of the present review is to provide 'best practices' for hospital ECG monitoring. Randomized clinical trials in this area are almost nonexistent; therefore, expert opinions are based upon clinical experience and related research in the field of electrocardiography. This consensus document encompasses all areas of hospital cardiac monitoring in both children and adults. The emphasis is on information clinicians need to know to monitor patients safely and effectively. Recommendations are made with regard to indications, timeframes, and strategies to improve the diagnostic accuracy of cardiac arrhythmia, ischemia, and QT-interval monitoring. Currently available ECG lead systems are described, and recommendations related to staffing, training, and methods to improve quality are provided. Key Words: AHA Scientific Statements Ⅲ pediatrics Ⅲ electrocardiography Ⅲ torsade de pointes Ⅲ myocardial infarction Ⅲ tachyarrhythmias Ⅲ ischemia Ⅲ antiarrhythmic agents Ⅲ long-QT syndrome S ince the introduction of electrocardiographic (ECG) monitoring in hospital units Ͼ40 years ago, 1 the goals of monitoring have expanded from simple tracking of heart rate and basic rhythm to the diagnosis of complex arrhythmias, the detection of myocardial ischemia, and the identification of a prolonged QT interval. During the same 4 decades, major improvements have occurred in cardiac monitoring systems, including computerized arrhythmia detection algorithms, STsegment/ischemia monitoring software, improved noisereduction strategies, multilead monitoring, and reduced lead sets for monitoring-derived 12-lead ECGs with a minimal number of electrodes. 2,3 Despite these advances in technology, the need for human oversight in the interpretation of ECG monitoring data is as important today as it was 40 years ago for the following reasons. First, cardiac monitor algorithms are intentionally set for high sensitivity at the expense of specificity. As a result, numerous false alarms occur that must be evaluated by healthcare professionals so that overtreatment of patients will not occur. Examples of overtreatment are reported in the The American Heart Association makes every effort to avoid any actual or potential ...

BACKGROUND Atrial fibrillation is usually thought of as a "random" pattern of circulating wavelets. However, local atrial activation should be influenced by the constant anatomy and receding tail of refractoriness from the previous activation. The general tendency for wave fronts to follow paths of previous excitation has been termed "linking." We examined intra-atrial electrograms recorded during atrial fibrillation for evidence of linking. METHODS AND RESULTS Two minutes of atrial fibrillation were recorded in 15 patients with an orthogonal catheter. We have previously demonstrated that this catheter can be used to detect changes in the direction of local atrial activation. A mean vector was calculated for each electrogram. The similarity of the direction of the vectors from two consecutive electrograms can be quantified on a scale of 1 to -1 by calculating the cosine (cos) of the smallest angle (theta) between them. Two vectors pointing in the same or opposite directions then have cos(theta) = 1 or -1, respectively. For the entire group of patients, mean cos(theta) was significantly greater than 0 (mean, 0.36; p less than 0.001). In nine of 15 patients, there were groups of six or more consecutive beats (total, 44 groups; range, six to 14 beats per group) in which the direction of activation of each beat was within 30 degrees of the previous beat. The likelihood of one group of six or 14 consecutive similar beats occurring by chance in any one patient in 1 minute is less than 0.05 and less than 0.0000001, respectively. There was a significant correlation (r = 0.90) between the amount of linking during the first and second minutes of atrial fibrillation in each patient. CONCLUSIONS Transient similarities in the direction of wavelet propagation in the majority of patients with atrial fibrillation is consistent with the presence of transient linking. To our knowledge, this is the first direct evidence that atrial activation during atrial fibrillation in humans is not entirely random.

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Low frequency fibrillation was found to be much more likely to terminate. Frequency changes preceding spontaneous termination were abrupt, in contrast to the gradual frequency drop reported with drug-induced termination. The analysis of fibrillatory wave characteristics and their change over time might be used to target specific moments for pacing therapy in patients with AF.

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00837798.

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Previous work has suggested that a comparison of electrograms from two or more sites may best differentiate fibrillatory from nonfibrillatory rhythms. The coherence spectrum is a measure by which two signals may be compared quantitatively in the frequency domain. In the present study, the coherence spectrum was used to quantify the relation between spectral components of electrograms from two sites in either the atrium or ventricle during both fibrillatory and nonfibrillatory rhythms. Bipolar recordings of 35 rhythms from 20 patients were analyzed for coherence in the 1-59 Hz band. The 17 nonfibrillatory rhythms were sinus rhythm (six), paroxysmal supraventricular tachycardia (two), atrial flutter (four), and monomorphic ventricular tachycardia (five). The 18 fibrillatory rhythms were atrial fibrillation (12) and ventricular fibrillation (six). Nonfibrillatory rhythms exhibited moderate-to-high levels of coherence throughout the 1-59 Hz band, with peaks concentrated at the rhythm's fundamental frequency and its harmonics. Fibrillatory rhythms exhibited little coherence throughout the 1-59 Hz band, and harmonics were not evident. The mean magnitude-squared coherence (scale of 0 to 1) for the 1-59 Hz band ranged from 0.22 to 0.86 (mean± SD, 0.52 ±0.19) for nonfibrillatory rhythms and from 0.042 to 0.12 (0.067±0.021) for fibrillatory rhythms. Separation of fibrillatory and nonfibrillatory rhythms was possible whether signals were recorded by floating or fixed-electrode configurations. These findings indicate that comparison of two electrograms with magnitude-squared coherence measurements differentiates fibrillatory from nonfibrillatory rhythms. A recognition algorithm based on coherence spectra may be robust in the face of major variations in lead configuration. Furthermore, the coherence spectra may provide a means to quantify the "organization" or "disorganization" of a cardiac rhythm. (Circulation 1989;80:112-119) F ibrillatory rhythms are typically described as "chaotic" and "disorganized." More specifically, the activity from multiple sites during fibrillation has been described as asynchronous and incoordinate.1-6 Allessie et a16 have suggested that the comparison of activity from two or more sites may best differentiate fibrillatory from nonfibrillatory rhythms. While this "altered spatial arrangement of conduction"7 is quintessential to fibrillation, this qualitative characteristic remains to be quantified. Previous studies have used frequencyFrom the

The magnetohydrodynamic effect generates voltages related to blood flow, which are superimposed on the electrocardiogram (ECG) used for gating during cardiac magnetic resonance imaging (MRI). A method is presented for extracting the magnetohydrodynamic signal from the ECG. The extracted magnetohydrodynamic blood flow potential may be physiologically meaningful due to its relationship to blood flow. Removal of the magnetohydrodynamic voltages from the ECG can potentially lead to improved gating and diagnostically useful ECGs.