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The ade4 Package - NexTag Supports Open Source Initiatives

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ecg 95<br />

ecg<br />

Electrocardiogram data<br />

Description<br />

Usage<br />

Format<br />

<strong>Source</strong><br />

<strong>The</strong>se data were measured during the normal sinus rhythm of a patient who occasionally experiences<br />

arrhythmia. <strong>The</strong>re are 2048 observations measured in units of millivolts and collected at a rate of<br />

180 samples per second. This time series is a good candidate for a multiresolution analysis because<br />

its components are on different scales. For example, the large scale (low frequency) fluctuations,<br />

known as baseline drift, are due to the patient respiration, while the prominent short scale (high<br />

frequency) intermittent fluctuations between 3 and 4 seconds are evidently due to patient movement.<br />

Heart rhythm determines most of the remaining features in the series. <strong>The</strong> large spikes occurring<br />

about 0.7 seconds apart the R waves of normal heart rhythm; the smaller, but sharp peak coming<br />

just prior to an R wave is known as a P wave; and the broader peak that comes after a R wave is a T<br />

wave.<br />

data(ecg)<br />

A vector of class ts containing 2048 observations.<br />

Gust Bardy and Per Reinhall, University of Washington<br />

References<br />

Percival, D. B., and Walden, A.T. (2000) Wavelet Methods for Time Series Analysis, Cambridge<br />

University Press.<br />

Examples<br />

## Not run:<br />

# figure 130 in Percival and Walden (2000)<br />

if (require(waveslim) == TRUE) {<br />

data(ecg)<br />

ecg.level

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