seizure: IEEG segments from a seizure patient
In assist: A Suite of R Functions Implementing Spline Smoothing Techniques
seizure R Documentation
IEEG segments from a seizure patient
Description
The 'seizure' data frame has 60,000 rows and 3 columns of data from an IEEG time series
Usage
data(seizure)
Details
The baseline segment contains 5-minute IEEG signal
extracted at least four hours before the seizure's onset.
The preseizure segment contains 5-minute IEEG signal
right before a seizure's clinical onset. The sampling rate
of the IEEG signal is 200 observations per second.
Therefore there are 60,000 time points in each segment.
Format
The data frame contains the following columns:
t a numeric vector of the observation number
base a numeric vector of the baseline segment
preseizure a numeric vector of the segment right before a seizure
Source
D'Alessandro, M., Vachtsevanos, G., Esteller, R., Echauz, J.
and Litt, B. (2001). A Generic Approach to Selecting the
Optimal Feature for Epileptic Seizure Prediction.
IEEE International Meeting of the Engineering in Medicine and Biology Society.
references
Qin, L. and Wang, Y. (2008), Nonparametric Spectral Analysis
With Applications to Seizure Characterization Using EEG Time Series. Annals of Applied Statistics 2, 1432-1451.
assist documentation built on Aug. 22, 2023, 9:12 a.m.
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| seizure | R Documentation |
IEEG segments from a seizure patient
Description
The 'seizure' data frame has 60,000 rows and 3 columns of data from an IEEG time series
Usage
data(seizure)
Details
The baseline segment contains 5-minute IEEG signal extracted at least four hours before the seizure's onset. The preseizure segment contains 5-minute IEEG signal right before a seizure's clinical onset. The sampling rate of the IEEG signal is 200 observations per second. Therefore there are 60,000 time points in each segment.
Format
The data frame contains the following columns:
t a numeric vector of the observation number
base a numeric vector of the baseline segment
preseizure a numeric vector of the segment right before a seizure
Source
D'Alessandro, M., Vachtsevanos, G., Esteller, R., Echauz, J. and Litt, B. (2001). A Generic Approach to Selecting the Optimal Feature for Epileptic Seizure Prediction. IEEE International Meeting of the Engineering in Medicine and Biology Society.
references
Qin, L. and Wang, Y. (2008), Nonparametric Spectral Analysis With Applications to Seizure Characterization Using EEG Time Series. Annals of Applied Statistics 2, 1432-1451.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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