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Epileptic Seizure Recognition Data Set
Download: Data Folder, Data Set Description

Abstract: This dataset is a pre-processed and re-structured/reshaped version of a very commonly used dataset featuring epileptic seizure detection.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:

11500

Area:

Life

Attribute Characteristics:

Integer, Real

Number of Attributes:

179

Date Donated

2017-05-24

Associated Tasks:

Classification, Clustering

Missing Values?

N/A

Number of Web Hits:

8334


Source:

Qiuyi Wu
School of Mathematical Sciences
Rochester Institute of Technology
email: qw9477'@'rit.edu

Ernest Fokoue
School of Mathematical Sciences
Rochester Institute of Technology
email: epfeqa'@'rit.edu
Phone: 585 739 6893


Data Set Information:

Please find the original data at '[Web Link]'


Attribute Information:

The original dataset consists of 5 different folders, each with 100 files, with each file representing a single subject/person. Each file is a recording of brain activity for 23.6 seconds. The corresponding time-series is sampled into 4097 data points. Each data point is the value of the EEG recording at a different point in time.

The response variable is y in column 179

y contains the category of the 178-dimensional input vector. Specifically y in {1, 2, 3, 4, 5}

5- eyes open, means when they were recording the EEG signal of the brain the patient had their eyes open
4- eyes closed, means when they were recording the EEG signal the patient had their eyes closed
3- Yes they identify where the region of the tumor was in the brain and recording the EEG activity from the healthy brain area
2- They recorder the EEG from the area where the tumor was located

All subjects falling in classes 2, 3, 4, and 5 are subjects who did not have epileptic seizure

1- Recording of seizure activity

The Explanatory variables X1, X2, ..., X178

Each 178-dimensional vector contained in a row, represents a randomly selected 1-second long sample picked from the single file. Recall that
each file is a recording of brain activity for 23.6 seconds. The corresponding time-series is sampled into 4097 data points.

The original dataset consists of 5 different folders, each with 100 files, with each file representing a single subject/person. Each file is a recording of brain activity for 23.6 seconds. The corresponding time-series is sampled into 4097 data points. Each data point is the value of the EEG recording at a different point in time. Our motivation for creating this version of the data was to simplify access to the data via the creation of a .csv version of it. Although there are 5 classes most authors have done binary classification, namely class 1 (Epileptic seizure) against the rest.


Relevant Papers:

N/A



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