DS003555#

Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system

Access recordings and metadata through EEGDash.

Citation: Dorottya Cserpan, Ece Boran, Richard Rosch, San Pietro Lo Biundo, Georgia Ramantani, Johannes Sarnthein (2021). Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system. 10.18112/openneuro.ds003555.v1.0.1

Modality: eeg Subjects: 30 Recordings: 96 License: CC0 Source: openneuro Citations: 8.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003555

dataset = DS003555(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS003555(cache_dir="./data", subject="01")

Advanced query

dataset = DS003555(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds003555,
  title = {Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system},
  author = {Dorottya Cserpan and Ece Boran and Richard Rosch and San Pietro Lo Biundo and Georgia Ramantani and Johannes Sarnthein},
  doi = {10.18112/openneuro.ds003555.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003555.v1.0.1},
}

About This Dataset#

Dataset of EEG recordings containing HFO markings for 30 pediatric patients with epilepsy

Summary

High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the pediatric population. We recorded and processed the first 3 hours of sleep EEG data in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80-250 Hz) in bipolar channels. The software for the detection of HFOs is freely available at the GitHub repository (ZurichNCH/Automatic-High-Frequency-Oscillation-Detector). Furthermore HFO markings are also added in this database for the selected N3 intervals.

View full README

Dataset of EEG recordings containing HFO markings for 30 pediatric patients with epilepsy

Summary

High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the pediatric population. We recorded and processed the first 3 hours of sleep EEG data in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80-250 Hz) in bipolar channels. The software for the detection of HFOs is freely available at the GitHub repository (ZurichNCH/Automatic-High-Frequency-Oscillation-Detector). Furthermore HFO markings are also added in this database for the selected N3 intervals.

Repository structure

Main directory (hfo/)

Contains metadata files in the BIDS standard about the participants and the study. Folders are explained below.

Subfolders

*hfo/sub-**/ Contains folders for each subject, named sub-<subject number> and session information. *hfo/sub-**/ses-01/eeg Contains the raw eeg data in .edf format for each subject. The duration is typically 3 hours, that was recorded in the beginning of the sleep. Details about the channels are given in the corresponding .tsv file. * hfo/derivatives

Besides containingsubfolders for the raw data, there are two .json files. The events_description.json explains the meaning of the columns of the event description tsv files (in the subfolders). The interval_description.json explains the meaning of the columns of the interval description tsv files (in the subfolders).

*hfo/derivatives/sub-**/ses-01/eeg/ Contains processed data for each subject. Based on the sleep annotations, first we identified the sleep stages. Then we cut 5 minutes data intervals from the N3 sleep stages. We applied bipolar referencing by considering all nearest neighbour chanels, thus resulting in 52 bipolar channels. Each run corresponds to one 5 minute data interval. The DataIntervals.tsv file provides information about how the various runs are related to the raw data by providing the start and end indeces. Besides the .edf and channel descriptor .tsv files there is an other .tsv file containing the detected candidate event details. Eg. sub-26_ses-01_task-hfo_run-01_events.tsv contains for subject 26 for the first processed data interval the event markings as indeces with additional features of this event described in the abovementioned events_description.json file.

Related materials

The code for HFO detection is available at ZurichNCH/Automatic-High-Frequency-Oscillation-Detector

Support

For questions on the dataset or the task, contact Johannes Sarnthein at johannes.sarnthein@usz.ch_.

Dataset Information#

Dataset ID	`DS003555`
Title	Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system
Year	2021
Authors	Dorottya Cserpan, Ece Boran, Richard Rosch, San Pietro Lo Biundo, Georgia Ramantani, Johannes Sarnthein
License	CC0
Citation / DOI	10.18112/openneuro.ds003555.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 30
Recordings: 96
Tasks: 1

Channels & sampling rate

Channels: 23 (54), 24 (6)
Sampling rate (Hz): 1024.0
Duration (hours): 0.0

API Reference#

Use the DS003555 class to access this dataset programmatically.

class eegdash.dataset.DS003555(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003555. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003555 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003555

Examples

>>> from eegdash.dataset import DS003555
>>> dataset = DS003555(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None