DS004840#

Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.

Access recordings and metadata through EEGDash.

Citation: Jose Cordoba-Silva, Rafael Maya, Mario Valderrama, Luis Felipe Giraldo, William Betancourt-Zapata, Andrés Salgado-Vascob, Juliana Marín-Sánchez, Viviana Gómez-Ortega, Mark Ettenberger (2023). Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.. 10.18112/openneuro.ds004840.v1.0.1

Modality: eeg Subjects: 9 Recordings: 107 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004840

dataset = DS004840(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 = DS004840(cache_dir="./data", subject="01")

Advanced query

dataset = DS004840(
    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{ds004840,
  title = {Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.},
  author = {Jose Cordoba-Silva and Rafael Maya and Mario Valderrama and Luis Felipe Giraldo and William Betancourt-Zapata and Andrés Salgado-Vascob and Juliana Marín-Sánchez and Viviana Gómez-Ortega and Mark Ettenberger},
  doi = {10.18112/openneuro.ds004840.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004840.v1.0.1},
}

About This Dataset#

Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit - README

Table of Contents

1. Experimental Design_ - 1.1 Study Overview_

View full README

Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit - README

Table of Contents

1. Experimental Design_ - 1.1 Study Overview_ - 1.2 Electrophysiological Measurements_
2. Pain Perception and Anxiety-Depression Levels_
3. Code GIT repository_

1. Experimental Design

1.1 Study Overview

This dataset forms part of an ongoing single-site Randomized Clinical Trial (RCT) involving adult burn patients admitted to the Intensive Care Unit (ICU). The key details of the study include:

Participants: The study encompasses 82 adult burn patients admitted to the ICU.
Randomization: Participants were randomly assigned to either an intervention group or a control group in a 1:1 ratio. The intervention group received standard care in addition to a maximum of six Music therapy sessions provided by a certified music therapist over a 2-week period.
Electrophysiological Measures: Electrophysiological measures were taken from a subset of 9 participants in the intervention group (11%).
Ethics and Registration: The study was approved by the ethics committee of the Fundación Santa Fe de Bogotá (FSFB) with approval IDs CCEI-11234-2019 and CCEI-11971-2020. It is registered on Clinicaltrials.gov under the identifier NCT04571255.
Informed Consent: All participants provided informed consent to participate in the study.

1.2 Electrophysiological Measurements

Participants: The electrophysiological measurements were conducted with nine adult burn patients hospitalized in the ICU of the University Hospital Fundación Santa Fe de Bogotá (FSFB).
Inclusion Criteria: Inclusion criteria involved individuals of legal adult age with an expected hospitalization period of more than 7 days. Patients with known psychiatric disorders, cognitive disabilities, sedation, or mechanical ventilation were excluded. Patients with burns in regions above the neck were also excluded.
Measurement Sessions: Electrophysiological measurements were performed with each patient during two Music-Assisted Relaxation (MAR) sessions on two different days.
Recording Phases: Each recording session included three phases: - Pre-Intervention (PRE): The resting state was measured as a baseline with the patient’s eyes closed or fixed at a point. - MAR MTI: The specialist performed the MAR MTI. - Post-Intervention (POST): Measurements were taken during the patient’s reincorporation after MAR.
Equipment: Recordings were made with the Micromed LTM64 equipment with a sampling frequency of at least 256Hz. The Micromed LTM64 is of clinical quality and has approval from the Colombian National Institute for Drug and Food Safety (approval ID: 20090486-2015).
Electrode Setup: - EEG: The electrode montage followed the international System 10-20. Due to time limitations, the number of electrodes was reduced to eight: FP1, FP2, T3, T4, C3, C4, O1, and O2. The reference electrode was set to Cz, and the ground electrode was placed on the mastoids. - ECG: ECG was acquired by a bipolar assembly of lead II with two electrodes located bilaterally in the upper part of the thorax or both arms, depending on each patient’s possibilities or limitations. - EMG: For EMG, a bipolar electrode configuration was positioned on the left eyebrow to assess the motor activity of the corrugator supercilii muscle. The electrodes were placed with a 20 mm distance between them, following the natural alignment of the muscle fibers.

2. Pain Perception and Anxiety-Depression Levels

Measures: To correlate pain, anxiety, and depression levels with electrophysiological signals, two complementary measures were obtained.
Pain Assessment: A Visual Analog Scale (VAS) was administered before the PRE and after the POST. The VAS ranged from 0 (indicating no pain) to 10 (representing the maximum pain possible).
Anxiety and Depression: The Colombian version of the Hospital Anxiety and Depression Scale (HADS) was used. HADS consists of two sub-scales for anxiety and depression, each containing seven items. Items are rated on a four-point Likert scale (0 to 3), with higher scores indicating increased anxiety or depression levels (maximum score of 21 for each subscale). HADS was administered after obtaining informed consent, both as a baseline and after the last music therapy sessions.

3. Code GIT repository

All the analysis and graphics in this project were conducted using Python 3.7. We utilized custom scripts in combination with various libraries, such as NumPy, Pandas, SciPy, MNE, Biopsy, neurokit2, and Visbrain. After generating the graphics, we enhanced them in PowerPoint by adding titles, symbols, and any necessary supplementary information.
Code is Open by MIT license at: jgcordoba/BurnICU_MusicTherapy_Signals.git

For more detailed information about the study, please refer to the associated article titled “Article not submitted”, DOI: ‘-Insert Link-’

Last Update: 23/10/2023 Author: Jose Gabriel Cordoba Silva

Dataset Information#

Dataset ID	`DS004840`
Title	Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.
Year	2023
Authors	Jose Cordoba-Silva, Rafael Maya, Mario Valderrama, Luis Felipe Giraldo, William Betancourt-Zapata, Andrés Salgado-Vascob, Juliana Marín-Sánchez, Viviana Gómez-Ortega, Mark Ettenberger
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004840.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: 9
Recordings: 107
Tasks: 3

Channels & sampling rate

Channels: 8
Sampling rate (Hz): 1024.0 (66), 512.0 (30), 256.0 (6)
Duration (hours): 0.0

API Reference#

Use the DS004840 class to access this dataset programmatically.

class eegdash.dataset.DS004840(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004840. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 9; recordings: 51; tasks: 3.

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/ds004840 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004840

Examples

>>> from eegdash.dataset import DS004840
>>> dataset = DS004840(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