cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13026/hwvj-5b53
title: Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB)

description: Brno University of Technology ECG Signal Database with Annotations of P Wave
(BUT PDB) is an ECG signal database with marked peaks of P waves created by
the cardiology team at the Department of Biomedical Engineering, Brno
University of Technology. The database consists of 50 2-minute 2-lead ECG
signal records with various types of pathology. The ECGs were selected from
three existing databases of ECG signal - the MIT-BIH Arrhythmia Database, the
MIT-BIH Supraventricular Arrhythmia Database, and the Long Term AF Database.
The P waves positions were manually annotated by two ECG experts in all 50
records. Each record contains also annotation of positions and types of QRS
complexes (from original database) and dominant diagnosis (pathology) present
in record. This database is created for the development, evaluation and
objective comparison of P wave detection algorithms.
DOI: 10.13026/ksw4-4y55
title: KURIAS-ECG: a 12-lead electrocardiogram database with standardized diagnosis ontology

description: The 12-lead electrocardiogram (ECG) is the fundamental test used to evaluate
the electrophysiological state of the heart. Most previous ECG databases use
the diagnosis confirmed by experts. Therefore, ECG diagnosis is not
standardized and its quality is not uniform. In addition, there is a problem
in that the diagnosis statements differ depending on the ECG machine. This
limits the use of the database for further research, such as artificial
intelligence and clinical research. Of note, modern ECG machines provide
computerized rule-based ECG diagnoses comparable to physicians'
interpretations.

Here a high-quality 12-lead ECG database with standard vocabularies (SNOMED-CT
and OMOP-CDM), which was transformed from a computerized ECG diagnosis of ECG
machines was developed. A total of 147 ECG diagnoses were grouped into 10
categories of the Minnesota code classification. To improve the quality of the
database, ECG cases of inappropriate ECG, such as poor quality, reverse arm
ECG, and missing data, were removed. In addition, to minimize skew of the
database, 2000 ECG cases were extracted for each Minnesota classification
category. As a result of database construction, the database consisted of
20000 records for 10 categories of the Minnesota code classification from
13862 patients. The standardized database can be utilized for comprehensive
research on the diagnosis of cardiac disease and the development of robust
artificial intelligence technology.
DOI: 10.13026/m792-h077
title: Multitaper spectra recorded during GABAergic anesthetic unconsciousness

description: This database contains electroencephalography (EEG) multitaper spectra and
associated conscious/unconscious labels for a cohort of 10 healthy volunteers
undergoing stereotyped anesthetic administration and direct monitoring of
subject responsiveness, and 44 patients receiving anesthesia care in an
operating room (OR) context. Cases recorded in an OR context are further
divided into those administered propofol alone under a total intravenous
anesthesia (TIVA) approach, and those who received either sevoflurane alone or
sevoflurane following a propofol induction. These data were used to train and
validate classification models for tracking unconsciousness. Corresponding
code for building and testing classification models is publicly available.
DOI: 10.13026/mjat-sv46
title: VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs

description: Radiographs are used as the most critical imaging tool for identifying spine
anomalies in clinical practice [1]. The evaluation of spinal bone lesions,
however, is a challenging task for radiologists. To the best of our knowledge,
no existing studies are devoted to developing and evaluating a comprehensive
system for classifying and localizing multiple spine lesions from X-ray scans.
The lack of large-scale spine X-ray datasets with high-quality images and
human expert annotations is the key obstacle. To fill this gap, we introduce a
large-scale annotated medical image dataset for spinal lesion detection and
classification from radiographs. The dataset, called VinDr-SpineXR, contains
10,466 spine X-ray images from 5,000 studies, each of which is manually
annotated with 13 types of abnormalities by an experienced radiologist with
bounding boxes around abnormal findings. This is the largest dataset to date
that provides radiologist's bounding-box annotations for developing
supervised-learning algorithms for spine X-ray analysis.
DOI: 10.13026/pgk4-gx55
title: Upper body thermal images and associated clinical data from a pilot cohort study of COVID-19

description: The prospective upper body thermal images SARS-CoV2 association study was
designed to test the hypothesis that thermal videos may aid in the early
diagnosis of COVID-19. The study recorded a set of measurements from 252
participants regarding PCR results, demographics, vital signs, participant
activities, medications, respiratory symptoms, and a thermal video session
where the volunteers performed simple breath-hold in four different positions.
The acquired data may be used to test clinical association questions regarding
temperature patterns, demographics, and vital signs. Furthermore, it could be
valuable to develop new computer algorithms for extracting useful scientific
information from thermal videos.
DOI: 10.13026/pnkb-9r03
title: NCH Sleep DataBank: A Large Collection of Real-world Pediatric Sleep Studies with Longitudinal Clinical Data

description: In order to accelerate research on pediatric sleep and its connection to
health, Nationwide Children's Hospital (NCH) and Carnegie Mellon University
(CMU) introduce the NCH Sleep DataBank. This dataset has 3,984 pediatric sleep
studies on 3,673 unique patients conducted at NCH in Columbus, Ohio, USA
between 2017 and 2019, along with the patient's longitudinal clinical data.
The published Polysomnography (PSG) contains the patient's physiological
signals as well as the technician's assessment of the sleep stages and
descriptions of additional irregularities.

The novelties of this dataset include: (1) Size **:** Its large size is
suitable for discovering new scientific insights via data mining,  (2) ****
Patient population **:** It explicitly focuses on pediatric patients, (3)
Clinical setting **:** The sleep studies were gathered in the real-world
clinical setting at NCH as opposed to, for example, in a controlled clinical
trial, and (4) Rich set of clinical data **:** The accompanying 5.6 million
records of clinical data are extracted from the Electronic Health Record
(EHR), and are separated into encounters, medications, measurements (e.g. body
mass index), diagnoses, and procedures.

The NCH Sleep DataBank is a valuable resource for advancing automatic sleep
scoring and real-time sleep disorder prediction, among many other potential
scientific discoveries. Accompanying code in Python to assist users in
interacting with the dataset is published on GitHub.
DOI: 10.13026/qhft-e228
title: REFLACX: Reports and eye-tracking data for localization of abnormalities in chest x-rays

description: Labels localizing anomalies are rare in current chest x-rays datasets. We
collected a dataset, using a method that can potentially be scaled up, to be a
proof-of-concept for collecting implicit localization data through an eye-
tracker. This dataset, named REFLACX, provides eye-tracking data collected
while radiologists dictated reports for frontal chest x-rays from the MIMIC-
CXR dataset, paired with the timestamped transcription of the dictation. The
dataset contains 3,032 cases of synchronized eye-tracking and transcription
pairs labeled by five radiologists. For each case, we also provide other
labels for validating algorithms derived from this dataset. These labels
include image-level labels of anomalies found during the reading, ellipses
localizing these anomalies, and bounding boxes around lungs and heart. A
subset of 109 chest x-rays has readings from all radiologists to estimate the
variability of the provided labels.
DOI: 10.13026/qk1q-vq60
title: RadNLI: A natural language inference dataset for the radiology domain

description: The problem of natural language inference (NLI) determines whether a natural
language hypothesis can be justifiably inferred from a natural language
premise. NLI has attracted researchers to benchmark it in a number of settings
including medical ones. While NLI datasets such as the MedNLI dataset exist
for the clinical domain, systems trained with them do not generalize well to
applications that require understanding of radiology reports. We therefore
introduce an NLI dataset in the radiology domain, in which NLI information is
annotated on sentences drawn from radiology reports. Sentence pairs in our
dataset are sampled from MIMIC-CXR and we annotated them with NLI labels by
two experts: one medical expert and one computer science expert. Each pair is
annotated twice, swapping its premise and hypothesis, resulting in 960 pairs.
The set is then split in half, resulting in 480 pair for a validation set and
480 pairs for a test set. We confirmed that a BERT-based NLI model trained
with a distant supervision approach can achieve the accuracy of 77.8% on this
test set.
DOI: 10.13026/sq6b-9n68
title: NCH Sleep DataBank: A Large Collection of Real-world Pediatric Sleep Studies with Longitudinal Clinical Data

description: In order to accelerate research on pediatric sleep and its connection to
health, Nationwide Children's Hospital (NCH) and Carnegie Mellon University
(CMU) introduce the NCH Sleep DataBank. This dataset has 3,984 pediatric sleep
studies on 3,673 unique patients conducted at NCH in Columbus, Ohio, USA
between 2017 and 2019, along with the patient's longitudinal clinical data.
The published Polysomnography (PSG) contains the patient's physiological
signals as well as the technician's assessment of the sleep stages and
descriptions of additional irregularities.

The novelties of this dataset include: (1) Size **:** Its large size is
suitable for discovering new scientific insights via data mining,  (2) ****
Patient population **:** It explicitly focuses on pediatric patients, (3)
Clinical setting **:** The sleep studies were gathered in the real-world
clinical setting at NCH as opposed to, for example, in a controlled clinical
trial, and (4)  Rich set of clinical data **:** The accompanying 5.6 million
records of clinical data are extracted from the Electronic Health Record
(EHR), and are separated into encounters, medications, measurements (e.g. body
mass index), diagnoses, and procedures.

The NCH Sleep DataBank is a valuable resource for advancing automatic sleep
scoring and real-time sleep disorder prediction, among many other potential
scientific discoveries. Accompanying code in Python to assist users in
interacting with the dataset is published on GitHub.
DOI: 10.13026/vk2z-bz63
title: Clinical BERT Models Trained on Pseudo Re-identified MIMIC-III Notes

description: This project contains the weights of 7 different BERT models trained over a
surrogate re-identified set of MIMIC-III (v1.4) notes. Each model is suitable
for direct compatibility with the HuggingFace framework; users can easily load
and apply the model. We release these model weights with the intent of
facilitating research into the dangers of revealing patient information via
model sharing, specifically for models pretrained on non-deidentified
electronic health record (EHR) data. We additionally release our post-
processed data, which most importantly contains a mapping of patient subject
id to condition(s) that the given patient has. The purpose of these "subject
id to condition" files is to measure how much of a correlation can be
extracted from a surrogate patient name and the medical conditions that the
given patient has. This will allow users to quantify the amount of Protected
Health Information (PHI) "leakage" these large pretrained language models may
exhibit.
DOI: 10.13026/wfr2-5973
title: Upper body thermal images and associated clinical data from a pilot cohort study of COVID-19

description: The prospective upper body thermal images SARS-CoV2 association study was
designed to test the hypothesis that thermal videos may aid in the early
diagnosis of COVID-19. The study recorded a set of measurements from 252
participants regarding PCR results, demographics, vital signs, participant
activities, medications, respiratory symptoms, and a thermal video session
where the volunteers performed simple breath-hold in four different positions.
The acquired data may be used to test clinical association questions regarding
temperature patterns, demographics, and vital signs. Furthermore, it could be
valuable to develop new computer algorithms for extracting useful scientific
information from thermal videos.
DOI: 10.13026/zwds-nn76
title: MIMIC-III and eICU-CRD: Feature Representation by FIDDLE Preprocessing

description: This is a preprocessed dataset derived from patient records in MIMIC-III and
eICU, two large-scale electronic health record (EHR) databases. It contains
features and labels for 5 prediction tasks involving 3 adverse outcomes
(prediction times listed in parentheses): in-hospital mortality (48h), acute
respiratory failure (4h and 12h), and shock (4h and 12h). We extracted
comprehensive, high-dimensional feature representations (up to ~8,000
features) using FIDDLE ( **F** lex **I** ble  **D** ata- **D** riven pipe
**L** in **E** ), an open-source preprocessing pipeline for structured
clinical data. These 5 prediction tasks were designed in consultation with a
critical care physician for their clinical importance, and were used as part
of the proof-of-concept experiments in the original paper to demonstrate
FIDDLE's utility in aiding the feature engineering step of machine learning
model development. The intent of this release is to share preprocessed MIMIC-
III and eICU datasets used in the experiments to support and enable
reproducible machine learning research on EHR data.
DOI: 10.13026/136t-7g98
title: Electrodermal Activity of Healthy Volunteers while Awake and at Rest

description: This dataset of electrodermal activity was collected from 11 healthy volunteer
subjects who were awake and at rest in seated position. The activity was
recorded from each subject's non-dominant hand for one hour at 256 Hz.
Electrodermal activity measures changing electrical conductance of the skin as
an indicator of sweat gland activity. Sweat glands are a primitive part of the
fight-or-flight response. These data were collected as part of a larger study
to understand and build computational models for autonomic nervous system
activity (including electrodermal activity) with approval from the
Massachusetts Institute of Technology Committee on the Use of Humans as
Experimental Subjects (COUHES).  The second dataset of electrodermal activity
was collected from 11 healthy volunteer subjects who were under computer-
controlled propofol sedation. The activity was recorded from each subject's
left hand for about 3 hours at 500 Hz. These data were collected with approval
from the Massachusetts General Hospital Human Research Committee.
DOI: 10.13026/2rbc-1r03
title: Behavioral and autonomic dynamics during propofol-induced unconsciousness

description: During general anesthesia, both behavioral and autonomic changes are caused by
the administration of anesthetics such as propofol. Propofol produces
unconsciousness by creating highly structured oscillations in brain circuits.
The anesthetic also has autonomic effects due to its actions as a vasodilator
and myocardial depressant. Understanding how autonomic dynamics change in
relation to propofol-induced unconsciousness is an important scientific and
clinical question since anesthesiologists often infer changes in level of
unconsciousness from changes in autonomic dynamics.

Therefore, we present a dataset combining physiology-based indices for heart
rate variability and electrodermal activity with a robust statistical tool to
compare behavioral and multimodal autonomic changes before, during, and after
propofol-induced unconsciousness. This dataset was recorded from nine healthy
volunteers during computer-controlled administration of propofol.
DOI: 10.13026/7egw-0p30
title: Haaglanden Medisch Centrum sleep staging database

description: A collection of 154 whole-night polysomnographic (PSG) sleep recordings (88
Male, 66 Female, mean Age of 53.8 ± 15.4) collected during 2018 at the
Haaglanden Medisch Centrum (HMC, The Netherlands) sleep center. Patient
recordings were randomly selected and include a heterogeneous population which
was referred for PSG examination on the context of different sleep disorders.
The dataset contains electroencephalographic (EEG), electrooculographic (EOG),
chin electromyographic (EMG), and electrocardiographic (ECG) activity, as well
as event annotations corresponding to scoring of sleep patterns (hypnogram)
performed by sleep technicians at HMC. The dataset was collected as part of a
study evaluating the generalization performance of an automatic sleep scoring
algorithm across multiple heterogeneous datasets.
DOI: 10.13026/ema4-bv32
title: NCH Sleep DataBank: A Large Collection of Real-world Pediatric Sleep Studies with Longitudinal Clinical Data

description: In order to accelerate research on pediatric sleep and its connection to
health, Nationwide Children's Hospital (NCH) and Carnegie Mellon University
(CMU) introduce the NCH Sleep DataBank. This dataset has 3,984 pediatric sleep
studies on 3,673 unique patients conducted at NCH in Columbus, Ohio, USA
between 2017 and 2019, along with the patient's longitudinal clinical data.
The published Polysomnography (PSG) contains the patient's physiological
signals as well as the technician's assessment of the sleep stages and
descriptions of additional irregularities.

The novelties of this dataset include: (1) Size **:** Its large size is
suitable for discovering new scientific insights via data mining,  (2) ****
Patient population **:** It explicitly focuses on pediatric patients, (3)
Clinical setting **:** The sleep studies were gathered in the real-world
clinical setting at NCH as opposed to, for example, in a controlled clinical
trial, and (4) Rich set of clinical data **:** The accompanying 5.6 million
records of clinical data are extracted from the Electronic Health Record
(EHR), and are separated into encounters, medications, measurements (e.g. body
mass index), diagnoses, and procedures.

The NCH Sleep DataBank is a valuable resource for advancing automatic sleep
scoring and real-time sleep disorder prediction, among many other potential
scientific discoveries. Accompanying code in Python to assist users in
interacting with the dataset is published on GitHub.
DOI: 10.13026/fp4y-6m41
title: NCH Sleep DataBank: A Large Collection of Real-world Pediatric Sleep Studies with Longitudinal Clinical Data

description: In order to accelerate research on pediatric sleep and its connection to
health, Nationwide Children's Hospital (NCH) and Carnegie Mellon University
(CMU) introduce the NCH Sleep DataBank. This dataset has 3,984 pediatric sleep
studies on 3,673 unique patients conducted at NCH in Columbus, Ohio, USA
between 2017 and 2019, along with the patient's longitudinal clinical data.
The published Polysomnography (PSG) contains the patient's physiological
signals as well as the technician's assessment of the sleep stages and
descriptions of additional irregularities.

The novelties of this dataset include: (1) Size **:** Its large size is
suitable for discovering new scientific insights via data mining,  (2) ****
Patient population **:** It explicitly focuses on pediatric patients, (3)
Clinical setting **:** The sleep studies were gathered in the real-world
clinical setting at NCH as opposed to, for example, in a controlled clinical
trial, and (4)  Rich set of clinical data **:** The accompanying 5.6 million
records of clinical data are extracted from the Electronic Health Record
(EHR), and are separated into encounters, medications, measurements (e.g. body
mass index), diagnoses, and procedures.

The NCH Sleep DataBank is a valuable resource for advancing automatic sleep
scoring and real-time sleep disorder prediction, among many other potential
scientific discoveries. Accompanying code in Python to assist users in
interacting with the dataset is published on GitHub.
DOI: 10.13026/gmav-2367
title: Behavioral and autonomic dynamics during propofol-induced unconsciousness

description: During general anesthesia, both behavioral and autonomic changes are caused by
the administration of anesthetics such as propofol. Propofol produces
unconsciousness by creating highly structured oscillations in brain circuits.
The anesthetic also has autonomic effects due to its actions as a vasodilator
and myocardial depressant. Understanding how autonomic dynamics change in
relation to propofol-induced unconsciousness is an important scientific and
clinical question since anesthesiologists often infer changes in level of
unconsciousness from changes in autonomic dynamics.

Therefore, we present a dataset combining physiology-based indices for heart
rate variability and electrodermal activity with a robust statistical tool to
compare behavioral and multimodal autonomic changes before, during, and after
propofol-induced unconsciousness. This dataset was recorded from nine healthy
volunteers during computer-controlled administration of propofol.
DOI: 10.13026/gq9q-rr81
title: Tai Chi, Physiological Complexity, and Healthy Aging - Gait

description: This dataset contains gait and electromyography (EMG) data collected during a
hybrid study that included a two-arm randomized clinical trial (RCT) along
with an additional observational comparison group. The RCT arm investigated
the short-term effects of a Tai Chi intervention on 60 Tai Chi healthy naive
subjects, aged 50-79 years, living within the Greater Boston area, and
reporting no regular Tai Chi practice within the past 5 years. Subjects were
either randomized to 6 months of Tai Chi or usual care. Subjects came in for a
baseline visit along with 3 month and 6 month follow-ups. The observational
comparison group consisted of 27 healthy Tai Chi experts, aged 50-79 years,
currently engaged in an active Tai Chi training regimen, each with at least 5
years of Tai Chi experience. The expert group was tested only at their
baseline visit. The gait and EMG data were simultaneously recorded during
walking at the subjects preferred speed for 10 minutes under the single-task
condition and for 90 seconds under the dual-task condition. Our aim for this
study was to use gait and EMG data to gain a better understanding of the
mobility changes associated with Tai Chi training.
DOI: 10.13026/h6z4-fx76
title: A multi-camera and multimodal dataset for posture and gait analysis

description: Gait and posture analysis while using assisting robotic devices is of utmost
importance to attain effective assistance. This work provides a multi-camera,
multimodal, and detailed dataset for vision-based applications using a wheeled
robotic walker equipped with a pair of affordable cameras. Depth data was
acquired at 30 fps from a total of 14 healthy participants walking at 3
different gait speeds, across 3 different walking scenarios/paths at 3
different locations. Simultaneously, accurate skeleton joint data was recorded
using an inertial-based commercial motion capture system that provides a
reliable ground-truth for classical or novel (i.e., machine learning-based)
vision-based applications. In total, the database contains approximately 166K
frames of synchronized data, which amounts to 92 minutes of total recording
time. This dataset may contribute to the development and evaluation of: i)
classic or data-driven vision-based pose estimation algorithms; ii)
applications in human detection and tracking, and movement forecasting; iii)
and gait/posture metrics analysis using a rehabilitation device.