This database, collected at the Children’s Hospital Boston, consists of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention. The recordings are grouped into 23 cases and were collected from 22 subjects (5 males, ages 3–22; and 17 females, ages 1.5–19).

Univ. of Bonn’ and ‘CHB-MIT Scalp EEG Database’ are publically available datasets which are the most sought after amongst researchers. Bonn dataset is very small compared to CHB-MIT. But still researchers prefer Bonn as it is in simple '.txt' format. The dataset being published here is a preprocessed form of CHB-MIT. The dataset is available in '.csv' format.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on November 22, 2022. Data set collected at the Children''s Hospital Boston, of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention. All signals were sampled at 256 samples per second with 16-bit resolution. Most files contain 23 EEG signals (24 or 26 in a few cases).

This dataset is a BIDS-compatible version of the CHB-MIT Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect:

• The data from subject chb21 was moved to sub-01/ses-02.
• Metadata was organized according to BIDS.
• Data in the EEG edf files was modified to keep only the 18 channels from a double banana bipolar montage.
• Annotations were formatted as BIDS-score compatible `tsv` files.

Details related to access to the data

License

The dataset is released under the Open Data Commons Attribution License v1.0.

Contact person

The original Physionet CHB-MIT Scalp EEG Database was published by Ali Shoeb. This BIDS-compatible version of the dataset was published by Jonathan Dan.

Practical information to access the data

The original Physionet CHB-MIT Scalp EEG Database is available on the Physionet website.

Overview

Project name

CHB-MIT Scalp EEG Database

Year that the project ran

2010

Brief overview of the tasks in the experiment

This database, collected at the Children's Hospital Boston, consists of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention.

Description of the contents of the dataset

Each folder (sub-01, sub-01, etc.) contains between 9 and 42 continuous .edf files from a single subject. Hardware limitations resulted in gaps between consecutively-numbered .edf files, during which the signals were not recorded; in most cases, the gaps are 10 seconds or less, but occasionally there are much longer gaps. In order to protect the privacy of the subjects, all protected health information (PHI) in the original .edf files has been replaced with surrogate information in the files provided here. Dates in the original .edf files have been replaced by surrogate dates, but the time relationships between the individual files belonging to each case have been preserved. In most cases, the .edf files contain exactly one hour of digitized EEG signals, although those belonging to case sub-10 are two hours long, and those belonging to cases sub-04, sub-06, sub-07, sub-09, and sub-23 are four hours long; occasionally, files in which seizures are recorded are shorter.

The EEG is recorded at 256 Hz with a 16-bit resolution. The recordings are referenced in a double banana bipolar montage with 18 channels from the 10-20 electrode system.

The dataset also contains seizure annotations as start and stop times.

The dataset contains 664 `.edf` recordings. 129 those files that contain one or more seizures. In all, these records include 198 seizures.

Methods

Subjects

23 pediatric subjects with intractable seizures. (5 males, ages 3–22; and 17 females, ages 1.5–19; 1 n/a)

Apparatus

Recordings were performed at the Children's Hospital Boston using the International 10-20 system of EEG electrode positions. Signals were sampled at 256 samples per second with 16-bit resolution.

Artificial intelligence (AI) based automated epilepsy diagnosis has aimed to ease the burden of manual detection, prediction, and management of seizure and epilepsy-specific EEG signals for medical specialists. With increasing open-source, raw, and large EEG datasets, there is a need for data standardization of patient and seizure-sensitive AI analysis with reduced redundant information. This work releases a balanced, annotated, fixed time and length meta-data of CHB-MIT Scalp EEG database v1.0.0.0.

The work releases patient-specific (inter and intra) and patient non-specific EEG data extracted using specific time stamps of ictal, pre-ictal, post-ictal, peri-ictal, and non-seizure EEG provided in the original dataset (annotations). Further details of this metadata can be found in the provided csv file (CHB-MIT DB timestamp.csv). The released EEG data is available in csv format and class labels are provided in the last row of the csv files. Data of ch06, ch12, ch23, and ch24 in patient-specific and chb24_11 in patient non-specific have not been included. The importance of peri-ictal EEGs has been elucidated in Handa, P., & Goel, N. (2021). Peri‐ictal and non‐seizure EEG event detection using generated metadata. Expert Systems, e12929.

This database, from the Czech Technical University (CTU) in Prague and the University Hospital in Brno (UHB), contains 552 cardiotocography (CTG) recordings, which were carefully selected from 9164 recordings collected between 2010 and 2012 at UHB.

The CHB-MIT dataset is a dataset of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure mediation in order to characterize their seizures and assess their candidacy for surgical intervention. The dataset contains 23 patients divided among 24 cases (a patient has 2 recordings, 1.5 years apart). The dataset consists of 969 Hours of scalp EEG recordings with 173 seizures. There exist various types of seizures in the dataset (clonic, atonic, tonic). The diversity of patients (Male, Female, 10-22 years old) and different types of seizures contained in the datasets are ideal for assessing the performance of automatic seizure detection methods in realistic settings.

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due to individual differences in EEG signals, the generalization ability of epilepsy detection models is week. To address this issue, this paper presents a cross-patient epilepsy detection method utilizing a multi-head self-attention mechanism. This method first utilizes Short-Time Fourier Transform (STFT) to transform the original EEG signals into time-frequency features, then models local information using Convolutional Neural Network (CNN), subsequently captures global dependency relationships between features using the multi-head self-attention mechanism of Transformer, and finally performs epilepsy detection using these features. Meanwhile, this model employs a light multi-head attention mechanism module with an alternating structure, which can comprehensively extract multi-scale features while significantly reducing computational costs. Experimental results on the CHB-MIT dataset show that the proposed model achieves accuracy, sensitivity, specificity, F1 score, and AUC of 92.89%, 96.17%, 92.99%, 94.41%, and 96.77%, respectively. Compared to the existing methods, the method proposed in this paper obtains better performance along with better generalization.

Artificial intelligence (AI) based automated epilepsy diagnosis has aimed to ease the burden of manual detection, prediction, and management of seizure and epilepsy-specific EEG signals for medical specialists. Existing research work in this domain has highlighted the significance of 2D EEG frames extracted through various processing pipelines over 1D signal analysis using various CNN architectures like AlexNet, LeNet. This is a pre-processed image (rhythmicity spectrogram) dataset generated from the CHB-MIT EEG scalp database. The dataset consists of 105 frames from chb01, 30 frames from chb02, 90 frames from chb05, and 75 frames from chb05 separately from both ictal and non-seizure edf files. The total image frames and ictal time (20 ictal signals) are 600 frames and 25 minutes respectively. The dataset has been divided into train, test and validate folders wherein seizure and non-seizure EEG images have been put in png format. It can be incorporated in the machine and deep learning pipelines for the detection of seizure and non-seizure EEG images.

For further technical details see the following publication: Handa, P., & Goel, N. (2021, August). Epileptic Seizure Detection Using Rhythmicity Spectrogram and Cross-Patient Test Set. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 898-902). IEEE.

Protein-Protein, Genetic, and Chemical Interactions for CHB (Drosophila melanogaster) curated by BioGRID (https://thebiogrid.org); DEFINITION: chromosome bows

Notes: We tested the overrepresentation of rare variants between the CHB (90 individuals) and CHD populations (30 individuals). For each dataset, the total number of variants was given, followed by the number of variants that have larger frequencies in the CHB population (the first number in the parenthesis) and the number of variants that have larger frequencies in the CHD population (the second number in the parenthesis). See notes for table 1 for abbreviation detail.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

A list of the top 50 CHB Investment Group LLC holdings showing which stocks are owned by CHB Investment Group LLC's hedge fund.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset is affiliated to the paper: Xi, W., Zhang, X., & Ayalon, L. (2022). When less intergenerational closeness helps: The influence of intergenerational physical proximity and technology attributes on technophobia among older adults. Computers in Human Behavior, 131, 107234. doi.org/10.1016/j.chb.2022.107234

Comparison with existing methods on the CHB-MIT dataset.

We found high levels of contaminants, in particular organochlorines, in eggs of the ivory gull Pagophila eburnea, a high Arctic seabird species threatened by climate change and contaminants. An 80% decline in the ivory gull breeding population in the Canadian Arctic the last two decades has been documented. Because of the dependence of the ivory gull on sea ice and its high trophic position, suggested environmental threats are climate change and contaminants. The present study investigated contaminant levels (organochlorines, brominated flame retardants, perfluorinated alkyl substances, and mercury) in ivory gull eggs from four colonies in the Norwegian Svalbard) and Russian Arctic (Franz Josef Land and Severnaya Zemlya). The contaminant levels presented here are among the highest reported in Arctic seabird species, and we identify this as an important stressor in a species already at risk due to environmental change.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

AI-powered price forecasts for CHB.TO stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Figure S1. Forest plot of the association of Vit D on CHB (A), Vit D on CHC (B), 25-OHD on CHB (C), 25-OHD on CHC (D). Vit D, vitamin D; 25-OHD, 25-hydroxyvitamin D; CHB, chronic hepatitis B; CHC, chronic hepatitis C.
Figure S2. LOO sensitivity analysis of the association of Vit D on CHB (A), Vit D on CHC (B), 25-OHD on CHB (C), 25-OHD on CHC (D). Vit D, vitamin D; 25-OHD, 25-hydroxyvitamin D; CHB, chronic hepatitis B; CHC, chronic hepatitis C.
Figure S3. Scatter plots showing the causal effect of Vit D on CHB (A), Vit D on CHC (B), 25-OHD on CHB (C), 25-OHD on CHC (D). Vit D, vitamin D; 25-OHD, 25-hydroxyvitamin D; CHB, chronic hepatitis B; CHC, chronic hepatitis C.
Figure S4. Funnel plot of the association of Vit D on CHB (A), Vit D on CHC (B), 25-OHD on CHB (C), 25-OHD on CHC (D). Vit D, vitamin D; 25-OHD, 25-hydroxyvitamin D; CHB, chronic hepatitis B; CHC, chronic hepatitis C.
Figure S5. Forest plot of the association of CHB on Vit D (A), CHC on Vit D (B), CHB on 25-OHD (C), CHC on 25-OHD (D). Vit D, vitamin D; 25-OHD, 25-hydroxyvitamin D; CHB, chronic hepatitis B; CHC, chronic hepatitis C.
Figure S6. LOO sensitivity analysis of the association of CHB on Vit D (A), CHC on Vit D (B), CHB on 25-OHD (C), CHC on 25-OHD (D). Vit D, vitamin D; 25-OHD, 25-hydroxyvitamin D; CHB, chronic hepatitis B; CHC, chronic hepatitis C.
Figure S7. Scatter plots showing the causal effect of CHB on Vit D (A), CHC on Vit D (B), CHB on 25-OHD (C), CHC on 25-OHD (D). Vit D, vitamin D; 25-OHD, 25-hydroxyvitamin D; CHB, chronic hepatitis B; CHC, chronic hepatitis C.
Figure S8. Funnel plot of the association of CHB on Vit D (A), CHC on Vit D (B), CHB on 25-OHD (C), CHC on 25-OHD (D). Vit D, vitamin D; 25-OHD, 25-hydroxyvitamin D; CHB, chronic hepatitis B; CHC, chronic hepatitis C.
Table S1. Baseline characteristics of Vit D and CH dataset in the present study.
Table S2. MR results between Vit D and CH.
Table S3. Pleiotropy and heterogeneity test between Vit D and CH.