SeizeIT1

[The SeizeIT1 dataset will no longer be shared upon request due to expiry of the ethical approval. You can get access to SeizeIT2 if you sign up to the challenge at
<a href=https://biomedepi.github.io/seizure_detection_challenge/>https://biomedepi.github.io/seizure_detection_challenge/</a>]
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This dataset is obtained during an ICON project (2017-2018) in collaboration with KU Leuven (ESAT-STADIUS), UZ Leuven, UCB, Byteflies and Pilipili. The goal of this project was to design a system using Behind the ear (bhE) EEG electrodes for monitoring the patient in a home environment. This way, a nice balance can be found between sufficient accuracy of seizure detection algorithms (because EEG is used) and wearability (bhe EEG is relatively subtle, similar to a hear-aid device). The dataset acquired in the hospital during presurgical evaluation. During such presurgical evaluation, neurologists try to see if a specific part of the brain is causing the seizures, and if so, if that part of the brain can be removed during surgery. During the presurgical evaluation, patients are monitored using the vEEG for multiple days (typically a week). Patients are however restricted to move within their room because of the wiring and video analysis. In this dataset, following data is available per patient:
• Full 10-20 scalp EEG data of the patient during the presurgical evaluation.
• Behind-the-ear data (2 sensors positioned behind each ear)
• Single-lead ECG data (typically lead II)

Seizures are annotated by the clinicians based on the gold standard vEEG system. These seizure annotations are also available in the dataset. In total 82 patients were recorded between 23/01/2017 and 26/10/2018. From those patients, 54 were recorded with the bhe channels. Forty-two of those patients had seizures during their presurgical evaluation, while for twelve patients no seizure has been recorded.

The number of seizures per patient ranged from 1 to 22, with a median of 3 seizures per patient. The duration of the seizures, the time difference of seizure EEG onset and end, varied between 11 and 695 seconds with a median of 50 seconds. 89% of the seizures were Focal Impaired Awareness seizures. 91% of the seizures originated from the (fronto-) temporal lobe.

In the folder ’Data’ the raw data in the form of .edf, are provided with annotations for all the patients. The annotations are provided in .tsv (tab separated values) files. For every seizure the first column represents the starting point (in seconds) of the seizure, the second one the end point of the seizure, the third one the type of the seizure, while in the last column extra information are provided. The extra information includes the origin of the seizure, the hemisphere and if the seizure can be noted from the behind the ear channels (bhe:1 in that case). In the header section of every file information concerning the dataset and the annotations used are included. For every subject and for every session (even if no seizure is present) two different sets of annotations are provided. The ”a1”set of annotations is the annotations as provided by the doctors. The ”a2” set of annotations are the annotations used in [2] for training of the algorithm. The annotations provided from the doctors were not always perfectly aligned with the typical rhythmic ictal pattern, hence in ”a2” a refinement of the start of each annotation was performed visually by an engineer. Furthermore, in the annotations of the doctor the
end point of some seizures was missing (”none”) in the ”a2” subset of annotations each seizure was considered with a stable length of 10 seconds.

由于伦理审批已过期，SeizeIT1数据集将不再接受申请共享。您可通过报名参与该挑战赛获取SeizeIT2数据集，报名链接为：https://biomedepi.github.io/seizure_detection_challenge/

本数据集源自2017-2018年的ICON项目，由KU Leuven（ESAT-STADIUS实验室）、UZ Leuven、UCB、Byteflies与Pilipili联合开展。该项目旨在开发一套基于耳后（Behind the ear, bhE）脑电（Electroencephalogram, EEG）电极的系统，用于居家环境下的患者监护，以此在癫痫检测算法的足够精度（依托脑电采集）与设备佩戴舒适性（耳后脑电电极相对隐蔽，类似助听器）之间取得平衡。本次采集的数据集来自医院术前评估阶段。

在术前评估阶段，神经科医师会通过检查明确引发癫痫发作的大脑特定区域，并评估该区域是否可通过手术切除。此阶段患者需接受视频脑电（Video EEG, vEEG）连续多日（通常为一周）的监护，但由于布线与视频分析的限制，患者仅能在病房内活动。本数据集包含每位患者的以下数据：
• 患者术前评估期间的完整10-20系统头皮脑电数据
• 耳后脑电数据（双耳后方各放置1个传感器）
• 单导联心电图（Electrocardiogram, ECG）数据（通常为II导联）

癫痫发作标注由临床医师基于金标准视频脑电系统完成，该标注信息也包含在本数据集中。本次数据集共收录2017年1月23日至2018年10月26日期间的82例患者数据，其中54例患者采集了耳后脑电通道数据。82例患者中，42例在术前评估期间出现癫痫发作，其余12例未记录到癫痫发作。

每位患者的癫痫发作次数介于1至22次之间，中位数为3次。单次癫痫发作的持续时间（即脑电发作起始至结束的时长）范围为11至695秒，中位数为50秒。89%的发作类型为局灶性意识障碍性癫痫（Focal Impaired Awareness seizures），91%的癫痫起源于（额）颞叶。

所有患者的原始数据以.edf格式存储于"Data"文件夹中，并附带标注信息。标注信息保存为.tsv（制表符分隔值）文件。每份标注文件中，每一行对应一次癫痫发作：第一列为发作起始时间（单位：秒），第二列为发作结束时间，第三列为发作类型，最后一列为额外信息。额外信息包括癫痫起源区域、大脑半球，以及该发作是否可通过耳后脑电通道检测到（符合条件则标注bhe:1）。每个标注文件的头部均包含数据集与所用标注的相关说明。
针对每位受试者的每一次监护时段（即使未出现癫痫发作），均提供两套不同的标注集："a1"为临床医师直接提供的原始标注；"a2"为文献[2]中用于算法训练的标注集。由于医师提供的原始标注并非总能与典型的发作期节律模式完全匹配，因此在"a2"标注集中，工程师通过视觉检查对每处标注的起始时间进行了修正。此外，医师标注中部分癫痫发作的结束时间缺失（标注为"none"），因此在"a2"子集中，所有发作均统一设置为固定时长10秒。

搜集汇总

背景与挑战

背景概述

SeizeIT1是一个专注于癫痫监测的研究数据集，包含82名患者的脑电图(EEG)和心电图(ECG)数据，特别关注耳后电极的使用，旨在提高家庭环境中的癫痫检测。数据集包含详细的癫痫发作注释，但由于伦理批准到期，现已不再公开共享。