Spiking Seizure Classification Dataset

Dataset for event encoded analog EEG signals for detection of Epileptic seizures This dataset contains events that are encoded from the analog signals recorded during pre-surgical evaluations of patients at the Sleep-Wake-Epilepsy-Center (SWEC) of the University Department of Neurology at the Inselspital Bern. The analog signals are sourced from the SWEC-ETHZ iEEG Database This database contains event streams for 10 seizures recorded from 5 patients and generated by the DYnamic Neuromorphic Asynchronous Processor (DYNAP-SE2) to demonstrate a proof-of-concept of encoding seizures with network synchronization. The pipeline consists of two parts (I) an Analog Front End (AFE) and (II) an SNN termed as"Non-Local Non-Global" (NLNG) network. In the first part of the pipeline, the digitally recorded signals from SWEC-ETHZ iEEG Database are converted to analog signals via an 18-bit Digital-to-Analog converter (DAC) and then amplified and encoded into events by an Asynchronous Delta Modulator (ADM). Then in the second part, the encoded event streams are fed into the SNN that extracts the features of the epileptic seizure by extracting the partial synchronous patterns intrinsic to the seizure dynamics. Details about the neuromorphic processing pipeline and the encoding process are included in a manuscript under review. The main files in the database are described in the hierarchy below. EventSezDataset/ ├─ data/ │ ├─ P x S x │ │ ├─ Pat x _Sz_ x _CH x .csv ├─ fig_gen.py ├─ support.py ├─ sync_mat_gen.py ├─ requirements.txt where, x represents the Patient ID and the Seizure ID respectively. requirements.txt: This file lists the requirements for execution of the Python code. fig_gen.py: This file plots the analog signals and the associated AFE and NLNG event streams. The execution of the code happens with python3 fig_gen.py 2 1 13, where patient 2, seizure 1 and channel 13 of the recording are plotted. sync_mat_gen.py: This file describes the function for plotting the synchronization matrices emerging from the NLNG spikes. The execution of the code happens with python3 sync_mat_gen.py. This execution generated three figures for pre-ictal , ictal, and post-ictal time-periods. The directory can be changed with SEIZ_DIR ="data/P1S1/" to specific seizures of specific patients and the time windows of the sync matrix calculation can be changed with pre_seizure_times, seizure_times, post_seizure_times for their respective time-periods. The time is the signal-time as mentioned in the table below. support.py: This file contains the necessary functions. data/P1S1/: This folder, for example, contains the event streams for all channels for seizure 1 of patient 1.</p> Pat1_Sz_1_CH1.csv: This file contains the spikes of the AFE and the NLNG layers with the following tabular format (which can be extracted by the fig_gen.py) ## Comments # SStart: 180 //Start of the Seizure in signal time# SEnd: 276.0 //Start of the Seizure in signal time# Pid: 2 // The patient ID as per the SWEC-ETHZ iEEG Database # Sid: 1 // The Seizure ID as per the SWEC-ETHZ iEEG Database # Channel_No: 1 // The channel number SYS_time signal_time dac_value ADMspikes NLNGspikes The time from the interface FPGA The time of the signal as per the SWEC ETHZ Database The value of the analog signals as recorded in the SWEC ETHZ Database The event-steam is the output of the AFE in boolean format. True represents a spike The spike-steam is the output of the SNN in boolean format. True represents a spike用于癫痫发作检测的事件编码模拟脑电（EEG）信号数据集 本数据集包含的事件，源自伯尔尼 Inselspital 大学神经内科睡眠-觉醒-癫痫中心（Sleep-Wake-Epilepsy-Center, SWEC）对患者进行术前评估期间记录的模拟信号。此类模拟信号来源于SWEC-ETHZ颅内脑电图（intracranial EEG, iEEG）数据库。 该数据库包含来自5名患者的10次癫痫发作的事件流，由动态神经形态异步处理器（DYnamic Neuromorphic Asynchronous Processor, DYNAP-SE2）生成，用于验证基于网络同步的癫痫发作编码概念可行性。整个处理流程分为两个部分：(I) 模拟前端（Analog Front End, AFE），以及(II) 被称为“非局部非全局”（Non-Local Non-Global, NLNG）的脉冲神经网络（Spiking Neural Network, SNN）。 流程的第一阶段中，SWEC-ETHZ iEEG数据库中的数字记录信号先通过18位数模转换器（Digital-to-Analog Converter, DAC）转换为模拟信号，随后经放大处理，并通过异步增量调制器（Asynchronous Delta Modulator, ADM）编码为事件流。第二阶段中，编码后的事件流被输入至SNN，该网络通过提取癫痫发作动态中固有的局部同步模式，实现对癫痫发作特征的提取。 有关神经形态处理流程与编码过程的详细信息，收录于一篇正在审稿的学术论文中。 该数据库的主要文件按如下层级结构组织： EventSezDataset/ ├─ data/ │ ├─ P<PatientID>_S<SeizureID>/ │ │ ├─ Pat<PatientID>_Sz<SeizureID>_<ChannelID>.csv ├─ fig_gen.py ├─ support.py ├─ sync_mat_gen.py ├─ requirements.txt 其中，<PatientID>与<SeizureID>分别代表患者编号与发作编号。 requirements.txt：该文件列出了运行Python代码所需的依赖项。 fig_gen.py：该文件用于绘制模拟信号及其对应的AFE与NLNG事件流。运行代码的命令为`python3 fig_gen.py 2 1 13`，即可绘制患者2、发作1的第13通道的记录数据。 sync_mat_gen.py：该文件包含用于绘制由NLNG脉冲生成的同步矩阵的函数。直接运行`python3 sync_mat_gen.py`即可执行代码，其将生成发作前期、发作期与发作后期三个时段的同步矩阵图。用户可通过修改`SEIZ_DIR = "data/P1S1/"`来指定特定患者的特定发作数据，并通过修改`pre_seizure_times`、`seizure_times`与`post_seizure_times`参数来调整各时段的时间窗口，其中时间单位为SWEC ETHZ数据库中定义的信号时间。 support.py：该文件包含所需的辅助函数。 data/P1S1/：以患者1的发作1为例，该文件夹包含该次发作所有通道的事件流数据。 Pat1_Sz_1_CH1.csv：该文件包含AFE与NLNG层的脉冲数据，其表格格式可通过fig_gen.py提取，格式如下： SStart: 180 // 以信号时间计的癫痫发作起始时刻 SEnd: 276.0 // 以信号时间计的癫痫发作结束时刻 Pid: 2 // SWEC-ETHZ iEEG数据库中对应的患者编号 Sid: 1 // SWEC-ETHZ iEEG数据库中对应的发作编号 Channel_No: 1 // 通道编号 表格列字段说明： SYS_time：来自接口现场可编程门阵列（Field-Programmable Gate Array, FPGA）的系统时间 signal_time：SWEC ETHZ数据库中定义的信号时间 dac_value：SWEC ETHZ数据库中记录的模拟信号数值 ADMspikes：AFE输出的事件流，以布尔格式存储，True代表存在脉冲 NLNGspikes：SNN输出的脉冲流，以布尔格式存储，True代表存在脉冲