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 c
We proposed a patient-specific deep learningbased single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial s...
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Collection of EEG recordings of 22 pediatric subjects with intractable seizuresData CardCode (24)Discussion (0)Suggestions (0)Suggestions search tuneAll FiltersClear Allclose Typeexpand_morePendingexpand_more Recently updated No results found To see more results, try reducing the number of ...
Dataset(X_test, y_test, reference=lgb_train) # 参数设置# 迭代次数 early_stop_rounds = 100 # 验证数据若在early_stop_rounds轮中未提高,则提前停止 params = { 'boosting_type': 'gbdt', # 设置提升类型 'objective': 'binary', # 目标函数 'metric': 'acc,auc,binary_logloss', # 评估函数 ...
In this study, using EEG signals of patients from CHB-MIT dataset, we were able to achieve sensitivity of 90.76%.Ali EsmaeilpourShaghayegh Shahiri TabarestaniAlireza NiaziEngineering Reports
Deep learning‐based seizure prediction using EEG signals: A comparative analysis of classification methods on the CHB‐MIT datasetdoi:10.1002/eng2.12918Esmaeilpour, AliTabarestani, Shaghayegh ShahiriNiazi, AlirezaEngineering Reports