ArticleJournal of neuroscience methods2021
Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns.
Article in Journal of neuroscience methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Harvard Electroencephalography Database: A comprehensive clinical electroencephalographic resource from four Boston hospitals.Epilepsia · 2025Article
- Automatic epileptic seizure detection based on EEG using a moth-flame optimization of one-dimensional convolutional neural networks.Frontiers in neuroscience · 2023Article
- Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks.Resuscitation · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundManual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG. NEW
methodWe learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters.
resultsMean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ± 4.44 min to label the 30.19 ± 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency. COMPARISON WITH EXISTING
methodsPrevious studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods.
conclusionsOur results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.
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