Jin Jing1, Emile d'Angremont2, Senan Ebrahim3, Mohammad Tabaeizadeh3, Marcus Ng4, Aline Herlopian5, Justin Dauwels6, M Brandon Westover7. 1. Massachusetts General Hospital, Boston, MA, United States; Nanyang Technological University, Singapore, Singapore. 2. University Medical Center Groningen, The Netherlands. 3. Massachusetts General Hospital, Boston, MA, United States. 4. University of Manitoba, Winnipeg, MB, Canada. 5. Yale University School of Medicine, New Haven, CT, United States. 6. Nanyang Technological University, Singapore, Singapore. 7. Massachusetts General Hospital, Boston, MA, United States. Electronic address: mwestover@mgh.harvard.edu.
Abstract
BACKGROUND: Manual 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 METHOD: We 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. RESULTS: Mean 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 METHODS: Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. CONCLUSIONS: Our 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.
BACKGROUND: Manual 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 METHOD: We 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. RESULTS: Mean 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 METHODS: Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. CONCLUSIONS: Our 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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