IMPROVED SEGMENTATION FOR AUTOMATED SEIZURE DETECTION USING CHANNEL-DEPENDENT POSTERIORS
The electroencephalogram (EEG) is the primary tool used for the diagnosis of a varietyof neural pathologies such as epilepsy. Identification of a critical event, such as an epileptic seizure, is difficult because the signals are collected by transducing extremely low voltages, and as a result, are corrupted by noise. Also, EEG signals often contain artifacts due to clinical phenomena such as patient movement. These artifacts are easily confused as seizure events. Factors such as slowly evolving morphologies make accurate marking of the onset and offset of a seizure event difficult. Precise segmentation, defined as the ability to detect start and stop times within a fraction of a second, is a challenging research problem. In this dissertation, we improve seizure segmentation performance by developing deep learning technology that mimics the human interpretation process. The central thesis of this work is that separation of the seizure detection problem into a two-phase problem – epileptiform activity detection followed by seizure detection – should improve our ability to detect and localize seizure events. In the first phase, we use a sequential neural network algorithm known as a long short-term memory (LSTM) network to identify channel-specific epileptiform discharges associated with seizures. In the second phase, the feature vector is augmented with posteriors that represent the onset and offset of ictal activities. These augmented features are applied to a multichannel convolutional neural network (CNN) followed by an LSTM network. The multiphase model was evaluated on a blind evaluation set and was shown to detect 106 segment boundaries within a 2-second margin of error. Our previous best system, which delivers state-of-the-art performance on this task, correctly detected only 9 segment boundaries. Our multiphase system was also shown to be robust by performing well on two blind evaluation sets. Seizure detection performance on the TU Seizure Detection (TUSZ) Corpus development set is 41.60% sensitivity with 5.63 false alarms/24 hours (FAs/24 hrs). Performance on the corresponding evaluation set is 48.21% sensitivity with 16.54 FAs/24 hrs. Performance on a previously unseen corpus, the Duke University Seizure (DUSZ) Corpus is 46.62% sensitivity with 7.86 FAs/24 hrs. Our previous best system yields 30.83% sensitivity with 6.74 FAs/24 hrs on the TUSZ development set, 33.11% sensitivity with 19.89 FAs/24 hrs on the TUSZ evaluation set and 33.71% sensitivity with 40.40 FAs/24 hrs on DUSZ. Improving seizure detection performance through better segmentation is an important step forward in making automated seizure detection systems clinically acceptable. For a real-time system, accurate segmentation will allow clinicians detect a seizure as soon as it appears in the EEG signal. This will allow neurologists to act during the early stages of the event which, in many cases, is essential to avoid permanent damage to the brain. In a similar way, accurate offset detection will help with delivery of therapies designed to mitigate postictal (after seizure) period symptoms. This will also help reveal the severity of a seizure and consequently provide guidance for medicating a patient.
Artificial intelligence, Neurosciences, EEG, Evaluation metrics, Machine learning, Neural networks, Segmentation, Seizures
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