Automatic detection of epileptiform events in EEG recordings

An electroencephalogram (EEG) is the most important tool in the diagnosis of seizure disorders. Between seizures, epileptiform neural activities in EEG recordings occur in the forms of spikes or spike-and-slow wave complexes. Seeking for an automated EEG interpretation algorithm that is well-accepted by clinicians has been a research goal stretched for decades. As a participant in an NSF-funded Research Experience for Undergraduates (REU) program hosted at Clemson University School of Computing, I continued on this endeavor to develop an automated system that detected epilepsy-related events, in real-time, from scalp EEG recordings.

In finding the optimal algorithm for this purpose, I constructed a multi-stage processing pipeline. In the first stage, I cleaned up the clinic data gathered from 100 epileptic patients and treated them with cross-validation. Next, I used wavelet transformations to generate the features for study from EEG signal in a “sliding window” approach. I then applied machine learning algorithms and analyzed their performances in classifying data patterns into epileptiform activities versus other activities. For this stage I also explored the use of hidden Markov model to fit the time sequence in which epileptiform events occurred. In the final step, I further separated target eplieptiform events from noise signals, by applying a statistical model locally, and stitched outputs from different signal windows together. – source code

The automation results were highlighted these findings in realtime on the eegNet (standardized EEG database developed by Clemson) web interface.

Automatic detection of epileptiform events in EEG recordings – poster

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