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
