Automatic Detection of Epileptiform Discharges in the EEG
This provides a valuable tool for neurology services to reduce workload in analyzing long-term EEG records, though it is incremental as it builds on existing EEG analysis techniques.
The paper tackled the time-consuming visual inspection of EEG for epilepsy diagnosis by developing an automatic detection system for epileptiform discharges, achieving average sensitivity and specificity higher than 80% and 70%, respectively.
The diagnosis of epilepsy generally includes a visual inspection of EEG recorded data by the Neurologist, with the purpose of checking the occurrence of transient waveforms called interictal epileptiform discharges. These waveforms have short duration (less than 100 ms), so the inspection process is usually time-consuming, particularly for ambulatory long term EEG records. Therefore, an automatic detection system of epileptiform discharges can be a valuable tool for a Neurology service. The proposed approach is the development of a multi stage detection algorithm, which processes the complete EEG signals and applies decision criteria to selected waveforms. It employs EEG analysis techniques such as Wavelet Transform and Mimetic Analysis, complemented with a classification based on Fuzzy Logic. In order to evaluate the algorithm's performance, data were collected from several epileptic patients, with epileptiform activity marked by a Neurologist. The average values obtained for both Sensitivity and Specificity were respectively higher than 80 and 70 percent.