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An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients the algorithm detected 100% of the seizures. A mean sensitivity of 83% was achieved. An average of 7.2 false alarms within 24 hours for unselected EEG makes the algorithm attractive for epilepsy monitoring units.

The detection of epileptic seizures in long-term electroencephalographic (EEG) recordings is a time-consuming and tedious task requiring specially trained medical experts. The EpiScan seizure detection algorithm developed by the Austrian Institute of Technology (AIT) has proven to achieve high detection performance with a robust false alarm rate in the clinical setting. This paper introduces a novel time domain method for detection of epileptic seizure patterns with focus on irregular and distorted rhythmic activity. The method scans the EEG for sequences of similar epileptiform discharges and uses a combination of duration and similarity measure to decide for a seizure. The resulting method was tested on an EEG database with 275 patients including over 22000h of unselected and uncut EEG recording and 623 seizures. Used in combination with the EpiScan algorithm we increased the overall sensitivity from 70% to 73% while reducing the false alarm rate from 0.33 to 0.30 alarms per hour.

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