Seizure pattern-specific epileptic epoch detection in patients with intellectual disability. (May 2017) - Wang, Lei; Arends, Johan B.A.M.; Long, Xi; Cluitmans, Pierre J.M.; van Dijk, Johannes P.
Highlights: We evaluated the impact of seizure patterns on detection performance. Apart from the state-of-the-art EEG features, we also developed new features here. The entire non-seizure EEG recordings were used for classification validation. The P-R curve was used for performance evaluation instead of the ROC curve. The importance analysis of EEG features was performed. Abstract: Electroencephalogram (EEG) features are crucial for the seizure detection performance. Traditional algorithms are designed for a population with normal brain development. However, for patients with an intellectual disability the seizure detection performance is still largely unknown. In addition, distinct EEG activities/patterns occur during the evolution of seizure events. However, few studies distinguished what EEG activities contribute to accurate seizure detections. To evaluate the effect of different seizure patterns on the seizure detection, we start from the four predefined seizure patterns: wave, fast spike, spike-wave complex, and seizure-related EMG artifacts. A wide range of promising EEG features in the time, frequency, time–frequency, and spatio-temporal domains, as well as synchronization-based features were extracted to characterize these patterns. The performance of seizure detection was evaluated in an epoch-based way. EEG recordings of 615 h from 29 epilepsy patients with intellectual disability were used in this study for validation. Results show that the seizure patterns of wave, and seizure-related EMG were easier to detect than the fast spike, spike-wave patterns, with sensitivities of 0.76, 0.74, 0.42, and 0.51, respectively (when specificity approximately equal to 1). We achieved the overall epoch-based detection performance with sensitivity of 68%, positive predictive value (PPV) 81%, and average duration of false detection 0.76 s per hour. Feature importance analysis indicated that the classification performance of traditional EEG features can be improved when combined with our newly-proposed features from the spatio-temporal domain and the synchronization-based methods.