Seizure detection: an assessment of time- and frequency-based features in a unified two-dimensional decisional space using nonlinear decision functions

Abstract

Objective: This study proposes a new approach for offline seizure detection in intracranial (subdural) electroencephalogram recordings using nonlinear decision functions. It implements well-established features that are designed to deal with complex signals, such as brain recordings, and proposes a two-dimensional (2D) domain of analysis that overcomes the dilemma faced with the selection of empirical thresholds often used to delineate epileptic events. This unifying approach makes it possible for researchers in epilepsy to establish other performance evaluation criteria on the basis of the proposed nonlinear decision functions as well as introduce additional dimensions toward multidimensional analysis because the mathematics of these decision functions allows for any number of dimensions and any degree of complexity. Furthermore, because the features considered assume both time and frequency domains, the analysis is performed both temporally and as a function of different frequency ranges to ascertain those measures that are most suitable for seizure detection. In retrospect, by using nonlinear decision functions and by establishing a unified 2D domain of analysis, this study establishes a generalized approach to seizure detection that works across several features and across patients.

Methods: Clinical experiments involved 14 patients with intractable seizures that were evaluated for potential surgical interventions. Of the total 157 files considered, 35 (21 interictal and 14 ictal) intracranial electroencephalogram data files or 22% were used initially in a training phase to ascertain the reliability of the formulated features that were implemented in the seizure detection process. The remaining 122 intracranial electroencephalogram data files or 78% were then used in the testing phase to assess the merits of each feature considered as means to detect a seizure.

Results: The testing phase using the remaining 122 intracranial electroencephalogram data files revealed that the gamma power in the frequency domain is the feature that performed best across all patients with a sensitivity of 96.296%, an accuracy of 96.721%, and a specificity of 96.842%. The second best feature in the time domain was the mobility with a sensitivity of 81.481% an accuracy of 90.169%, and a specificity of 92.632%. In the frequency domain, all of the five other spectral bands lesser than 36 Hz revealed mixed results in terms of low sensitivity in some frequency bands and low accuracy in other frequency bands, which is expected given that the dominant frequencies during an ictal state are those higher than 30 Hz. In the time domain, other features, including complexity and correlation sum, revealed mixed success.

Conclusions: All the features that are based on the time domain performed well, with mobility being the optimal feature for seizure detection. In the frequency domain, the gamma power outperformed the other frequency bands. Within this 2D plane, the best results were also observed when the degree of complexity is 3 or 4 in the implementation of the proposed nonlinear decision functions.

Significance: : A singular contribution of this study is in creating a common 2D space for analysis through the use of nonlinear decision functions for delineating data clusters of ictal files from data clusters of interictal files. This is critically important in establishing unifying measures that work across different features as expressed by the weight vector of the decision functions for a standardized assessment. The mathematical foundation is consequently established in support of a generalized seizure detection algorithm that works across patients, and in which all type of features that have been amply tested in the literature could be assessed within the realm of nonlinear decision functions.