Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Abstract

Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.

Keywords: Algorithms; Clinical Neuroscience; Computer Application in Medicine; Computer-Aided Diagnosis Method; Techniques in Neuroscience.

Graphical abstract

Figure 1

Sketch of the proposed algorithm for automated seizure detection during training and testing For a Figure360 author presentation of this figure, see https://doi.org/10.1016/j.isci.2020.101997. The algorithm takes as input the LEM recording signal and partitions it into non-overlapping windows. For each window, it extracts eight features (descriptive statistics and complexity measures) that are used for classification by the RUSBoost algorithm. During training, clinical supervision is needed to determine the onset and ending of a seizure for 80% of the data. During testing, no clinical supervision is required, and the trained model is employed to classify the remaining 20% of the data, within 5-fold cross-validation.

Figure 2

Sensitivity, specificity, and accuracy (in percent) for 5-fold cross-validation analysis of 24-h LEM recordings of 10 different patients, from different channels of the LEM recordings For each patient and each metric, we report data from central (blue diamonds) electrodes and electrodes in the right (filled, red circles) or left (open, red circles) hemispheres, along with mean and standard deviation (black bars with whiskers).

Figure 3

Visualization of a seizure through the topology of the ε-symbolic recurrence, constructed from 100 observations (0.391 s) from a single channel (T3) For clarity, the network is overlaid with the EEG recordings to display the onset of the seizure, ictal organization, and seizure ending and post-ictal. The network is assembled using six symbols (embedding dimension m = 3) and proximity parameter ε = 10 μV; each color identifies symbolic recurrence to a different symbol. From the left to the right network, mean degree, betweenness centrality, and closeness are (11.04, 4.58, 1.37 × 10−3), (1.22, 4.55, 0.16 × 10−3), and (9.77, 3.82, 1.21 × 10−3).