Automatic Seizure Detection Using Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and Improved Deep Forest Learning

Automatic seizure detection could facilitate early detection, improve treatment planning, and reduce medical workload. This study describes a novel Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and an improved Deep Forest learning algorithm for epileptic seizure detection. The LE-GMMs could map the Riemannian manifold structure of Gaussian models to linear Euclidean space, which fully exploits the ability of GMMs to distinguish non-seizure and seizure EEG signals. The Multi-Pooling and error Screening Forest (MPSForest) learning method based on Deep Forest uses multi-pooling and out-of-bagging (OOB) error screening to reduce memory load and random tree construction. Firstly, variational modal decomposition (VMD) is applied to decompose electroencephalogram (EEG) signals into five layers, and the first three layers are chosen to construct EEG time-frequency distribution. Then Gaussian Mixture Models are estimated, and the LE-GMMs are constructed to extract valid EEG features. These features are input into the MPSForest model to classify seizure and non-seizure samples. After that, the outputs are subjected to post-processing to get the final seizure detection results, including moving average filtering and the adaptive collar technique. The proposed method achieves average sensitivity of 98.22% and specificity of 98.99% on the UPenn and Mayo Clinic dataset, and for the long-term Freiburg EEG dataset with 21 patients, the sensitivity of 98.47% and specificity of 98.57% are yielded respectively with the false detection rate of 0.24/h. The experimental results show that this proposed method has excellent accuracy in distinguishing non-seizure and seizure EEG signals and holds great potential for clinical research and diagnostics.