A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction

Abstract

Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a critical step. Past approaches failed to attain real-world applicability due to lack of generalization capacity. More recently, deep learning techniques may outperform traditional classifiers and handle time dependencies. However, despite the existing efforts for providing interpretable insights, clinicians may not be willing to make high-stake decisions based on them. Furthermore, a disadvantageous aspect of the more usual seizure prediction pipeline is its modularity and significant independence between stages. An alternative could be the construction of a search algorithm that, while considering pipeline stages’ synergy, fine-tunes the selection of a reduced set of features that are widely used in the literature and computationally efficient. With extracranial recordings from 19 patients suffering from temporal-lobe seizures, we developed a patient-specific evolutionary optimization strategy, aiming to generate the optimal set of features for seizure prediction with a logistic regression classifier, which was tested prospectively in a total of 49 seizures and 710 h of continuous recording and performed above chance for 32% of patients, using a surrogate predictor. These results demonstrate the hypothesis of pre-ictal period identification without the loss of interpretability, which may help understanding brain dynamics leading to seizures and improve prediction algorithms.

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches

“…In this paper, Pinto et al [44] used extra data from temporal-lobe seizure-suffering patients which were recorded and developed a specific (patient-oriented) prediction system with optimization policy, targeting to produce best features for seizure forecast. Regression-based logistic regression classifiers were used for testing and verified rigorously for 710 hours total of 49 seizures of continuous recording of the workflow as 5 Computational and Mathematical Methods in Medicine shown in Figure 4.…”

Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

“…Reliable seizure forecasts could potentially allow people living with recurrent seizures to modify their activities, take a fast-acting medication, or increase neuromodulation therapy to prevent or manage impending seizures. Accurate seizure forecasts have been demonstrated using invasively sampled ultralong-term EEG in ambulatory canine [6][7][8] and human subjects [9][10][11][12][13][14] , including a prospective study with a dedicated device 11 . However, invasive devices may not be acceptable for some patients with epilepsy, and no clinically available invasive device currently has the capability to sample and telemeter data needed for seizure forecasting.…”

Prediction of Seizure Recurrence. A Note of Caution

“…A few reviews of the many applications of signal processing and predictive algorithms present the enormous breadth of this effort ( 151 – 154 ). This approach has begun to be applied to seizure prediction ( 155 , 156 ) with the recognition that the amount of raw EEG data needed for deep learning approaches might be prohibitively large ( 157 ).…”