Topological Reinforcement Adaptive Algorithm (TOREADA) Application to the Alerting of Convulsive Seizures and Validation with Monte Carlo Numerical Simulations

The detection of adverse events-for example, convulsive epileptic seizures-can be critical for patients suffering from a variety of pathological syndromes. Algorithms using remote sensing modalities, such as a video camera input, can be effective for real-time alerting, but the broad variability of environments and numerous nonstationary factors may limit their precision. In this work, we address the issue of adaptive reinforcement that can provide flexible applications in alerting devices. The general concept of our approach is the topological reinforced adaptive algorithm (TOREADA). Three essential steps-embedding, assessment, and envelope-act iteratively during the operation of the system, thus providing continuous, on-the-fly, reinforced learning. We apply this concept in the case of detecting convulsive epileptic seizures, where three parameters define the decision manifold. Monte Carlo-type simulations validate the effectiveness and robustness of the approach. We show that the adaptive procedure finds the correct detection parameters, providing optimal accuracy from a large variety of initial states. With respect to the separation quality between simulated seizure and normal epochs, the detection reinforcement algorithm is robust within the broad margins of signal-generation scenarios. We conclude that our technique is applicable to a large variety of event detection systems.