###### Abstract  
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.  
reinforcement learning; detectors; epilepsy; remote sensing; optical flow +
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A central issue for such a development is the wide variety of clinical conditions, camera parameters, and other environmental factors that can challenge the universality of the algorithms used to provide real-time alerting. To address these challenges, we developed an adaptive reinforcement concept that can account for nonstationary applications and optimize the detection process accordingly.  
In an earlier work, we addressed the problem of reducing false-positive detections by restricting the detection to only validated true-positive events. This, however, left open the question of missing events, or false negatives. Here, a solution is proposed that can also expand the detection space and gradually reduce the chance of undetected true events.  
To verify the effectiveness of our algorithms, synthetic data that can account for a variety of dynamic situations and nonstationary environments are used.  
This paper is organized as follows. In the Materials and Methods (Section 2), the general concept of TOREAD (Topological Reinforcement Adaptive Algorithm) is described. Then, we present its particular application to convulsive seizure alerting. We introduce the concept of dual detection, which allows for the accounting of missed true seizures and expansion of the detection parameters.  
A brief introduction to the MC (Monte Carlo) type of simulating input data is included, and its role in the validation of the classifier and adaptive algorithms is explained.  
In the Results (Section 3), an array of trials demonstrating the effectiveness of our approach for cases of different initial detector states and different levels of separation between events and background signal controlled by the signal-generation parameters is presented.  
In the Discussion (Section 4), we state the main motivations for our concept and the assumptions behind it. We also acknowledge certain limitations of the approach and propose some directions for further extensions.  
Finally, our major findings are summarized in the Conclusions (Section 5).