## 4 Discussion
It can be, for example, the fluctuation rate or, more generally, certain momenta of the distribution. We believe that the topological reinforcement paradigm proposed here can be applicable to those cases by properly parameterizing the embedding and envelope manifolds and algorithms. As for the simulation of data, one can apply a similar technique to the one used here to generate synthetic signals, by selecting two model distributions representing seizures and normal states. This and possibly other generalized implementations of TOREAD will be considered in our future research. The classic approaches to machine learning, including applications targeting detection of convulsive epileptic states, rely on available big collections of labeled data [14]. The founding belief is that these collections contain the necessary information and that they can account for all the variability of the real-world scenarios. Our concept does not require, although it may use, a prerecorded labeled sets of events. It acquires its training sets along with the operation of the detector and, therefore, is a paradigm of continuous learning from experience. This way, the method is applicable to nonstationary situations and, more generally, can cope with the real-world variability of conditions. Moreover, in contrast to the static data collection paradigm, the TOREAD concept can potentially apply adaptive reinforcement to the data acquisition protocols. It can, for example, adjust spectral ranges, apertures, and other sensor properties in order to achieve optimal performance. The above topological paradigm also presents certain restrictions. If the events that we are attempting to detect are of several different classes, corresponding to disconnected manifolds, our approach is unlikely to work. A possible solution would be to introduce multiple initial detection manifolds and apply the TOREAD algorithm to each one separately. As a particular real-world application, we presented here our development of systems for convulsive seizures detection. This explicit example of TOREAD is an operational implementation undergoing field trials in clinical and patient care settings. We use this specific realization, called also the dual detection algorithm, to provide numerical validation and performance assessment of the general concept. In this particular application, the embedding stage consists of decreasing the detection threshold and simultaneously relaxing the duration criteria. There are no absolute rules to determine the boundaries of the embedding in general. The optimal choice may depend on the data variability. It might be interesting to explore a second level of reinforcement and to optimize the embeddingparameters according to the rates of false positive versus negative training detections. This possibility may also be addressed in our future investigations. In this work, instead of collected clinical data, data generated by the Monte Carlo (MC) type of numerical simulations were used. There are several reasons for this. First, validating and exploring the limits of adaptive and machine learning algorithms in clinical conditions may present certain ethical issues. If the alerting system departs from its optimal setting and fails to alert for convulsive events, this may place the patient at risk. Conversely, if an unsuccessful reinforcement trial leads to many false alerts, the caregiving personnel may switch the system off. Second, no amount of real-life examples can provide enough data for all possible scenarios and exceptions that the system may encounter in general. Finally, the use of simulated data avoids the issues of privacy, especially present in the cases of supervised video data validation. Concerning our results from the trials described in Section 3, we again point out the \"asymmetry\" between the performance boundaries of our approach with respect to the sensitivity and the specificity. The detector loses specificity earlier than it loses sensitivity from the decrease in seizure/normal separation.