Effecting 50 million people worldwide, APLFC is one of the most common neurological disorders. Patients with APLFC have chronic seizures, which are episodes in which the subject experiences involuntary movement and sometimes even lose this consciousness. It has been estimated that 70% of affected people could live a life without seizures if treated adequately. The unpredictability and frequency of seizure and set sometimes once per week forces strong socio-economic constraints on the life of patients and that of their relatives. By monitoring patients at risk, doctors can make meaningful therapeutic decisions. However, continuous monitoring of patients creates vast amounts of data and observation is extremely time-consuming. Researchers have developed methods to automatically detect seizure, relying on EEG data. However, there can be artifact that trigger detection of force positives. For example, petting neonates can trigger an EEG signal that resembles that of a seizure and mislead the automated methods. In this presentation, I will go over a recent work which aims to identify petting from video recordings of neonates with APLFC and eventually discard force-positive detections. In this work, we have 35 videos that have been labeled with the beginning of petting and the end of petting. These are video recordings of neonates from Lucid Packard's Hospital from Stanford. To optimize and evaluate our models, we extracted 5 second segments from these videos. Each of these segments is consequently labeled as petting or no petting depending on whether it falls within the annotated petting time. Our objectives were to classify the segments into petting or no petting. We didn't use directly the video data but instead computed a frame-to-frame correlation matrix for each frame of the segment. The idea is that petting is a repeated motion that could show some periodicity in the cross-correlation matrix. We then input this cross-correlation matrix to a neural network. We use a very simple neural network design that resembles that of a resonating network which is a standard neural network texture. We use add-a-delta optimizer. We did flipping of the correlation matrix for data augmentation and we used a weighted binary cross-entropy function for the loss function. In this table, you can see the results on the validation set. It is interesting to note that thanks to the I recall, we will be able to identify most of the petting samples. However, there will be some force alarms. To verify that the neural network identified the correct features in the cross-correlation matrix and not some randomly correlated artifacts, we computed the attention of the network. The attention of the network highlights in the image, areas that were discriminative for the network to carry on their prediction. We notice indeed that the areas with this periodic pattern in the cross-correlation matrix are picked up by the network while the rest of the correlation matrix is ignored. This confirms our hypothesis that the network uses the periodicity in the cross-correlation matrix to be able to accurately detect the petting from the video data. In future work, we will run the EEG-based seizure detection model on the portion of the data where we know that there is petting. And we will see whether there are force-positive detection in those cases. If the seizure EEG model detects a seizure while the petting and a petting model detects a petting when the petting will be able to discard that detection as being a force-positive corresponding to petting and improve the performance of our seizure detection models. Eventually, we will also compute the cross-correlation matrix on shorter segments, for example, two seconds, to have a more precise definition of the petting time. Thank you very much for your attention and I wish you a pleasant day.