Browsing by Author "Chazarra, Pedro"
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Item Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity.(Computer Methods and Programs in Biomedicine, 2023) Nogales Moyano, Alberto; García Tejedor, Álvaro José; Chazarra, Pedro; Ugalde-Canitrot, ArturoBackground and objective Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of brain connectivity using artificial intelligence and network analysis techniques since their study requires large amounts of data over large spatial and temporal scales. For example, to discriminate states that would otherwise be indistinguishable from the human eye. This paper aims to identify the different brain states that appear concerning the intriguing seizure type of epileptic spasms. Once these states have been differentiated, an attempt is made to understand their corresponding brain activity. Methods The representation of brain connectivity can be done by graphing the topology and intensity of brain activations. Graph images from different instants within and outside the actual seizure are used as input to a deep learning model for classification purposes. This work uses convolutional neural networks to discriminate the different states of the epileptic brain based on the appearance of these graphs at different times. Next, we apply several graph metrics as an aid to interpret what happens in the brain regions during and around the seizure. Results Results show that the model consistently finds distinctive brain states in children with epilepsy with focal onset epileptic spasms that are indistinguishable under the expert visual inspection of EEG traces. Furthermore, differences are found in brain connectivity and network measures in each of the different states. Conclusions Computer-assisted discrimination using this model can detect subtle differences in the various brain states of children with epileptic spasms. The research reveals previously undisclosed information regarding brain connectivity and networks, allowing for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. From our data, we speculate that the prefrontal, premotor, and motor cortices could be more involved in a hypersynchronized state occurring in the few seconds immediately preceding the visually evident EEG and clinical ictal features of the first spasm in a cluster. On the other hand, a disconnection in centro-parietal areas seems a relevant feature in the predisposition and repetitive generation of epileptic spasms within clusters.Item EEGraph: An open-source Python library for modeling electroencephalograms using graphs.(Neurocomputing, 2023) Maitin, Ana M.; Nogales Moyano, Alberto; Chazarra, Pedro; García Tejedor, Álvaro JoséBackground and objective Connectivity studies make it possible to identify alterations in brain connections and to associate these pathologies with different neurological disorders. However, a clinical test is necessary to obtain information about the state of the brain. Electroencephalograms (EEGs) provide this information in addition to being tests with other benefits for the patient (non-invasive, low-cost, high reproducibility). Graph theory can be used to represent both the anatomical and functional connections of the brain by means of connectivity measures. The procedure of transforming an EEG into a graph can be slightly tedious for researchers, especially when implementing different connectivity measures. Methods The open-source Python library EEGraph automatically performs the modeling of an EEG through a graph, providing its matrix and visual representation. It recognizes various EEG input formats, identifying the number of electrodes and the location of each electrode in the brain. Moreover, it allows the user to choose from 12 connectivity measures to produce the graph from the EEG, with great flexibility to define specific parameters to adapt them to each study, including EEG time-windows segmentation and separation in frequency bands. Results The EEGraph library is developed as a tool, for researchers and clinical specialists in the field of neuroscience, that provides direct information on the connectivity of the brain from electroencephalography signals. Its documentation and source code are available at https://github.com/ufvceiec/EEGRAPH. It can be installed from the Python Package Index using pip install EEGRAPH. Conclusions The EEGraph library was built aiming to facilitate the development of connectivity studies based on the modeling of electroencephalography tests through graphs. It includes a wide range of connectivity measures, which, together with the multiple output options, make EEGraph an easy to use and powerful tool with direct applications in both the clinical and neuroscience research fields.