In this study, a novel real-time seizure prediction algorithm is introduced to predict epileptic seizures. The proposed algorithm is expected to be applicable in a noninvasive neuromodulator. As a model of the epileptogenic zone, a small-world network of Huber-Braun neurons was built up. To assess the effects of noninvasive stimulation techniques, such as transcranial magnetic stimulation, this network was modified, and the magneto-motive forces and the electromagnetically induced currents were further applied on the network. Comprehensive investigations of the electroencephalograms of epilepsy patients have suggested that some chaotic mechanisms generate the seizures. Hence, chaos and bifurcation theory was applied, and the induced current was considered as the bifurcation parameter. The bifurcation diagram of the 'inter-spike' intervals of the mean voltage of the small world network was obtained. The precise time at which the bifurcation took place was subsequently considered as the time of the seizure onset. Comparisons of the bifurcation diagrams obtained from the patients’ electroencephalographs showed that the proposed network model could reasonably represent the actual neuronal networks of the epileptogenic zone. A dataset of the electroencephalographs of epilepsy patients and normal volunteers from an epilepsy center in Germany was used to validate the prediction algorithm. The simulation results show that the proposed algorithm has a significant capability to predict the precise occurrence of seizures and the achieved sensitivity, accuracy, and specificity of this approach were remarkably higher than those reported in previous studies.
Original scientific paper Thermally sensitive neurons represent a bursting-spiking activity that is indicated by rapid repetitive spiking trains of action potentials pursued by dormant periods. Synchronization of such behavior in a network of coupled spiking neurons such as the epileptogenic zone in the brain may cause some neurological disorders such as epileptic seizures. This paper introduces a new approach for predicting the seizure onset in a model of an epileptic neuron. The parameters which are used for simulations have been selected in such a way that they would be potentially applicable in the non-invasive brain stimulation approaches such as repetitive transcranial magnetic stimulation (rTMS). In this regard, a modified Huber-Braun model of a thermally sensitive neuron exposed to external rTMS-induced voltages is presented. Applying the chaos theory, the bifurcation diagram of a modified Huber-Braun model with a new bifurcation parameter is used to estimate the time at which the bifurcation takes place whereby allowing a more accurate prediction of the seizure onset based on the modified model. Keywords: bifurcation theory; Huber-Braun model; seizure Istraživanje novog parametra bifurkacije u modificiranom Huber-Braun modeluIzvorni znanstveni članak Toplinsko osjetljivi neuroni predstavljaju pulsirajuće-prskajuću aktivnost predskazanu brzim ponavljajućim iznenadnim razvojem događanja praćenim periodima spavanja. Sinhronizacija takvog ponašanja u mreži spojenih prskajućih neurona kao što je epileptogena zona u mozgu može dovesti do neuroloških poremećaja poput epileptičkih napada. U radu se predlaže novi pristup predviđanju napada modelom epileptičkog neurona. Parametri upotrebljeni u simulaciji izabrani su tako da se mogu potencijalno primijeniti u pristupima neinvazivnim stimulacijama mozga kao što je ponavljajuća transkranijalna magnetska stimulacija (rTMS). U tom pogledu, predstavlja se modificirani Huber-Braun model termalno osjetljivog neurona izloženog rTMS-induciranoj voltaži. Primjenjujući teoriju kaosa dijagram bifurkacije modificiranog Huber-Braun modela s novim parametrom bifurkacije primijenjen je za procjenu vremena u kojem dolazi do bifurkacije čime se omogućuje točnije predviđanje početka napada na temelju modificiranog modela.
In this study, a novel real-time seizure prediction algorithm is introduced to predict epileptic seizures. The proposed algorithm is expected to be applicable in a noninvasive neuromodulator. As a model of the epileptogenic zone, a small-world network of Huber-Braun neurons was built up. To assess the effects of noninvasive stimulation techniques, such as transcranial magnetic stimulation, this network was modified, and the magneto-motive forces and the electromagnetically induced currents were further applied on the network. Comprehensive investigations of the electroencephalograms of epilepsy patients have suggested that some chaotic mechanisms generate the seizures. Hence, chaos and bifurcation theory was applied, and the induced current was considered as the bifurcation parameter. The bifurcation diagram of the 'inter-spike' intervals of the mean voltage of the small world network was obtained. The precise time at which the bifurcation took place was subsequently considered as the time of the seizure onset. Comparisons of the bifurcation diagrams obtained from the patients’ electroencephalographs showed that the proposed network model could reasonably represent the actual neuronal networks of the epileptogenic zone. A dataset of the electroencephalographs of epilepsy patients and normal volunteers from an epilepsy center in Germany was used to validate the prediction algorithm. The simulation results show that the proposed algorithm has a significant capability to predict the precise occurrence of seizures and the achieved sensitivity, accuracy, and specificity of this approach were remarkably higher than those reported in previous studies.
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