Application of self-organizing map to identify nocturnal epileptic seizures



An approach to find the best patient-specific seizure detection system for nocturnal frontal lobe epilepsy (NFLE) is proposed. Data of several patients affected by NFLE, extracted from the European Epilepsy Database, have been used for this study. As every patient possesses different physiological characteristics, several simulations were performed in order to find the best features to be extracted from the best number of electroencephalogram (EEG) channels and to be inputted to two-dimensional self-organizing maps (SOM). The proposed method allows the definition of maps capable of presenting meaningful information on the actual brain state, revealing the mapping potential of clustering the data coming from seizure and non-seizure patterns; moreover, it also suggests to use SOM for early seizure detection. In fact, the temporal sequence of the patterns in an EEG recording can be projected on the SOM, obtaining a trajectory that describes the dynamics of the brain state as captured by the EEG. The analysis of this trajectory allows one to detect the seizure event, highlighting the possibility to promote therapies aimed at rapid and targeted disarming the seizures. A further contribution of the paper is to propose a methodology to be followed to customize the prediction model to a new patient. The performances of the proposed approach, in terms of sensitivity and specificity, have been compared with those of other well-established detection models proving it is a good alternative to early and accurately detect nocturnal frontal lobe epilepsy seizures.


Neural Computing and Applications 2019


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