Carbon fibers have been prepared by thermal decomposition of benzene at temperatures 1050–1080°C. Structural change with stepwise heat treatments up to 3000°C has been studied by X-ray and selected-area electron diffraction. The as-prepared fiber as well as the 1400°C-treated is basically of turbostratic structure, but has a preferred orientation of aromatic carbon planes more or less parallel to the fiber axis. By heat treatment around 2000°C, the preferred orientation is improved enormously; the carbon planes become almost completely parallel to the fiber axis, while the stacking order is still turbostratic. A three dimensional graphite structure is developed by heat treatment above 2400°C, which has qualitatively a similar behavior to that of graphitizing carbon. The fiber heat-treated at 3000°C consists of graphite layers concentrically surrounding the fiber axis.
Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with a poor long-term outcome. Early detection and treatment may improve prognosis. This paper aims to develop an optimal set of parameters and a comprehensive scheme for patient-independent multichannel EEG-based neonatal seizure detection. We employed a dataset containing 411 neonatal seizures. The dataset consists of multi-channel EEG recordings with a mean duration of 14.8 h from 17 neonatal patients. Early-integration and late-integration classifier architectures were considered for the combination of information across EEG channels. Three classifier models based on linear discriminants, quadratic discriminants and regularized discriminants were employed. Furthermore, the effect of electrode montage was considered. The best performing seizure detection system was found to be an early integration configuration employing a regularized discriminant classifier model. A referential EEG montage was found to outperform the more standard bipolar electrode montage for automated neonatal seizure detection. A cross-fold validation estimate of the classifier performance for the best performing system yielded 81.03% of seizures correctly detected with a false detection rate of 3.82%. With post-processing, the false detection rate was reduced to 1.30% with 59.49% of seizures correctly detected. These results represent a comprehensive illustration that robust reliable patient-independent neonatal seizure detection is possible using multi-channel EEG.
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