Management of frequent epileptic seizures in febrile infection-related epilepsy (FIRES) is often challenging. FIRES is an uncommon disease condition. Children with FIRES develop refractory epilepsy with severe cognitive deficits that affect the function of the temporal and frontal lobes. However, better seizure control during the acute stage of FIRES could protect against injury to the nervous system. Ketogenic diet (KD) can effectively resolve super-refractory status epilepticus (SRSE) in the acute phase and improve the prognosis of FIRES. We present the case of a previously healthy 3-year-old male with newonset status epilepticus (SE) admitted to the paediatric intensive care unit for 55 days. Despite treatment with multiple anti-epileptic agents in addition to IV anaesthetics, the patient remained in SRSE and continued to have generalised epileptic activity on electroencephalography (EEG). KD therapy was initiated on the 14th day of the onset, and the patient achieved complete neurological recovery following the KD. Throughout the remainder of admission, the patient was successfully weaned off the ventilator, tolerated oral meals, and worked with occupational and physical therapists to return to his baseline functional status. The convulsions were well controlled after discharge. We discuss the treatment strategies for FIRES and highlight the role of KD therapy in the acute phase to control disease progression and improve the prognosis, and early diagnosis of FIRES and early initiation of KD therapy combined with anti-epileptic drugs (AEDs) could improve the prognosis.
A target detection method based on polarimetric multi-domain feature fusion is proposed in this paper to improve the detection performance of slow small targets on the sea. Firstly, a complex symmetric matrix was established based on the Pauli scattering vector. On the basis of an analysis on the matrix, the Takagi decomposition method was adopted to extract the normalized polarimetric maximum eigenvalue to characterize the echo signal. Secondly, a real symmetric Hurst exponent matrix was constructed by processing the echo signal of the polarimetric radar, and the normalized polarimetric Hurst exponent was extracted by the eigenvalue decomposition method. Thirdly, the normalized polarimetric Doppler peak height was extracted through the Doppler peak height algorithm. Finally, by fusing multi-domain features, a false alarm controllable detector was constructed through the convex hull algorithm. The results of experimental analysis on the measured datasets indicate that when the parameters are the same, compared with the traditional detection methods based on polarimetric features, the proposed method presents better robustness in the case of short observation time and low signal to clutter rate.
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