RESUMO
Some researchers and clinics have reported that non-drug treatments for Alzheimer disease (AD) such as electrical stimulation, light stimulation, music stimulation, laser stimulation, and transcranial magnetic stimulation may have beneficial treatment effects. Following these findings, in this study, we performed multimodel physical stimulation on APP/PS1 mice using visible light, music with a γ rhythm, and an infrared laser. And the effects of physical stimulation on APP/PS1 mice were evaluated by behavioral analysis, the content of amyloid (Aß40 and Aß42), and NISSL staining of hippocampal tissue slices. The results of subsequent behavioral and tissue analyses showed that the multi-model physical stimulations could relieve APP/PS1 mice's dementia symptoms, such as the behavior ability, the content of Aß40 and Aß42 in the hippocampal tissue suspension, and Nissl staining for hippocampal tissue analyses.
RESUMO
Automatic seizure detection plays a significant role in the diagnosis of epilepsy. This paper presents a novel method based on S-transform and singular value decomposition (SVD) for seizure detection. Primarily, S-transform is performed on EEG signals, and the obtained time-frequency matrix is divided into submatrices. Then, the singular values of each submatrix are extracted using singular value decomposition (SVD). Effective features are constructed by adding the largest singular values in the same frequency band together and fed into Bayesian linear discriminant analysis (BLDA) classifier for decision. Finally, postprocessing is applied to obtain higher sensitivity and lower false detection rate. A total of 183.07 hours of intracranial EEG recordings containing 82 seizure events from 20 patients were used to evaluate the system. The proposed method had a sensitivity of 96.40% and a specificity of 99.01%, with a false detection rate of 0.16/h.