Epilepsy is a prevalent neurological disorder, which disturbs the lives of millions of people worldwide owing to the onset of abrupt seizures. The forecasting of seizures could help in protecting their lives by alerts or in clinical operations during epilepsy surgeries. The present paper addresses this problem by proposing a deep learning framework for prediction of epileptic seizures using intracranial EEG (iEEG) recordings. This framework performs filtering and segmentation of iEEG signals into 10s, 20s, 30s, 40s, 50s and 60s duration segments. These segments are further resolved into eight distinct spectral bands corresponding to delta, theta, alpha, beta and gamma sub-bands with frequency-domain transformation. Then, mean amplitude and band power features are extracted from each band, which are provided to convolutional neural network (CNN) and long short-term memory network (LSTM) algorithms for classification. The simulation results of the proposed CNN model exhibit higher performance with average accuracy, sensitivity, specificity, AUC and F1 score of 94.74%, 95.8%, 94.46%, 95.13% and 94.75% respectively for iEEG segments of 40s duration. Thus, the performance analysis and comparison with existing literature unveil that the proposed CNN model is an optimal approach for accurate and real-time prediction of epileptic seizures.
相似文献Emerging trend of ubiquitous data access is driving the demand for effective wireless communication connectivity. In essence to this, wireless local area network (WLAN) technology seems to be a reliable and cost effective access for the next-generation wireless ecosystem. But the pivotal challenge for WLAN in the next generation wireless networks is to cater the legions of heterogeneous services with characteristic sets of quality of service requirements. However, the strategies present in the existing literature are not accoutered for the application-agnostic association and are incompetent in handling the enormous WLAN state space. Realising the pitfalls of the existing strategies, a novel software-defined networking enabled artificial intelligence framework has been proposed. The proposed framework implements a novel invalid action reduction scheme and double deep reinforcement learning to guarantee the flow based association in a multi-service WLAN environment. Moreover, it allows the multi-parametric optimisation of the association decision and faster convergence to the stable solution. The analytical results validated through the extensive simulations revealed that the proposed scheme achieves high performance gain in terms of convergence, stability and network utility as compared to the other solutions in the literature.
相似文献This article reports the development of a high bit rate terrestrial free space optics (FSO) transmission link. Spectral-efficient polarisation division multiplexed-quadrature phase-shift keyed signals are used to transmit high bit rate data. Coherent receiver is proposed to ameliorate the demodulator performance under environmental conditions. Also, digital signal processing techniques at the receiver are used to mitigate the adverse channel effects on the information signal. The bit error rate analysis for different environmental conditions is carried out using numerical simulations and the results demonstrate reliable 160 Gbps transmission. The impact of atmospheric scintillation due to turbulent channel conditions on the link performance is also investigated. Further, the transmission performance is compared with previous reported studies which shows that the system demonstrates better achievable range and information bit rate performance. The reported work provides a suitable reference to realize bandwidth-efficient high-capacity FSO links under dynamic weather conditions.
相似文献The investigation of high-speed optical orthogonal-frequency division multiplexed-free space optical system using mode division multiplexing of three spiral phased Hermite Gaussian modes has been discussed in this article. The investigation of the system is reported by considering the effect of beam divergence and weather conditions. Square root module technique has been applied at the receiver side and the system performance has been analyzed by using the enhanced detection technique. Results indicate augmentation in the range limit of the system along with improvements in terms of signal quality.
相似文献