Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM.

Intelligent recognition methods for classifying non-stationary and non-invasive epileptic diagnoses are essential tools in neurological research. Electroencephalogram (EEG) signals exhibit better temporal characteristics in the detection of epilepsy compared to radiation medical images like computed tomography (CT) and magnetic resonance imaging (MRI), as they provide real-time insights into the disease' condition. While classical machine learning methods have been used for epilepsy EEG classification, they still often require manual parameter adjustments. Previous studies primarily focused on binary epilepsy recognition (epilepsy vs. healthy subjects) rather than as ternary status recognition (continuous epilepsy vs. intermittent epilepsy vs. healthy subjects). In this study, we propose a novel deep learning method that combines a convolution neural network (CNN) with a long short-term memory (LSTM) network for multi-class classification including both binary and ternary tasks, using a publicly available benchmark database on epilepsy EEGs. The hybrid CNN-LSTM automatically acquires knowledge without the need for extra pre-processing or manual intervention. Besides, the joint network method benefits from memory function and stronger feature extraction ability. Our proposed hybrid CNN-LSTM achieves state-of-the-art performance in ternary classification, outperforming classical machine learning and the latest deep learning models. For the three-class classification, in the method achieves an accuracy, specificity, sensitivity, and ROC of 98%, 97.4, 98.3% and 96.8%, respectively. In binary classification, the method achieves better results, with ACC of 100%, 100%, and 99.8%, respectively. Our dual stream spatiotemporal hybrid network demonstrates superior performance compared to other methods. Notably, it eliminates the need for manual operations, making it more efficient for doctors to diagnose during the clinical process and alleviating the workload of neurologists.

Ultrabroad Brillouin instantaneous frequency measurement with a designed linear system response.

We demonstrate an ultrabroad instantaneous frequency measurement (IFM) based on stimulated Brillouin scattering (SBS) with a designed linear system response. The linear system response is found to be the key factor that broadens the system bandwidth. It is realized by designing the sweeping method of frequency and amplitude of the local pump signal. With the improvement of linearity, the measurement error is decreased and the bandwidth of the SBS-based IFM is consequently enlarged. A Costas frequency modulated signal with an instantaneous bandwidth of 10.5 GHz is successfully measured by the designed system response. Further optimization of pump signal's characteristics extends the system bandwidth to 14.5 GHz. The measurement error of a linear frequency modulated (LFM) signal ranging from 6 GHz to 20.5 GHz is less than 1% of the instantaneous bandwidth.