Although existing contrastive learning models utilizing conventional data augmentation achieve modest performance in EEG-based epilepsy analysis, such models risk disrupting EEG semantic consistency during pre-training and compromise the robustness of downstream tasks. To this end, we propose a clinical-knowledge-informed augmentation for contrastive learning on childhood epilepsy analysis (CL4CEA) in this paper. Our method comprises two core components: firstly, inspired by clinical knowledge, we introduce a montage conversion-based plug-and-play augmentation strategy for preserving EEG semantic consistency in contrastive learning. Secondly, a channel-aware adaptive fusion block is employed to integrate features from both temporal and frequency domains, enabling the model to capture discriminative representations of childhood EEG signals, and thus enhancing performance in downstream tasks. Through pre-training on an EEG dataset with more than 1,000 hours childhood EEG recording, and performance fine-tuning, the developed CL4CEA model can achieve promising performance on 3 downstream tasks from 3 medical centers in childhood epilepsy analysis, including onset detection, seizure type classification, and hypoxic-ischaemic encephalopathy (HIE) grading. Comparative experiments with state-of-the-art methods and systematic ablation studies demonstrate the superiority of our proposed model.

Developing a generalizable model across subjects is crucial for the practical application of Electroencephalogram (EEG) based seizure detection model. However, inter-subject variability poses a challenge to the accurate identification of epileptic EEG, and applications often require recalibration and training of the base model using individual labeled data. To overcome this limitation, we propose a cross-subject transfer learning algorithm based on mutual information decomposition driven representation learning (MIDRL). The algorithm first introduces the structured state space sequence model to capture the long-term dependencies of epileptic EEG, and the residual module is used to mine the deep information between channels. Additionally, the mutual information estimation is employed to decompose the middle layer features of the network into domain-invariant representations and domain-specific representations, with the dynamically learnable weight updating mechanism to adaptively balance the learning tasks associated with the two representations. Finally, to address the problem of target samples being easily confused near the classification boundary, the minimum class confusion loss is introduced to reduce the class correlation predicted by the classifier. Experimental results demonstrate that the proposed algorithm effectively retains patterns of seizure region and exhibits strong performance for cross-subject seizure detection.

Audio and Visual are two important visual modalities in video content understanding. However, the absence of one modality may be observed in practical applications due to the real environmental factors, which leads to the information loss. Therefore, audio and visual fusion is focused on using the shared and complementary information between modalities to recover the missing modalities from the available data modalities. In this paper, an Adversarial Hierarchical Variational Auto-Encoder (Adv-HVAE) model is proposed to solve this problem of modality data loss. A multimodal representation is first learned using a hierarchical Variational Autoencoder (VAE) model that enables the generation of missing modal data under any subset of available modalities. Also to obtain a more robust multimodal representation, a feature generation network is utilized to approximate the latent distribution of missing modalities. Finally, the adversarial training network is shown to be effective in improving the data quality generated through the Adv-HVAE framework. Experimental results demonstrate that Adv-HVAE achieves best generation results on two benchmark datasets, avMNIST and Sub-URMP.

Spike ripples in electroencephalogram (EEG) have been considered as a more promising biomarker for epilepsy analysis than using spikes. Almost all existing spike ripples detection concentrates in the high frequency band (80-500Hz) without considering its co-occurrence spikes in low frequency band (1-70Hz). In this paper, a novel EEG-based spike ripples detection algorithm combining both low and high frequency is proposed. For the low frequency band, the energy histogram is derived by the nonlinear energy operator (NLEO). When the average energy exceeds a pre-set threshold, the average duration of the monotonically decreasing segment (ADDS) and the signal filtered by smooth nonlinear energy operator (SNEO) are further calculated. The enhanced K-means algorithm is used for candidate spikes selection. For the high frequency band, the peak distribution is generated to select high frequency oscillations (HFOs). Then, 21 significant features are extracted from HFOs and a quadratic kernel support vector machine (SVM) is trained for candidate ripples selection. If both the candidate ripple and spike are in the same frame, it is considered as a spike ripple. Finally, feature selection based on Max-Relevance and Min-Redundancy (mRMR) is studied to enhance overall performance. The proposed algorithm is compared with two related methods on EEGs of 6 subjects, which can achieve a convincing performance with an average of 91.35% precision, 93.88% recall, 92.56% F1score, and 96.62% BA, respectively.

Video recording has been extensively studied for seizure detection and classification due to its convenience of collection. Most existing vision-based studies generally followed a two-stage scheme of first object detection and then action recognition to detect seizures for better real-world application. However, all of these approaches are two-stage not end-to-end, which may make the model locally optimal. Besides, the object detection algorithms applied in existing methods often suffer heavy computational burden, leading to slow inference speed and high hardware support. All these issues can seriously hinder the practical application and deployment of the model. Therefore, we proposed a novel end-to-end model in this paper, which could simultaneously achieve patient detection and seizure detection. The amount of parameters and computations in the conventional object detection branch can be reduced by innovatively exploring the idea of using a spatial attention module instead of object detection networks for patient detection. However, based on a toy example, we found that relying solely on a spatial attention module without guidance is not reliable, despite its high performance in seizure detection. Therefore, a guided spatial attention module (GSAM) is proposed in this paper. An extra regression loss function is used for guiding the learning of GSAM. In addition, the hard shrinkage operation is applied on the generated spatial attention heatmap (SAH), making the generated SAH closer to the real object detection with a faster model convergence. Besides, a temporal attention module is used to reduce the amount of parameters and computations, as well as to fuse the temporal information well. Experiments show that our method has less parameters and faster running speed than competing methods, yet better performance on seizure detection. The proposed GSAM with high performance could well replace the object detection algorithm for patient detection.

Eye blink is one of the most common artifacts in electroencephalogram (EEG) and significantly affects the performance of the EEG related applications, such as epilepsy recognition, spike detection, encephalitis diagnosis, etc. To achieve an accurate and efficient eye blink detection, a novel unsupervised learning algorithm based on a hybrid thresholding followed with a Gaussian mixture model (GMM) is presented in this paper. The EEG signal is priliminarily screened by a cascaded thresholding method built on the distributions of signal amplitude, amplitude displacement, as well as the cross channel correlation. Then, the channel correlation of the two frontal electrodes (FP1, FP2), the fractal dimension, and the mean of amplitude difference between FP1 and FP2, are extracted to characterize the filtered EEGs. The GMM trained on these features is applied for the eye blink detection. The performance of the proposed algorithm is studied on two EEG datasets collected by the Temple University Hospital (TUH) and the Children's Hospital, Zhejiang University School of Medicine (CHZU), where the datasets are recorded from epilepsy and encephalitis patients, and contain a lot of eye blink artifacts. Experimental results show that the proposed algorithm can achieve the highest detection precision and F1 score over the state-of-the-art methods.

Evidence filters have found a wide range of applications in military surveillance, environment monitoring, homeland security, etc. Designing evidence filters is challenging because of the nonnegative constraint on its impulse response. A new algorithm is proposed in this paper for the magnitude design of evidence filters with prescribed transition rolloff. It uses a bisection method to convert the design problem into a series of constrained minimax magnitude error subproblems, which are then solved by using an iterative constrained minimax elliptic error (ICMEE) method. Simulation examples demonstrate the effectiveness of the proposed design method for evidence filters.