Accurate seizure detection from multimodal physiological signals is a key clinical imperative for improving patient care and diagnostic outcomes. However, conventional methods often fail to adequately model the complex, higher-order relationships within and across signal modalities, limiting performance. To address this limitation, we propose the Multi-dimensional Hypergraph Fusion Network (MHFN), a hypergraph-based multimodal fusion framework that explicitly models these intricate correlations. MHFN constructs three complementary hypergraphs: (1) an intra-modal hypergraph based on cosine similarity to capture fine-grained feature dependencies; (2) an inter-modal hypergraph that represents synergistic interactions across modalities; and (3) a temporal hypergraph incorporating dynamic time warping (DTW) to model evolutionary signal dynamics. By applying hypergraph convolutional networks (HGCN) to these structures, MHFN learns discriminative feature embeddings that are fused to generate an initial prediction. The clinical utility of this output is enhanced by a temporal correction pipeline, which smooths predictions and suppresses short-duration artifacts to produce coherent event-level classification. Comprehensive evaluations on clinical datasets demonstrate state-of-the-art performance, achieving an accuracy of 96.87%, precision of 98.06%, sensitivity of 96.17%, and F1-Score of 97.19%. Furthermore, we validate deployment feasibility on a Raspberry Pi 4B edge node, demonstrating low inference latency of 0.49 s and efficient power consumption (2.82 W). These results confirm that MHFN offers a robust, high-performance, and deployable paradigm for ambulatory clinical monitoring.

Seizure warning system (SWS) based on electroencephalography (EEG) is a prominent research focus in the internet of medical things (IoMT). An effective SWS enables epilepsy patients to proactively implement interventions before seizures. Effective feature representation of EEG reduces communication load from IoMT processing devices to cloud servers and enhances subsequent machine/deep learning classification performance. This paper proposes a seizure prediction system based on multidimensional attention entropy (MAE) and an improved binary mantis search algorithm (IBMSA). First, the MAE is calculated at the edge computing gateway by extracting attention entropy (AE) from each subband of single channel (AE-ESSC). Based on this, the algorithm further computes the AE of single channel with multiple subbands (AE-SCMS), the AE of single subband with multiple channels (AE-SSMC) and the AE of the original signal in single channel (AE-OSSC). Second, the IBMSA which is deployed on cloud servers uses a composite S-shaped and V-shaped function (CSVF) and dynamic stage selection strategy (DSSS) to find the optimal feature subset. The proposed algorithm is evaluated on data from the CHB-MIT dataset via leave-one-out cross-validation, with experimental results demonstrating an average sensitivity of 94.74% and a false prediction rate of 0.045 per hour. Additionally, edge deployment testing on Raspberry Pi 4 verifies the lightweight feature of the proposed system.

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.

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.

Seizure detection is traditionally done using video/electroencephalography monitoring, but for out-of-hospital patients, this method is costly. In recent years, portable device to detect seizures gains attention. In this paper, multimodal signals collected by portable devices are studied, and a seizure detection algorithm is proposed based on adaptive multi-bit local differential ternary pattern (MLDTP). This algorithm is used for detecting seizure period and inter-seizure period. Traditional local binary pattern has certain limitations in describing one-dimensional time series signals. It can only describe two types of structures in signals: Rising structure and falling structure, making the signal patterns overly monotonous and not conducive to classification tasks. To address this issue, this paper introduces two additional structures, slowly rising structure and slowly falling structure, into the signal description using MLDTP method. This method constructs multi-bit neighboring relationships of the signals, and adaptively selects the optimal MLDTP parameters for different modalities using the Archimedes optimization algorithm (AOA). Additionally, this paper extensively discusses a multimodal signal fusion strategy, mapping features of different modal signals to the same feature space through the MLDTP algorithm to achieve information complementarity. Long-term recorded data from 18 patients were collected using the wearable device Biovital P1, with 13 cases from the Children’s Hospital affiliated with Children’s Hospital, Zhejiang University School of Medicine, and 5 cases from the fourth Affiliated Hospital of Anhui Medical University. The dataset underwent five-fold cross-validation, resulting in average accuracy, precision, sensitivity and F1 score of 96.81%, 98.55%, 95.24% and 96.87%, respectively.

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.