## 1 Introduction
Achieving a sensitivity of 98.5% and a low false-positive rate of 0.12/h, the model is well-suited for clinical use. Nonetheless, its generalizability and high resource requirements remain concerns. In the context of IoMT applications, [PERSON] et al. [9] proposed a hybrid model combining CNN, LSTM, and GRU layers, incorporating SMOTE for class imbalance and SHAP for model explainability. Designed for real-time deployment, the model achieved 99.13% accuracy. Despite its strengths in scalability and interpretability, limitations include high computational costs and privacy concerns in IoMT ecosystems. [PERSON] et al. [10] presented TOREADA, an adaptive seizure detection algorithm based on topological reinforcement learning. It dynamically adjusts detection parameters using real-time EEG embeddings. While simulation results highlight its robustness, the lack of clinical validation restrictsits practical application. [PERSON] et al. [PERSON] et al. (2018) analyzed variations in fractal dimensions during seizures using CHB-MIT data, applying both [PERSON]'s and roughness scaling extraction (RSE) methods. Their study clarified discrepancies in prior findings and emphasized the influence of preprocessing. However, its clinical relevance remains limited due to the absence of predictive modeling. The study by [PERSON] et al. [PERSON] et al. (2018) introduced HViT, a hybrid vision Transformer architecture for EEG-based seizure prediction that integrates convolutional neural networks (CNNs) to enhance local feature extraction and mitigate top-level gradient vanishing typically seen in pure transformers. On top of this, they incorporate data uncertainty learning (DUL), modeling each EEG embedding as a Gaussian or Laplacian distribution--where the mean is learned by the HViT and a parallel branch predicts variance or scale--thereby increasing robustness to noisy EEG signal representations. Additionally, a learnable constraint coefficient in the loss function is tailored per patient, and a simple uncertainty quantification method is applied to alarms using a k-of-n continuous prediction strategy. Evaluated on two public epilepsy datasets, their approach demonstrates a superior performance, highlighting the combined benefits of CNN-augmented transformers and uncertainty modeling in improving seizure prediction accuracy  
[PERSON] et al. [PERSON] et al. (2018) presented a novel EEG-based seizure prediction model that fuses a multidimensional Transformer encoder with LSTM and GRU recurrent neural networks to simultaneously capture both global and local temporal-frequency features from EEG spectrograms. The approach first applies short-time Fourier transform to extract time-frequency representations, then separately processes spectral and temporal dimensions via dual Transformer encoders. Outputs are further refined through LSTM and GRU branches, whose features are gated and fused for classification. Tested on two public datasets--CHB-MIT and Bonn--the model achieved an outstanding performance: on CHB-MIT, it averaged 98.24 sensitivity and 97.27 specificity; on Bonn, it reached 99 accuracy in binary and 98 in three-class classification. [PERSON] et al. [PERSON] et al. (2018) proposed a lightweight, compressive sensing-based approach to channel estimation in MIMO-FBMC systems, addressing challenges such as intrinsic imaginary interference and low-resource deployment in industrial IoT settings. By modeling the channel as sparse in the delay/Doppler domain, the authors design a low-complexity estimator that avoids dense pilot structures and heavy computations, achieving a comparable or better MSE than classical methods, especially in noisy environments. This methodology offers strong cross-domain relevance to biomedical applications like seizure prediction, where similar constraints--such as real-time processing, noise robustness, and low-power operation--exist.