## 1 Introduction
Applying these ideas to EEG analysis suggests potential for wavelet-domain sparsity modeling, adaptive recovery algorithms, and improved interpretability in wearable seizure-monitoring systems. To enhance novelty, related biomedical research can integrate these signal-model-driven, sparsity-aware techniques as alternatives to standard deep fusion models. The main aim of [PERSON] et al. (2018) is to address angle estimation in arbitrary-manifold array bistatic MIMO radar and propose a joint two-dimensional direction-of-departure (2D-DOD) and two-dimensional direction-of-arrival (2D-DOA) estimation algorithm assisted by IRS. Finally, [PERSON] et al. [PERSON] et al. (2018) developed a real-time, non-patient-specific algorithm for seizure detection and localization. Utilizing spectral and coherence-based features, the method achieved a median detection latency of 8 seconds. While computationally efficient and generalizable, its accuracy is reduced for brief seizures and requires robust artifact removal for optimal performance. Because adder networks use the t-norm distance as the similarity measure between input features and filters, the network's gradient behavior changes. To ensure the proper convergence of AddNet-SCL, we introduce an adaptive learning rate strategy. Building upon these innovations, this paper presents a novel epileptic seizure prediction method that integrates DWT-based time-frequency analysis with advanced feature extraction tech niques--including entropy, power, frequency, and amplitude--paired with deep learning using Fourier neural networks (FNNs). Evaluated on the well-established CHB-MIT EEG dataset, the proposed approach achieves high prediction accuracy with a zero false positive rate, marking a significant advancement over existing methods. The rest of this paper is structured as follows:  
* Section 2 describes the preprocessing steps, including discrete wavelet transform and feature selection methods. * Section 3 details the deep learning approach using Fourier neural networks. * Section 4 details the experimental results and provides a comparative analysis of the proposed method with current approaches. * The originality and primary contributions of this paper can be summarized as follows:
* Sparse signal representation in the wavelet domain: Unlike traditional approaches that rely on dense representations followed by deep fusion techniques (e.g., DWT combined with LSTM), our method explicitly models EEG signals as sparse in the wavelet domain. This formulation draws a parallel to the sparsity observed in the delay/Doppler domain of FBMC channels, allowing for more efficient signal analysis. * Lightweight adaptive recovery framework: We introduce an adaptive sparse recovery module inspired by pursuit-based algorithms, specifically designed to handle the non-stationary and noise-prone nature of EEG signals. While conceptually similar to sparse channel estimation techniques in communication systems, this module is customized for biomedical signal processing applications. * Optimized for real-time applications: The proposed model significantly reduces computational overhead, making it suitable for deployment on low-power, real-time platforms such as wearable seizure detection devices. This aligns with similar efficiency goals pursued in MIMO-FBMC systems for industrial IoT environments. * Improved interpretability and clinical relevance: The sparse wavelet coefficients produced by our model enhance the transparency and physiological interpretability of EEG signals. This feature not only supports clinical decision making but also mirrors the interpretability advantages seen in sparse communication channel estimation.