Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

Abstract

Accurate and adaptive time-frequency representation is essential for analyzing nonstationary signals in critical applications, such as epileptic seizure prediction utilizing electroencephalogram (EEG) data. However, existing deep learning approaches often suffer from two major limitations: the reliance on fixed, nonadaptive feature extraction methods and the lack of model interpretability. To bridge these gaps, we propose Adaptive Discrete Wavelet Convolution (ADWConv), a general-purpose trainable multi-level wavelet decomposition front-end, with validation in an end-to-end seizure prediction framework. ADWConv parameterizes the low-frequency and high-frequency filters within each decomposition level and decouples their kernel shapes from level-specific scaling factors, enabling task-related adaptive weighting of frequency bands. This end-to-end learning is guided by a joint optimization strategy, which incorporates a regularization loss with wavelet priors to ensure the learned filters are both discriminative and physically interpretable. Extensive experiments were conducted on the public CHB-MIT database and the private SH-SDU database. On the CHB-MIT database, the ADWConv-based model attained an event-based sensitivity of 98.65%, while maintaining a false prediction rate (FPR) of only 0.014/h. Comparable results were also demonstrated on the SH-SDU database, attaining 96.53% event-based sensitivity with 0.028/h FPR. Ablation and visualization analyses confirm that learning the wavelet kernels is the primary driver of performance gains and supports task-specific adaptation of discriminative frequency bands. Overall, ADWConv improves the feature-level interpretability of a deep learning framework for seizure prediction while maintaining competitive accuracy and robustness.