MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordings

• URL: http://arxiv.org/abs/2505.17972v1
• Date: Fri, 23 May 2025 14:40:50 GMT
• Title: MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordings
• Authors: Kazi Mahmudul Hassan, Xuyang Zhao, Hidenori Sugano, Toshihisa Tanaka,
• Abstract summary: We propose a novel end-to-end model, ''Multiresolutional EEGWaveNet (MR-EEGWaveNet),'' which efficiently distinguishes seizure events from background electroencephalogram (EEG) artifacts/noise.<n>The model has three modules: convolution, feature extraction, and predictor.<n>The proposed MR-EEGWaveNet significantly outperformed the conventional non-multiresolution approach.
• Score: 7.9595266728435545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature engineering for generalized seizure detection models remains a significant challenge. Recently proposed models show variable performance depending on the training data and remain ineffective at accurately distinguishing artifacts from seizure data. In this study, we propose a novel end-to-end model, ''Multiresolutional EEGWaveNet (MR-EEGWaveNet),'' which efficiently distinguishes seizure events from background electroencephalogram (EEG) and artifacts/noise by capturing both temporal dependencies across different time frames and spatial relationships between channels. The model has three modules: convolution, feature extraction, and predictor. The convolution module extracts features through depth-wise and spatio-temporal convolution. The feature extraction module individually reduces the feature dimension extracted from EEG segments and their sub-segments. Subsequently, the extracted features are concatenated into a single vector for classification using a fully connected classifier called the predictor module. In addition, an anomaly score-based post-classification processing technique was introduced to reduce the false-positive rates of the model. Experimental results were reported and analyzed using different parameter settings and datasets (Siena (public) and Juntendo (private)). The proposed MR-EEGWaveNet significantly outperformed the conventional non-multiresolution approach, improving the F1 scores from 0.177 to 0.336 on Siena and 0.327 to 0.488 on Juntendo, with precision gains of 15.9% and 20.62%, respectively.

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