Neonatal seizure detection from EEG using inception ResNetV2 feature extraction and XGBoost optimized with particle swarm optimization.

ABSTRACT: Neonatal seizure detection is crucial for preventing neurological damage, as these seizures are linked to abnormal brain activity. While machine learning and deep learning show promise for EEG analysis, persistent challenges remain, including signal non-stationarity, class imbalance, and limited interpretability. We propose a hybrid deep framework for early neonatal seizure detection. The method uses discrete wavelet transform (DWT) to decompose EEG signals into subbands and short-time Fourier transform (STFT) to create time-frequency spectrograms. Inception-ResNetV2 extracts deep features from these spectrograms, which are then classified by an XGBoost model. This model is fine-tuned offline using particle swarm optimization (PSO) to enhance performance. This hybrid approach tackles key c

SUBMITTER: Nemati N 

PROVIDER: S-EPMC12644789 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature