Harmonizing complexity and efficiency: Helix Fusion HarmonyNet's breakthrough in EEG-based postoperative delirium recognition.

Postoperative delirium (POD) is a common perioperative complication involving central nervous system dysfunction, particularly among critically ill and elderly patients, yet its rapid and objective detection remains challenging in clinical settings. To develop and evaluate a lightweight, interpretable electroencephalography (EEG)-based deep learning framework for POD detection that remains accurate under sparse-electrode acquisition and practical for bedside or portable deployment. Approach: We propose Helix Fusion HarmonyNet (HFHN), a transform-domain architecture that explicitly separates EEG representations into amplitude and phase pathways. HFHN integrates multi-head self-attention, convolutional local feature extraction, learnable sinusoidal positional encoding, and controlled cross-pathway fusion to jointly model spectral intensity, temporal synchrony, local patterns, and long-range dependencies. The framework was evaluated against representative CNN-, Transformer-, and EEG-specific deep learning models across signal domains, fusion strategies, electrode configurations, ablation settings, and deployment-oriented efficiency tests. Main results: In Fourier-domain POD detection, HFHN achieved 100% accuracy, precision, specificity, F1-score, and sensitivity. Ablation analysis confirmed the importance of amplitude-phase interaction, as removing cross-pathway fusion reduced accuracy from 100% to 95.38%. Under sparse-channel conditions, HFHN maintained 99.42% accuracy and a 99.33% F1-score with 8 electrodes, and 94.08% accuracy and a 93.26% F1-score with only 4 high-attention electrodes. Reducing the number of electrodes from 60 to 4 decreased FLOPs from 16.95 G to 0.99 G, while inference latency remained below 3.09 ms. Significance: HFHN provides an accurate, interpretable, and computationally efficient solution for EEG-based POD detection. Its robustness under sparse-channel acquisition and low-latency performance support potential integration into portable or bedside EEG systems for rapid postoperative monitoring in wards and intensive care units.