A novel personalized wearable healthcare framework: exploring EEG patterns for depression monitoring

Abstract

Real-time health monitoring via wearable devices has become increasingly essential for personalized health management. However, existing physiological signal processing methods, particularly for EEG data, focus primarily on frequency domain features, which can lead to lower monitoring accuracy. To address these limitations, we propose a novel personalized health monitoring framework that integrates both frequency and spatio-temporal characteristics of physiological signals. Within this framework, we further propose a deep learning model called the Spectral-Spatial Attention and Frequency Feature Fusion Network (SSAFNet). SSAFNet consists of three key modules: cross-frequency feature extraction, spatio-temporal feature extraction, and feature fusion, which together analyze frequency and spatio-temporal physiological data, enabling more precise real-time monitoring and improving the effectiveness of health management. Using this framework, we conducted several experiments to identify key patterns in EEG signals that effectively reflect individual health conditions and compared them with traditional health monitoring methods. The results demonstrate significant differences in EEG patterns across individuals, and the proposed framework outperforms existing methods in personalized health monitoring, showing its effectiveness and potential for widespread applications.

Graphical abstract

Keywords

personalized health monitoring / EEG / wearable devices / real-time health detection / depression

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