基于ECG的疲劳检测在不同疲劳诱导范式中的泛化研究：使用深度学习的方法

Introduction Fatigue and sleepiness can arise under different experimental and behavioral conditions, yet ECG-based detection models are often evaluated only within the dataset used for development. This study investigated the participant-independent and cross-dataset performance of two ECG-derived approaches: a 1D CNN based on six HRV features and a dual-input CNN-BiLSTM combining RR interval sequences with statistical HRV descriptors. Methods Models were developed using the DROZY database, in which sleepiness was induced by prolonged wakefulness. Internal performance was assessed using five-