ECG- and HRV-Based Hybrid Architecture—Early Detection of Alzheimer’s Disease and Mild Cognitive Impairment

Cardiac autonomic dysfunction has been implicated in cognitive impairment, yet its potential for early detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) remains largely unexplored. This study proposes EHANet, an electrocardiogram (ECG) and heart rate variability (HRV)-based attention network, to enable objective and noninvasive detection of cognitive impairment. Utilizing the CAUEEG dataset, we conducted comprehensive HRV analysis encompassing temporal, spectral, and nonlinear domains, revealing progressive autonomic dysregulation characterized by diminished parasympathetic activity and increased heart rate fragmentation from normal controls (NC) through MCI to AD. EHANet integrates ECG sequence representations with physiologically interpretable HRV features, achieving diagnostic accuracies of 86.91% (AD vs. NC), 76.0% (MCI vs. NC), 82.26% (AD vs. MCI), and 73.72% (three-way classification), substantially surpassing single-modality approaches. These findings demonstrate that ECG-based cardiac autonomic assessment, combined with deep learning, offers a cost-effective and practical modality for early detection and longitudinal monitoring of cognitive decline, with significant implications for clinical screening and management of neurodegenerative diseases.