Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app152312555

Authors: Duyan Geng, Qiang Wang, Weiran Zheng, Yue Yin, Peng Xu, Guizhi Xu

Institution: Hebei University of Technology

Language: English

Topic: Heart Rate Variability and Autonomic Control

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896