Enhancing Signal‐to‐Noise Ratio in Flexible ECG Monitoring: Materials, Structural Designs, and Algorithmic Strategies

ABSTRACT Against the backdrop of the escalating incidence of cardiovascular diseases (CVDs), the accurate acquisition and efficient analysis of electrocardiographic (ECG) signals hold pivotal significance for the early screening, diagnosis, and prognostic monitoring of diseases. Studies have established that stable and reliable ECG monitoring data serve as the core prerequisite for enhancing the accuracy of clinical diagnosis. Despite the widespread clinical adoption of conventional ECG monitoring devices, ECG signals are highly susceptible to contact noise, motion artifacts, and environmental interferences, while the limitations of signal processing algorithms further constrain the improvement of diagnostic efficiency. Consequently, the development of novel flexible technology‐based ECG monitoring systems and optimized algorithms has become increasingly critical. This review first analyzes the primary causes of signal interference in conventional ECG monitoring, then systematically outlines the material selection, structural, and algorithmic design of flexible ECG electrodes, and finally provides a forward‐looking perspective on the future development of ECG monitoring technology. This review aims to comprehensively summarize the research progress in electrode materials, structural design, and ECG algorithms, offering valuable insights for the performance optimization of flexible ECG monitoring devices and facilitating the resolution of key challenges related to signal stability and diagnostic accuracy in clinical ECG monitoring.

Authors

Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2026-05-31
DOI
https://doi.org/10.1002/adfm.76294
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Signal‐to‐Noise Ratio in Flexible ECG Monitoring: Materials, Structural Designs, and Algorithmic Strategies

Changpo Zhang, Guifen Sun, Z J Li, Peng Wang et al.
Advanced Functional Materials
Advanced Sensor and Energy Harvesting Materials
article

Enhancing Signal‐to‐Noise Ratio in Flexible ECG Monitoring: Materials, Structural Designs, and Algorithmic Strategies

Changpo Zhang, Guifen Sun, Z J Li, Peng Wang, Xuan Sun, Chuizhou Meng
article en

Abstract

ABSTRACT Against the backdrop of the escalating incidence of cardiovascular diseases (CVDs), the accurate acquisition and efficient analysis of electrocardiographic (ECG) signals hold pivotal significance for the early screening, diagnosis, and prognostic monitoring of diseases. Studies have established that stable and reliable ECG monitoring data serve as the core prerequisite for enhancing the accuracy of clinical diagnosis. Despite the widespread clinical adoption of conventional ECG monitoring devices, ECG signals are highly susceptible to contact noise, motion artifacts, and environmental interferences, while the limitations of signal processing algorithms further constrain the improvement of diagnostic efficiency. Consequently, the development of novel flexible technology‐based ECG monitoring systems and optimized algorithms has become increasingly critical. This review first analyzes the primary causes of signal interference in conventional ECG monitoring, then systematically outlines the material selection, structural, and algorithmic design of flexible ECG electrodes, and finally provides a forward‐looking perspective on the future development of ECG monitoring technology. This review aims to comprehensively summarize the research progress in electrode materials, structural design, and ECG algorithms, offering valuable insights for the performance optimization of flexible ECG monitoring devices and facilitating the resolution of key challenges related to signal stability and diagnostic accuracy in clinical ECG monitoring.

Advanced Functional Materials
Zhejiang Sci-Tech University (CN), Fudan University (CN), University of Jinan (CN)
China Postdoctoral Science Foundation, Natural Science Foundation of Shandong Province
Openalex Percentile: Top 10%
Advanced Sensor and Energy Harvesting Materials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.