Thermo-responsive and phase-separated hydrogels for cardiac arrhythmia diagnosis with deep learning algorithms

Area of Science:

• Biomaterials Science
• Wearable Technology
• Medical Devices

Background:

• Conventional adhesive hydrogel electrodes for electrocardiogram (ECG) monitoring lack programmable adhesion, risking skin damage and signal loss.
• Current methods struggle with reliable, long-term adhesion and detachment without compromising delicate skin tissues.

Purpose of the Study:

• To develop advanced hydrogel electrodes with on-demand programmable adhesion and detachment for improved ECG signal acquisition.
• To enable accurate, remote diagnosis of cardiac arrhythmias through a wearable sensing platform.

Main Methods:

• Integration of dynamic multiscale contact and temperature-mediated switchable hydrogen bonds in phase-separated smart hydrogels.
• Micro-scale regulation of adhesive molecules and meso-scale modulus modulation for tunable adhesion (1.3 N/m to 110 N/m).
• Development of a real-time cloud platform for remote ECG monitoring and deep learning algorithms for arrhythmia diagnosis.

Main Results:

• Achieved an 84.5-fold increase in on/off adhesive energy, reducing skin adhesion by 98% at low temperatures.
• Successfully captured high-fidelity ECG signals from patients with eight common arrhythmias.
• Demonstrated a wearable cardiac arrhythmia intelligent diagnosis system with 98.5% recognition accuracy.

Conclusions:

• The developed hydrogel electrodes offer programmable adhesion, enhancing skin-interfacing capabilities for cardiac monitoring.
• The wearable sensing platform enables remote, real-time ECG monitoring and accurate arrhythmia diagnosis.
• This technology represents a significant advancement in wearable diagnostics for cardiovascular health.