Fine-Grained ECG Monitoring Based on Stretchable Organic Electrochemical Transistors with Anisotropic Gel...

Conventional biometric methods are limited by poor liveness detection and spoofing resistance, whereas intrinsically unique Electrocardiogram (ECG) signals offer a more secure alternative for biometric authentication. However, the reliable acquisition of high-fidelity ECG signals remains a key challenge in stretchable and wearable electronics due to motion-induced signal degradation and material limitations. Here, we present a material-driven approach based on a stretchable organic electrochemical transistor (OECT) platform integrating percolation-optimized metal/elastomer composite electrodes and shear-aligned anisotropic eutectic gel electrolytes. This design enables robust electrophysiological signal transduction, achieving a high transconductance of 5.63 mS and a signal-to-noise ratio (SNR) of 35.3 dB. Due to the anisotropic stretchability of the hydrogel electrolytes, the device maintains good performance under 30% strain, enabling reliable acquisition of microvolt-scale ECG signals with preserved waveform integrity for accurate biometric identification. When combined with a one-dimensional convolutional neural network (1D-CNN), the system achieves an identification accuracy of 99%, validating its potential for intelligent, wearable authentication. This work offers a scalable and hardware-efficient strategy for next-generation wearable bioelectronics that unify health monitoring and identity verification within a single platform.