Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for traditional systems. Here, we propose a highly efficient neuromorphic physiological signal processing system based on VO₂ memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO₂ memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO₂ memristors is utilized in compact Leaky Integrate and Fire (LIF) and Adaptive-LIF (ALIF) neurons, which are incorporated into a decision-making Long short-term memory Spiking Neural Network. The system demonstrates superior computing capabilities, needing only small-sized LSNNs to attain high accuracies of 95.83% and 99.79% in arrhythmia classification and epileptic seizure detection, respectively. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces.

生理信号处理在下一代人机界面中发挥着关键作用，因为生理信号提供丰富的认知和健康相关信息。然而，生理信号数据的爆炸给传统系统带来了挑战。_{在这里，我们提出了一种基于 VO 2}忆阻器的高效神经形态生理信号处理系统。_{利用VO 2}忆阻器的易失性和正/负对称阈值切换特性来构建稀疏尖峰但高保真度的生理信号异步尖峰编码器。_{此外，VO 2}的动力学行为忆阻器用于紧凑型漏积分和激发 (LIF) 和自适应 LIF (ALIF) 神经元，这些神经元被纳入决策长短期记忆尖峰神经网络。该系统表现出卓越的计算能力，仅需要小型LSNN即可在心律失常分类和癫痫发作检测方面分别达到95.83%和99.79%的高精度。这项工作凸显了忆阻器在构建高效的神经形态生理信号处理系统和促进下一代人机界面方面的潜力。