A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface - Yuan, Rui; Tiw, Pek Jun; Cai, Lei; Yang, Zhiyu; Liu, Chang; Zhang, Teng; Ge, Chen; Huang, Ru; Yang, Yuchao (Nature communications, 2023)
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 VO 2 memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO 2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO 2 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. Next-generation human-machine interfaces require efficient physiological signal processing systems. Here, the authors propose a hardware system that uses VO 2 memristors to perform brain-like encoding and analysis of physiological signals, and is capable of identifying arrhythmia and epileptic seizures.