![]() Monitoring and analyzing these physiological signals form the basis of biomedical devices used for the diagnosis, detection, and treatment of various diseases 2. For instance, arrhythmias can be picked up by electrocardiogram (ECG) signals 3 while epilepsy, which is a common neurological disorder, manifests itself as abnormalities in electroencephalogram (EEG) signals during epileptic seizure 4. Physiological signals reflect the electrical activity of a specific body part 1 and provide valuable information about mood, cognition, and many other health issues 2, thus any deviation from the norm in patterns may indicate an underlying health problem. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces. 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. 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 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. ![]() Here, we propose a highly efficient neuromorphic physiological signal processing system based on VO 2 memristors. ![]() However, the explosion of physiological signal data presents challenges for traditional systems. Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. ![]()
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