Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification.

Early detection of malign patterns in patients' biological signals can save millions of lives. Despite the steady improvement of artificial intelligence-based techniques, the practical clinical application of these methods is mostly constrained to an offline evaluation of the patients' data. Previous studies have identified organic electrochemical devices as ideal candidates for biosignal monitoring. However, their use for pattern recognition in real time was never demonstrated. Here, we produce and characterize brain-inspired networks composed of organic electrochemical transistors and use them for time-series predictions and classification tasks using the reservoir computing approach. To show their potential use for biofluid monitoring and biosignal analysis, we classify four classes of arrhythmic heartbeats with an accuracy of 88%. The results of this study introduce a previously unexplored paradigm for biocompatible computational platforms and may enable development of ultralow-power consumption hardware-based artificial neural networks capable of interacting with body fluids and biological tissues.