An effective ECG signal classification method based on a minimalistic memristive reservoir computing system.

The advantage of reservoir computing (RC) is that only the connection weight between reservoir layer and output layer needs to be trained, and the rest of the connection weights are randomly generated and fixed, which is especially suitable for time series data processing. However, the hardware implementation of high-dimensional random connection of neurons in reservoir layer is still a challenge. The memristors are emerging components with unique nonlinear and memory characteristics. Particularly, memristors allow nonlinear mapping of input time series into high-dimensional feature space, which meets the requirements of the reservoir layer. The reservoir layers of current memristor-based RC systems are based on the dynamics of volatile memristors. In this paper, a non-volatile memristor-based reservoir layer is designed for constructing RC system, in which the voltages across the two memristors are utilized to calculate the reservoir states. In this design, one-dimensional voltage input signal can be easily nonlinearly mapped to a two-dimensional space, which significantly simplifies the complexity of data analysis and enhances the separability of signal features, satisfying the requirement of RC for the high-dimensional feature. The experimental results of the proposed RC system for electrocardiogram (ECG) signal classification task achieve high accuracies of 98.3% and 100% for QRS complexes with and without shift, respectively, which validates the effectiveness of the proposed RC system.