A Parallel GRU Recurrent Network Model and its Application to Multi-Channel Time-Varying Signal Classification

This study presents a modified recurrent neural network (RNN) model designed as a parallel computing structure for serial information processing. The result is a novel parallel recurrent neural network (P-RNN), proposed for application to time-varying signal classification. The network uses gated recurrent units (GRUs) for basic information processing and consists of a multi-channel time series signal input layer, parallel processing structure units, a signal feature fusion layer, and a softmax classifier. The P-RNN expands the existing RNN serial processing mode for multi-channel time-varying signals into parallel mode and realizes the embedding of multi-channel signal structure features. In these parallel processing units, the input signal for each channel corresponds to a GRU recurrent network. Feature extraction and attribute association of single-channel signals were performed to achieve parallel processing of all-channel signals. In the feature fusion layer, feature vectors from each channel signal were integrated to generate a comprehensive feature matrix. On this basis, the softmax function was used as a classifier for multi-channel signals. With this mechanism, the P-RNN model achieved independent feature extraction of single-channel signals, characteristic fusion of each channel signal, and signal classification based on an integrated feature matrix. This approach maintained characteristic combination relationships that improved serial modes for existing RNN multi-channel signal processing, reduced the loss of structural feature information, and improved the representation ability of combined feature in local time region and the efficiency of the algorithm. In this paper, the properties of the proposed P-RNN are analyzed and a comprehensive learning algorithm is developed. Seven disease classification types commonly diagnosed using 12-lead ECG signals were used to validate the technique experimentally. Results showed the computational efficiency improved by a factor of 11.519, compared with existing RNN serial processing times, producing a correct recognition rate of 95.976%. In particular, the resolution of signal samples with similar distribution characteristics improved significantly, which demonstrates the effectiveness of the proposed technique.