An intelligent computer-aided diagnosis approach for atrial fibrillation detection based on multi-scale convolution kernel and Squeeze-and-Excitation network

Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion

Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into two-dimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices.

SE-TCN network for continuous estimation of upper limb joint angles

The maturity of human-computer interaction technology has made it possible to use surface electromyographic signals (sEMG) to control exoskeleton robots and intelligent prostheses. However, the available upper limb rehabilitation robots controlled by sEMG have the shortcoming of inflexible joints. This paper proposes a method based on a temporal convolutional network (TCN) to predict upper limb joint angles by sEMG. The raw TCN depth was expanded to extract the temporal features and save the original information. The timing sequence characteristics of the muscle blocks that dominate the upper limb movement are not apparent, leading to low accuracy of the joint angle estimation. Therefore, this study squeeze-and-excitation networks (SE-Net) to improve the network model of the TCN. Finally, seven movements of the human upper limb were selected for ten human subjects, recording elbow angle (EA), shoulder vertical angle (SVA), and shoulder horizontal angle (SHA) values during their movements. The designed experiment compared the proposed SE-TCN model with the backpropagation (BP) and long short-term memory (LSTM) networks. The proposed SE-TCN systematically outperformed the BP network and LSTM model by the mean RMSE values: by 25.0 and 36.8% for EA, by 38.6 and 43.6% for SHA, and by 45.6 and 49.5% for SVA, respectively. Consequently, its R² values exceeded those of BP and LSTM by 13.6 and 39.20% for EA, 19.01 and 31.72% for SHA, and 29.22 and 31.89% for SVA, respectively. This indicates that the proposed SE-TCN model has good accuracy and can be used to estimate the angles of upper limb rehabilitation robots in the future.