Abstract: An electrocardiograph (ECG) signal quality evaluation method based on a multi-scale convolutional and densely connected network is provided. Firstly, an original ECG signal is preprocessed to remove a baseline drift and power line interference. Then, based on a consistency principle of a label determining result and a principle of setting a confidence coefficient, an AlexNet model is trained to mutually correct incorrect labels in a dataset to obtain a final ECG signal fragment for quality classification. Finally, the signal fragment is input into an improved lightweight densely connected quality classification model to classify quality of the ECG signal fragment.

Abstract: An electrocardiograph (ECG) signal detection and positioning method based on weakly supervised learning is provided. A deep learning model mainly includes a multi-scale feature extraction module, a self-attention encoding module, and a classification and positioning module. An extracted original ECG signal is denoised and segmented to obtain a fixed-length pure ECG signal segment. In the convolutionally-connected multi-scale feature extraction module, a channel local attention (CLA) layer is introduced, and a PReLU activation function is used to achieve a better local information extraction capability. The self-attention encoding module is introduced to establish an association between a local feature and a global feature. The classification and positioning module is introduced to output a general location of an abnormal signal. A fusion module enables the model to map a local predicted value onto a global predicted value, and model parameters are trained on a weakly annotated dataset.

Abstract: An electrocardiograph (ECG) signal enhancement method based on a novel generative adversarial network (GAN) effectively enhances a capability of a generator model for understanding and expressing input data by using a multi-branch structure of bi-directional long short-term memory (BiLSTM) neural networks with different quantities of hidden neurons, and stitching outputs of last time steps of forward propagation of the different BiLSTM networks. A new ECG signal enhancement module EEA-Net is proposed, which uses an adaptive convolutional layer to dynamically adjust a size of a convolution kernel, making the model more flexible in processing input sequences of different lengths. In addition, the model uses an adaptive average pooling layer to perform weighted average pooling on the input data to better capture important information of the input data.

Abstract: A multi-label electrocardiogram (ECG) signal classification method based on an improved attention mechanism is provided. A model is constructed for classifying a multi-label (multi-lead) ECG signal. The model has a strong ECG data learning ability, allowing a computer to fully extract a feature of the ECG signal and construct a data processing channel model. Therefore, the multi-label (multi-lead) ECG signal can be effectively classified, improving the accuracy and precision of classification.

Abstract: A dynamic facial expression recognition (FER) method based on a Dempster-Shafer (DS) theory improves a feature extraction effect of an expression video through multi-feature fusion, and deeply learns an imbalanced dynamic expression feature by using the DS theory, multi-branch convolution, and an attention mechanism. Compared with other methods, the dynamic FER method scientifically and effectively reduces an impact of sample imbalance on expression recognition, fully utilizes a spatio-temporal feature to mine potential semantic information of the video expression to perform expression classification, thereby improving reliability and accuracy and meeting a demand for the expression recognition.