Qilu University of Technology(shandong Academy of Sciences)

Shandong Computer Science Center (National Supercomputing Center in Jinan)

Shandong Artificial Intelligence Institute

Shuwang Zhou

Minglei Shu

Pengyao Xu

Zhaoyang Liu

Yinglong Wang

Wei Liu

Rui Qu

Mengyu Sun

Luyao Li

Zhenyu Liu

Kaiwei Li

A few-shot electrocardiogram (ECG) signal classification method based on an improved Siamese network is provided. The method constructs a CMP module as a sub-network of a Siamese network, and combines extracted local and global features to better analyze peak information such as position, amplitude, and offset, making a transformed feature vector more robust. In this way, the method improves the accuracy and stability of few-shot ECG signal classification.

Few-shot electrocardiogram (ECG) signal classification method based on improved siamese network

An electrocardiogram (ECG) signal classification method based on contrastive learning and multi-scale feature extraction are provided. A squeeze-and-excitation-residual networks with next-generation aggregated transformations-context-aware network (SE-ResNeXt-CAN) network model includes a shallow feature extraction module, a first squeeze-and-excitation residual module (SERM), a second SERM, a first context-aware residual module (CARM), and a second CARM. Through the combination and optimization of these modules, the SE-ResNeXt-CAN network model adaptively learns a correlation between channels, and expands a receptive field to fully capture a key feature, improving the performance and generalization ability of an ECG signal classification task.

Electrocardiogram (ECG) signal classification method based on contrastive learning and multi-scale feature extraction

A multi-lead electrocardiogram (ECG) signal classification method based on self-supervised learning relates to the technical field of ECG signal classification. The method includes: processing an original signal through different data augmentation methods, designing an appropriate encoder module, extracting a feature of an ECG signal through a large amount of easily available unlabeled data such that an encoder learns more class information of the ECG signal, fine-tuning the model encoder with a small amount of labeled data for feature optimization, and continuously optimizing a parameter of a feature extractor by training a model such that a generated feature well reflects a structure and information of input data. Through self-supervised learning, the method reduces obstacles caused by performing ECG signal classification through a large amount of expensive manually labeled data, improving the generalization ability of the model.

Multi-lead electrocardiogram (ECG) signal classification method based on self-supervised learning

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.

Electrocardiogram (ECG) signal detection and positioning method based on weakly supervised learning

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.

Electrocardiogram (ECG) signal quality evaluation method based on multi-scale convolutional and densely connected network

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.

Growing community of inventors

Jinan, China

Shuwang Zhou