Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement / 生物医学工程学杂志

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.

MF-SuP-pKa: Multi-fidelity modeling with subgraph pooling mechanism for pKa prediction

Acid-base dissociation constant (pKa) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pKa prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pKa (multi-fidelity modeling with subgraph pooling for pKa prediction), a novel pKa prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pKa prediction. To overcome the scarcity of accurate pKa data, low-fidelity data (computational pKa) was used to fit the high-fidelity data (experimental pKa) through transfer learning. The final MF-SuP-pKa model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pKa achieves superior performances to the state-of-the-art pKa prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pKa achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.

Diagnosis model for fusarium identification of fungal keratitis based on transfer learning and data augmentation / 国际眼科杂志(Guoji Yanke Zazhi)

@#AIM: To construct and evaluate a diagnostic model based on transfer learning and data augmentation as a non-invasive tool for fusarium identification of fungal keratitis. <p>METHODS: A retrospective study. In this study, 2 157 images of fungal keratitis patients who underwent <i>in vivo</i> confocal microscopy examination in the Department of Ophthalmology of the people's Hospital of Guangxi Zhuang Autonomous Region from March 2017 to January 2020 were included as the dataset, which was classified according to the results of microbial culture. The dataset was subsequently randomly divided into training set(1 380 images), validation set(345 images)and test set(432 images). We used the transfer learning Inception-ResNet V2 network to construct a diagnostic model, and to compare the performance of the model trained on different datasets. The performance of the diagnostic model evaluated with specificity, sensitivity, accuracy, and area under the receiver operating characteristics curve(AUC).<p>RESULTS: The model trained with the original training set had a specificity rate of 71.6%, a sensitivity rate of 72.0%, an accuracy rate of 71.8% and AUC of 0.785(95%<i>CI</i>: 0.742-0.828, <i>P</i><0.0001). And the model trained with the augmented training set had a specificity rate of 76.6%, a sensitivity rate of 83.1%, an accuracy rate of 79.9% and AUC of 0.876(95%<i>CI</i>: 0.843-0.909, <i>P</i><0.0001), which made the model's prediction performance boost.<p>CONCLUSION: In this study, we constructed an intelligent diagnosis system for fungal keratitis fusarium through transfer learning, which has higher accuracy, and realized the intelligent diagnosis of fungal keratitis pathogen fusarium. Furthermore, we verified that the data augmentation strategy can improve the performance of the intelligent diagnosis system when the original dataset is limited, and this method can be used for intelligent diagnosis and identification of fungal keratitis pathogen fusarium.

Classification of ECG arrhythmia based on convolutional neural network / 国际生物医学工程杂志

Objective:To improve the performance of ECG arrhythmia classification algorithm and provide auxiliary basis for clinical ECG diagnosis.Methods:The one-dimensional ECG data was segmented according to the R point, and the segmented data was generated into a 2D image. The samples were expanded by data augmentation technology, and the image features were extracted by the 2D convolutional layer, 2D maximum pooling layer, Flatten layer and fully connected layer in 2D-CNN. Then, the samples were classified with Softmax classifier. The loss function with weight coefficients was used to enhance the model's learning of class S and class V. The MIT-BIH data set was used for model training and algorithm performance evaluation.Results:Sample expansion and the use of loss functions with weight coefficients can improve the recall rate and specificity index of the model, while maintaining the model's accuracy index of the classificatio on VEB and SVEB.Conclusions:The accuracy of the proposed model is 99.02%, and the recall rate of SVEB is 96.4%, indicating that this classification method can assist medical staff in diagnosing heart diseases.