An intelligent diagnostic method of ECG signal based on Markov transition field and a ResNet

An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm

Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention mechanism (SE module), convolutional neural network (CNN), and long short-term memory neural network (LSTM). The data of this model use the MIT-BIH arrhythmia database, and after noise reduction of raw ECG data by the EEMD denoising algorithm, a CNN-LSTM is used to learn features from the data, and the fusion channel attention mechanism is used to adjust the weight of the feature map. The CNN-LSTM-SE model is compared with the LSTM, CNN-LSTM, and LSTM-attention models, and the models are evaluated using Precision, Recall, and F1-Score. The classification performance of the tested CNN-LSTM-SE classification prediction model is better, with a classification accuracy of 98.5%, a classification precision rate of more than 97% for each label, a recall rate of more than 98%, and an F1-score of more than 0.98. It meets the requirements of arrhythmia classification prediction and has a certain practical value.

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus

BACKGROUND

Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)-transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods - the Markov transition field, short-time Fourier transform and recurrence plot - were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness.

RESULTS

The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices.

CONCLUSION

The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry.

Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells

This work investigated the high-throughput classification performance of microscopic images of mesenchymal stem cells (MSCs) using a hyperspectral imaging-based separable convolutional neural network (CNN) (H-SCNN) model. Human bone marrow mesenchymal stem cells (hBMSCs) were cultured, and microscopic images were acquired using a fully automated microscope. Flow cytometry (FCT) was employed for functional classification. Subsequently, the H-SCNN model was established. The hyperspectral microscopic (HSM) images were created, and the spatial-spectral combined distance (SSCD) was employed to derive the spatial-spectral neighbors (SSNs) for each pixel in the training set to determine the optimal parameters. Then, a separable CNN (SCNN) was adopted instead of the classic convolutional layer. Additionally, cultured cells were seeded into 96-well plates, and high-functioning hBMSCs were screened using both manual visual inspection (MV group) and the H-SCNN model (H-SCNN group), with each group consisting of 96 samples. FCT served as the benchmark to compare the area under the curve (AUC), F1 score, accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) between the manual and model groups. The best classification Acc was 0.862 when using window size of 9 and 12 SSNs. The classification Acc of the SCNN model, ResNet model, and VGGNet model gradually increased with the increase in sample size, reaching 89.56 ± 3.09, 80.61 ± 2.83, and 80.06 ± 3.01%, respectively at the sample size of 100. The corresponding training time for the SCNN model was significantly shorter at 21.32 ± 1.09 min compared to ResNet (36.09 ± 3.11 min) and VGGNet models (34.73 ± 3.72 min) (P < 0.05). Furthermore, the classification AUC, F1 score, Acc, Sen, Spe, PPV, and NPV were all higher in the H-SCNN group, with significantly less time required (P < 0.05). Microscopic images based on the H-SCNN model proved to be effective for the classification assessment of hBMSCs, demonstrating excellent performance in classification Acc and efficiency, enabling its potential to be a powerful tool in future MSCs research.