Intravascular optical coherence tomography (IVOCT) has emerged as a high-resolution and minimal-invasive imaging technique that provides high-speed visualization of coronary arterial vessel walls and clearly displays the vessel lumen and lesions under the intima. However, morphological gray-scale images cannot provide enough information about the tissue components to accurately characterize the plaque tissues including calcified, fibrous, lipidic and mixed plaques. Quantitative IVOCT (qIVOCT) is necessary to provide the physiological contrast mechanisms and obtain the characteristic parameters of tissues with clinical diagnostic value. In this paper, the progress of qIVOCT is reviewed. The current methods for quantitatively measuring optical, elastic and hemodynamic parameters of vessel wall and plaque tissues using IVOCT gray-scale images and raw backscattered signals are introduced and potential development is forecast.
In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.