ObjectiveTo find the relationship between bicuspid aortic valve (BAV) and the dilatation or aneurysm of the aorta using electrocardiogram-gated computed tomography angiography (CTA). MethodsWe collected the clinical data of the BAV coexisting with suspected aortic dilatation or aneurysm from February 2012 through April 2015. A total of 124 patients were analyzed retrospectively. There were 97 males and 27 females at an anverage age of 50.35±16.26 years. According to the CTA, patients were classified into two groups: a pure BAV(without raphe) group and a BAV (with raphe) group. we recorded the aortic diameters, gender, age, and so on. ResultsOf the 124 patients, 91 (73.4%) had BAV with raphe, and 33 patients (26.6%) had pure BAV. The analysis revealed that the diameter of the annulus (23.90±3.34 mm vs. 21.74±3.46 mm, P=0.005), the sinuses of Valsalva (40.93±6.78 mm vs. 37.35±7.06 mm, P=0.022), the tubular portion of the ascending aorta (45.38±7.66 mm vs. 38.29±8.18 mm, P=0.0001), and the part of the aorta proximal to the innominate artery (34.19±4.98 mm vs. 30.23±6.62 mm, P=0.02) between patients with BAV with raphe and pure BAV had significant differences. And there was a significant difference in prevalence of dilatation of the aorta between patients with pure BAV and BAV with raphe [77/91 (84.6%) vs.18/31(58.1%), P=0.004]. Of the 91 BAV with raphe patients, we found 76 patients (83.5%) with right and left coronary cusps (R-L) fusion, 13 patients (14.3%) with right and non-coronary cusps (R-N) fusion, and 2 patients (1.2%) with left and non-coronary cusps (L-N) fusion. There was a statistical difference in the aortic root diameters between R-L fusion BAV and R-N fusion BAV. The diameter of the distal ascending aorta and proximal aortic arch between R-L and R-N fusion BAV had statistical differences. ConclusionsBAV with raphe is more common than pure BAV and is more often associated with dilatation and aneurysm of the ascending aorta. Otherwise R-L fusion BAV is associated with increased diameters of the aortic root, while R-N fusion BAV is associated with increased diameters of the distal ascending aorta and proximal arch.
Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F1-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
ObjectiveTo explore the actual effect of “graphic-sequenced memory method” in teaching electrocardiogram (ECG). MethodsOne hundred students were randomly divided into a traditional teaching group (n=50) and an innovative teaching group (n=50) in May, 2014. Teachers in the traditional teaching group utilized the traditional teaching outline, and teachers in the innovative teaching group received training in the new teaching method and syllabus. All students took an examination in the final semester by analyzing 20 ECGs from real clinical cases and gave their ECG reports. ResultsThe average ECG reading time was (32.0±4.8) minutes for the traditional teaching group and (18.0±3.6) minutes for the innovative teaching group. The average ECG accuracy results were (43.0±5.2)% for the traditional teaching group and (77.0±9.6)% for the innovative teaching group. ConclusionsECG learning is an important branch of the cardiac discipline, but ECG’s mechanisms are intricate and the learning content scattered. Textbooks tend to make students feel confused due to the restrictions of the length and format of the syllabi, and there are many other limitations. Graphic-sequenced memory method is a useful method which can be fully used in ECG teaching.
The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can identify the state of mental fatigue, which helps to maintain a healthy life. The method proposed in this paper is a new recognition method of psychological fatigue state of electrocardiogram signals based on convolutional neural network and long short-term memory. Firstly, the convolution layer of one-dimensional convolutional neural network model is used to extract local features, the key information is extracted through pooling layer, and some redundant data is removed. Then, the extracted features are used as input to the long short-term memory model to further fuse the ECG features. Finally, by integrating the key information through the full connection layer, the accurate recognition of mental fatigue state is successfully realized. The results show that compared with traditional machine learning algorithms, the proposed method significantly improves the accuracy of mental fatigue recognition to 96.3%, which provides a reliable basis for the early warning and evaluation of mental fatigue.
Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.
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.
Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.
Objective To analyze the electrocardiogram (ECG) and troponin (cTnI) in patients with acute coronary syndrome (ACS), so as to assess their value in diagnosing the extent of vascular lesions. Methods The results of ECG, cTnI and coronary angiography (CAG) were analyzed in 37 patients with ACS. Chi-square test and a logistic regression model were used for statistical analysis. Results In patients with positive ECG or cTnI, the results of Chi-square test showed that the incidences of coronary occlusion (P=0.016, 0.003, respectively) and coronary stenosis (P=0.121, 0.013, respectively) were significantly higher than for those with negative ECG or cTnI. The results of logistic regression analysis indicated that only cTnI was significantly correlated with coronary occlusion (P=0.013) and moderate to severe coronary stenosis (P=0.021). ECG has significant consistency with cTnI (Kappa=0.617, Plt;0.001). Conclusion Both ECG and the qual itative cTnI test can reflect the extent of vascular lesions in patients with ACS.
The judgment of the type of arrhythmia is the key to the prevention and diagnosis of early cardiovascular disease. Therefore, electrocardiogram (ECG) analysis has been widely used as an important basis for doctors to diagnose. However, due to the large differences in ECG signal morphology among different patients and the unbalanced distribution of categories, the existing automatic detection algorithms for arrhythmias have certain difficulties in the identification process. This paper designs a variable scale fusion network model for automatic recognition of heart rhythm types. In this study, a variable-scale fusion network model was proposed for automatic identification of heart rhythm types. The improved ECG generation network (EGAN) module was used to solve the imbalance of ECG data, and the ECG signal was reproduced in two dimensions in the form of gray recurrence plot (GRP) and spectrogram. Combined with the branching structure of the model, the automatic classification of variable-length heart beats was realized. The results of the study were verified by the Massachusetts institute of technology and Beth Israel hospital (MIT-BIH) arrhythmia database, which distinguished eight heart rhythm types. The average accuracy rate reached 99.36%, and the sensitivity and specificity were 96.11% and 99.84%, respectively. In conclusion, it is expected that this method can be used for clinical auxiliary diagnosis and smart wearable devices in the future.
Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.