Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.
ObjectiveTo discuss the safety of dental extraction with electrocardiogram (ECG) monitoring for cardiovascular patients.
MethodsWe summarized and analyzed the clinical data of 933 cases of dental extraction with ECG monitoring from May 2010 to May 2011. Analysis of the change of heart rate and blood pressure in the process of dental extraction was also carried out.
ResultsAll patients underwent the tooth extraction successfully. The heart rate and blood pressure increased after local anesthesia and in the process of tooth extraction without any accident.
ConclusionUnder the premise of strict control of indications, dental extraction with the implementation of ECG monitoring has a very high security for patients with cardiovascular diseases or other systemic disorders.
ObjectiveTo find out the influencing factors of electrocardiogram (ECG) monitor configuration decision in surgical nursing units and form a scientific configuration standard, so as to provide a basis for the reasonable configuration of ECG monitors.MethodsFrom May to June 2018, the indexes and weights affecting the configuration of ECG monitors in surgical nursing units of a large public hospital were determined by interview survey method and analytic hierarchy process.ResultsThe influencing factors for configuration of ECG monitors in surgical nursing units were the number of operations, number of rescues, number of emergencies, number of deaths, and number of patients transferred to and out of intensive care unit, and the weights were 0.459 7, 0.224 9, 0.155 3, 0.111 2, and 0.049 0, respectively. The classification of nursing units was taken as plan, and the configuration standard of ECG monitors was established.ConclusionThe configuration model of ECG monitors in surgical nursing units based on analytic hierarchy process realizes the combination of qualitative and quantitative analysis, which provides scientific and reasonable reference for the configuration of ECG monitors.
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.
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.
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.
ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.
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.
