Arrhythmia is a significant cardiovascular disease that poses a threat to human health, and its primary diagnosis relies on electrocardiogram (ECG). Implementing computer technology to achieve automatic classification of arrhythmia can effectively avoid human error, improve diagnostic efficiency, and reduce costs. However, most automatic arrhythmia classification algorithms focus on one-dimensional temporal signals, which lack robustness. Therefore, this study proposed an arrhythmia image classification method based on Gramian angular summation field (GASF) and an improved Inception-ResNet-v2 network. Firstly, the data was preprocessed using variational mode decomposition, and data augmentation was performed using a deep convolutional generative adversarial network. Then, GASF was used to transform one-dimensional ECG signals into two-dimensional images, and an improved Inception-ResNet-v2 network was utilized to implement the five arrhythmia classifications recommended by the AAMI (N, V, S, F, and Q). The experimental results on the MIT-BIH Arrhythmia Database showed that the proposed method achieved an overall classification accuracy of 99.52% and 95.48% under the intra-patient and inter-patient paradigms, respectively. The arrhythmia classification performance of the improved Inception-ResNet-v2 network in this study outperforms other methods, providing a new approach for deep learning-based automatic arrhythmia classification.
The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the F1 index was 98.3%. The algorithm has high performance, meets the needs of clinical diagnosis, and has low algorithm complexity. It can use low-power embedded processors for real-time calculations, and it’s suitable for real-time warning of wearable ECG monitoring equipment.
Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.
ObjectivesTo systematically review the efficacy of traditional Chinese medicine for arrhythmia caused by anthracycline drugs.MethodsPubMed, EMbase, The Cochrane Library, CBM, CNKI, WanFang Data databases were electronically searched to collect randomized controlled trials (RCTs) on the efficacy of traditional Chinese medicine for arrhythmia caused by anthracycline drugs from inception to October 2017. Two reviewers independently screened literature, extracted data and evaluated risk of bias of included studies. Meta-analysis was then performed by Revman 5.3 software.ResultsA total of 4 RCTs involving 312 patients were included. The results of meta-analysis showed that: the incidence of tachycardia in the Wenxin granule treatment group was lower than that in the control group (RR=0.35, 95%CI 0.18 to 0.67, P=0.002). Baoxinkang was more effective than antioxidant western medicine in protecting myocardial SOD activity (RR=2.25, 95%CI 1.74 to 2.76, P<0.000 01). But there was no significant difference between two groups on the incidence of atrial premature beats (RR=0.40, 95%CI 0.15 to 1.08,P=0.07), premature ventricular contractions (RR=0.56, 95%CI 0.23 to 1.34, P=0.19) and atrial fibrillation (RR=0.41, 95%CI 0.11 to 1.53, P=0.18). In addition, there was no significant difference between Wenxin granules and amiodarone in treating arrhythmia induced by anthracycline. The addition of Wenxin granules on the basis of anthracycline antitumor chemotherapy regimens was not effective in delaying disease progression compared with anthracycline alone. Wenxin granules could not change the SOD level of breast cancer patients with cardiotoxicity caused by anthracycline chemotherapy, compared with chemotherapy and basic treatment.ConclusionsThe current evidence shows that Wenxin granules can prevent and reduce anthracycline-induced tachycardia, but its efficacy in improving the overall efficiency, preventing and reducing atrial premature beats, premature ventricular contractions, atrial fibrillation, and SOD levels are unclear. Baoxinkang can protect myocardial SOD activity. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above conclusions.
Objective To investigate the risky factors of ventricular arrhythmias following open heart surgery in patients with giant left ventricle, and offer the basis in order to prevent it’s occurrence. Methods The clinical materials of 176 patients who had undergone the open heart surgery were analyzed retrospectively. There were 44 patients who had ventricular arrhythmia (ventricular arrhythmia group), 132 patients who had no ventricular arrhythmia as contrast (control group). The preoperative clinical data, indexes of types of cardiopathy, ultrasonic cardiogram, electrocardiogram and cardiopulmonary bypass (CPB) etc. were choosed, and tested by using χ2 test,t test and logistic regression to analyse the high endangered factors for incidence of ventricular arrhythmia after open heart surgery. Results Age≥55 years (OR=3.469), left ventricular enddiastolic diameter(LVEDD)≥80 mm (OR=3.927), left ventricular ejection fraction（LVEF）≤55% (OR=2.967), CPB time≥120min(OR=5.170) and aortic clamping time≥80min(OR=4.501) were the independent risk factors of ventricular arrhythmia. Conclusion Ventricular arrhythmia is a severe complication for the patients with giant left ventricle after open heart surgery, and influence the prognosis of the patients. Patient’s age, size of the left ventricle, cardiac function, CPB time and clamping time could influence the incidence of ventricular arrhythmias.
Atrial fibrillation is the most common arrhythmia in clinical practice, and catheter ablation has become a first-line treatment strategy. Among them, cryoballoon ablation has become a standardized treatment for atrial fibrillation due to its advantages such as short surgical time, short learning curve, and minimal patient pain. Currently, a large amount of clinical practice and research have provided new evidence for cryoballoon ablation as a first-line treatment for atrial fibrillation. Therefore, this article provides a review of the current status of catheter ablation, the current status, challenges faced, and prospects as a first-line catheter ablation strategy for atrial fibrillation of cryoballoon ablation, with the aim of providing reference for cardiologists in clinical decision-making in the initial rhythm control of atrial fibrillation.