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.
Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) signal features, so that the feature selection is subjective, and the feature extraction is complex, leaving the classification accuracy usually affected. Based on this situation, a new method of arrhythmia automatic classification based on discriminative deep belief networks (DDBNs) is proposed. The morphological features of heart beat signals are automatically extracted from the constructed generative restricted Boltzmann machine (GRBM), then the discriminative restricted Boltzmann machine (DRBM) with feature learning and classification ability is introduced, and arrhythmia classification is performed according to the extracted morphological features and RR interval features. In order to further improve the classification performance of DDBNs, DDBNs are converted to deep neural network (DNN) using the Softmax regression layer for supervised classification in this paper, and the network is fine-tuned by backpropagation. Finally, the Massachusetts Institute of Technology and Beth Israel Hospital Arrhythmia Database (MIT-BIH AR) is used for experimental verification. For training sets and test sets with consistent data sources, the overall classification accuracy of the method is up to 99.84% ± 0.04%. For training sets and test sets with inconsistent data sources, a small number of training sets are extended by the active learning (AL) method, and the overall classification accuracy of the method is up to 99.31% ± 0.23%. The experimental results show the effectiveness of the method in arrhythmia automatic feature extraction and classification. It provides a new solution for the automatic extraction of ECG signal features and classification for deep learning.
Objective To investigate whether single cycle ischemic preconditioning （IP） improves the myocardial preservation in patients undergoing cardiac valve replacement. Methods From August 2002 to April 2006, 85 patients who had chronic heart valve disease and required cardiac valve replacement were randomly divided into two groups. IP group, 47 allocated to receive IP and arrested with 4 C St. Thomas＇ Hospital cardioplegic solution during cardiopulmonary bypass（CPB）, preconditioning was accomplished by using single cycle of 2 minutes occlusion of aorta followed by 3 minutes of reperfusion before cross-clamping. Control group, 38 allocated to receive 4 C St. Thomas＇ Hospital cardioplegic solution alone. Myocardial protective effects were assessed by determinations of creatinine kinase-MB isoenzyme （CK-MB） and cardiac troponin I（cTnI）, ST-T changes, ventricular arrhythmias and other clinical data in ICU. Results Serum CK-MB and cTnI concentrations were increased postoperatively in two groups. At 24, 48 and 72h after operation, values of CK-MB in IP group was significantly lower than that in control group （P〈0.05）, cTnI at 24 and 48h after operation also less in IP group （P〈0.05）. The duration for patients needed for antiarrhythmic drugs in IP group was lower than that in control group （P〈0.05）. Compared with control group, fewer inotropic drugs were used in IP group. As a result, ICU stay time in IP group was shorter than that in control group （P〈0.05）. Conclusion IP enhances the myocardial protective effect when it was used with hypothermic hyper kalemic cardioplegic solution in patients undergoing cardiac valve replacement, IP significantly reduces the postoperative increase of CK-MB, cTnI and plessens the severity of postoperative ventricular arrhythmias.
Sepsis-associated organ dysfunction arises from uncontrolled inflammation and immune dysregulation, causing microcirculatory impairment and multi-organ failure. Stellate ganglion block (SGB) may confer organ protection by regulating the sympathetic nervous system and hypothalamic-pituitary-adrenal axis to suppress excessive inflammation and oxidative stress. Available evidence, mainly from experimental and small clinical studies, suggests potential benefits of SGB in sepsis-induced acute lung injury, ventricular arrhythmias, and limb ischemia, which require confirmation in multicenter randomized controlled trials. This review outlines the mechanisms and clinical advances of SGB in sepsis-related organ dysfunction, providing a theoretical basis for its application in critical care.
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.
Objective To improve the myocardial protection result, observe the effects of 11,12 epoxyeicosatrienoic acid (11,12 EET) on reperfusion arrhythmias in the isolated perfused immature rabbit hearts, which underwent long term preservation. Methods Sixteen isolated rabbit hearts were randomly assigned to two groups, 8 rabbits each group. Control group: treated with St.Thomas Ⅱ solution, experimental group: treated with St.Thomas Ⅱ solution plus 11,12 EET. By means of the Langendorff technique, these isolated rabbit hearts were arrested and stored for 16 hours with 4℃ hypothermia, and underwent 30 minutes of reperfusion(37℃). The mean times until the cessation of both electrical and mechanical activity were measured after infusion of cardioplegia. The heart rate (HR), coronary flow (CF), myocardial water content (MWC), value of creatine kinase (CK) and lactic dehydrogenase (LDH), myocardial calcium content and the arrhythmias score (AS) during the period and at the endpoint of the reperfusion were observed. Results The times until electrical and mechanical activity arrest in the experimental group were significantly shorter than those in control group ; HR, CF, MWC, CK, LDH, myocardial calcium content and AS were significantly better than those in control group. Conclusions These data suggest that 11,12 EET added to the cardioplegic solution of St.Thomas Ⅱ has lower incidence rate of reperfusion arrhythmias.
Arrhythmia is a kind of common cardiac electrical activity abnormalities. Heartbeats classification based on electrocardiogram (ECG) is of great significance for clinical diagnosis of arrhythmia. This paper proposes a feature extraction method based on manifold learning, neighborhood preserving embedding (NPE) algorithm, to achieve the automatic classification of arrhythmia heartbeats. With classification system, we obtained low dimensional manifold structure features of high dimensional ECG signals by NPE algorithm, then we inputted the feature vectors into support vector machine (SVM) classifier for heartbeats diagnosis. Based on MIT-BIH arrhythmia database, we clustered 14 classes of arrhythmia heartbeats in the experiment, which yielded a high overall classification accuracy of 98.51%. Experimental result showed that the proposed method was an effective classification method for arrhythmia heartbeats.
Objective To evaluate the feasibility of imaging the rat cardiac conduction system (CCS) using transaortic antegrade perfusion of Alexa Fluor 633-labeled antibodies targeting hyperpolarization-activated cyclic nucleotide-gated cation channel 4 (HCN4) and connexin (Cx). The study also sought to optimize antibody dosage, perfusion duration, and assess the photostability of the dye. Methods Ex vivo rat heart model with transaortic antegrade perfusion was established using 33 male SPF-grade Sprague-Dawley (SD) rats. Primary and secondary antibody solutions were sequentially perfused in an antegrade manner. After perfusion for predetermined durations, the atrioventricular junction was observed, and the fluorescence intensity of the corresponding area was recorded. Five dose-gradient groups (n=3 rats/group), five perfusion time-gradient groups (n=3 rats/group), and ten continuous LED light exposure time-gradient groups (using 3 rats prepared with a fixed dose and time) were established to observe and record regional fluorescence intensity. Standard immunofluorescence staining was performed on both paraffin and frozen sections for comparative histological analysis. Results A region of aggregated red fluorescent signal was observed in the atrioventricular junction. Following semi-quantitative fluorescence intensity analysis of HCN4/Cx43 and validation through comparative histology, this structure was identified as the atrioventricular node (AVN) region. The AVN-to-background fluorescence intensity ratio showed no statistically significant differences among groups with increasing antibody dosage (P>0.05). The ratio increased with longer antibody perfusion times. Furthermore, no statistically significant differences in the ratio were observed among groups with extended light exposure (P>0.05). Conclusion Transaortic antegrade perfusion of fluorescently labeled antibodies can successfully image the AVN within the CCS of ex vivo rat hearts. Increasing the antibody dosage does not significantly improve the AVN imaging effect. Longer antibody perfusion time results in better imaging quality of the AVN. The fluorescent dye maintains sufficient visualization of the AVN even after prolonged (8 h) exposure to light.
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.
Objective To evaluate the efficacy and safety of Shen Song Yang Xin Capsule for cardiac arrhythmia. Methods Randomized controlled trials (RCTs) were searched from the following electronic databases: WanFang, CNKI, CBM, VIP, PubMed, and The Cochrane Library. Quality assessment and data extraction were conducted by two reviewers independently. Disagreement was resolved through discussion. All data were analyzed by using RevMan 5.0 software. Results Thirteen studies involving 1896 participants were included. The results of meta-analyses showed that compared with the control group, a) efficacy: Shen Song Yang Xin Capsule was superior to mexiletine (OR=2.96, 95%CI 1.79 to 4.87), and propafenone (OR=2.41, 95%CI 1.60 to 3.62), but was not superior to miodarone (OR=1.25, 95%CI 0.88 to 1.71); b) safety: Shen Song Yang Xin Capsule was superior to propafenone and miodarone in reducing the incidence of cardiac arrhythmia (OR=0.06, 95%CI 0.01 to 0.35; OR=0.05, 95%CI 0.02 to 0.17), but no significant difference was found between the two groups in incidence of gastrointestinal adverse reactions. Conclusion Based on the current studies, Shen Song Yang Xin Capsule is not inferior to the commonly-used anti-arrhythmic medicine at present. It has lower incidence of cardiac arrhythmia, and has no significant difference in the incidence of gastrointestinal adverse reactions compared with western drugs. For the quality restrictions of the included studies, more double blind RCTs with high quality are required to further assess the effects.