The cardiac conduction system (CCS) is a set of specialized myocardial pathways that spontaneously generate and conduct impulses transmitting throughout the heart, and causing the coordinated contractions of all parts of the heart. A comprehensive understanding of the anatomical characteristics of the CCS in the heart is the basis of studying cardiac electrophysiology and treating conduction-related diseases. It is also the key of avoiding damage to the CCS during open heart surgery. How to identify and locate the CCS has always been a hot topic in researches. Here, we review the histological imaging methods of the CCS and the specific molecular markers, as well as the exploration for localization and visualization of the CCS. We especially put emphasis on the clinical application prospects and the future development directions of non-destructive imaging technology and real-time localization methods of the CCS that have emerged in recent years.
ObjectiveTo compare the therapeutic effect of dual-chamber pacing (DDD) and ventricular single-chamber pacing (VVI) on arrhythmia via systematic evaluation.
MethodsWith the method of Cochrane system evaluation, we searched Medline, Embase, CNKI, PubMed and Wanfang database (the searching time was up to June 30, 2016) for randomized controlled trials comparing DDD with VVI treatingcardiac arrhythmias. Meta analysis was performed using RevMan5.3 software.
ResultsWe collected 12 randomized controlled trials of DDD and VVI pacing treating cardiac arrhythmia including 1 704 patients, but the quality of the studies were not good. The results of Meta analysis showed that:compared with VVI pacing mode, DDD pacing mode reduced the risk of atrial fibrillation[RR=0.36, 95%CI (0.22, 0.59), P < 0.000 1]; besides, it reduced the left atrial diameter[SMD=-0.43, 95%CI (-0.68, -0.17), P=0.001], the left ventricular end diastolic dimension[SMD=-0.33, 95%CI (-0.61, -0.05), P=0.02] and increased the left ventricular ejection fraction[SMD=1.03, 95%CI (0.49, 1.57), P=0.000 2].
ConclusionsComparing DDD with VVI on the treatment of cardiac arrhythmia in patients with cardiac arrhythmia, DDD pacing can reduce the incidence of atrial fibrillation and thrombosis, enhance heart function and improve blood supply. But because of the low quality of the included studies, the curative effect cannot be confirmed, and more randomized controlled trials with high quality needs to be carried out in the future.
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.
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.
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.
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.
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.
