Objective To observe the change of sino-atrial nodal tissue structure and ectopic pacing function after xenogenic sino-atrial nodal tissue transplanted into left ventricular wall, so as to provide new ideas for the treatment of sick sinus syndrome and severe atrioventricular block. Methods Seventy healthy rabbits were selected, male or female, and weighing 1.5-2.0 kg. Of them, 42 were used as reci pient animals and randomly divided into sham operation group, warm ischemia transplantation group, and cold ischemia transplantation group (n=14), the other 28 were used as donors of warm ischemia and cold ischemia transplantation groups, which were sibl ing of the recipients. In recipients, a 6-mm-long and about 2-mm-deep incision was made in the vascular sparse area of left ventricular free wall near the apex. In sham operation group, the incision was sutrued directly by 7-0 Prolene suture; in cold ischemia transplantation group, after the aortic roots cross-clamping, 4 ℃ cold crystalloid perfusion fluid infusion to cardiac arrest, then sinoatrial node were cut 5 mm × 3 mm for transplantation; in warm ischemia transplantation group, the same size of the sinus node tissue was captured for transplantation. After 1, 2, 3, and 4 weeks, 3 rabbits of each group were harvested to make bradycardia by stimulating bilateral vagus nerve and the cardiac electrical activity was observed; the transplanted sinus node histology and ultrastructural changes were observed.? Results? Thirty-six recipient rabbits survived (12 rabbits each group). At 1, 2, 3, and 4 weeks after bilateral vagus nerve stimulation, the cardiac electrical activity in each group was significantly slower, and showed sinus bradycardia. Four weeks after operation the heart rates of sham operation group, warm ischemia, and cold ischemia transplantation group were (81.17 ± 5.67), (82.42 ± 7.97), and (80.83 ± 6.95) beats/ minute, respectively; showing no significant difference among groups (P gt; 0.05). And no ectopic rhythm of ventricular pacing occurred. Sino-atrial nodal tissue survived in 6 of warm ischemic transplantation group and in 8 of cold ischemia transplantation group; showing no significant difference between two groups (P gt; 0.05). Two adjacent sinoatrial node cells, vacuole-l ike structure in the cytoplasm, a few scattered muscle microfilaments, and gap junctions between adjacent cells were found in transplanted sinus node. Conclusion The allograft sinus node can survive, but can not play a role in ectopic pacing.
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.
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.
ObjectiveTo explore and analyze the risk factors for arrhythmia in patients after heart valve replacement.MethodsA retrospective analysis of 213 patients undergoing cardiac valve replacement surgery under cardiopulmonary bypass in our hospital from August 2017 to August 2019 was performed, including 97 males and 116 females, with an average age of 53.4±10.5 year and cardiac function classification (NYHA) grade of Ⅱ-Ⅳ. According to the occurrence of postoperative arrhythmia, the patients were divided into a non-postoperative arrhythmia group and a postoperative arrhythmia group. The clinical data of the two groups were compared, and the influencing factors for arrhythmia after heart valve replacement were analyzed by logistic regression analysis.ResultsThere were 96 (45%) patients with new arrhythmia after heart valve replacement surgery, and the most common type of arrhythmia was atrial fibrillation (45 patients, 18.44%). Preoperative arrhythmia rate, atrial fibrillation operation rate, postoperative minimum blood potassium value, blood magnesium value in the postoperative arrhythmia group were significantly lower than those in the non-postoperative arrhythmia group (P<0.05); hypoxemia incidence, hyperglycemia incidence, acidosis incidence, fever incidence probability were significantly higher than those in the non-postoperative arrhythmia group (P<0.05). The independent risk factors for postoperative arrhythmia were the lowest postoperative serum potassium value (OR=0.305, 95%CI 0.114-0.817), serum magnesium value (OR=0.021, 95%CI 0.002-0.218), and hypoxemia (OR=2.490, 95%CI 1.045-5.930).ConclusionTaking precautions before surgery, improving hypoxemia after surgery, maintaining electrolyte balance and acid-base balance, monitoring blood sugar, detecting arrhythmia as soon as possible and dealing with it in time can shorten the ICU stay time, reduce the occurrence of complications, and improve the prognosis of patients.
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.
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.
As an important medical electronic equipment for the cardioversion of malignant arrhythmia such as ventricular fibrillation and ventricular tachycardia, cardiac external defibrillators have been widely used in the clinics. However, the resuscitation success rate for these patients is still unsatisfied. In this paper, the recent advances of cardiac external defibrillation technologies is reviewed. The potential mechanism of defibrillation, the development of novel defibrillation waveform, the factors that may affect defibrillation outcome, the interaction between defibrillation waveform and ventricular fibrillation waveform, and the individualized patient-specific external defibrillation protocol are analyzed and summarized. We hope that this review can provide helpful reference for the optimization of external defibrillator design and the individualization of clinical application.
Lorenz plot (LP) method which gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze cardiac arrhythmias, and the morphologies and positions of the extracted attractors may reveal the underlying mechanisms of the onset and termination of arrhythmias. But automatic diagnosis is still impossible because it is lack of the method of extracting attractors by now. We presented here a methodology of attractor extraction and recognition based upon homogeneously statistical properties of the location parameters of scatter points in three dimensional LP (3DLP), which was constructed by three successive RR intervals as X, Y and Z axis in Cartesian coordinate system. Validation experiments were tested in a group of RR-interval time series and tags data with frequent unifocal premature complexes exported from a 24-hour Holter system. The results showed that this method had excellent effective not only on extraction of attractors, but also on automatic recognition of attractors by the location parameters such as the azimuth of the points peak frequency (APF) of eccentric attractors once stereographic projection of 3DLP along the space diagonal. Besides, APF was still a powerful index of differential diagnosis of atrial and ventricular extrasystole. Additional experiments proved that this method was also available on several other arrhythmias. Moreover, there were extremely relevant relationships between 3DLP and two dimensional LPs which indicate any conventional achievement of LPs could be implanted into 3DLP. It would have a broad application prospect to integrate this method into conventional long-time electrocardiogram monitoring and analysis system.