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.
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.
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.
Electrocardiogram (ECG) signals are easily disturbed by internal and external noise, and its morphological characteristics show significant variations for different patients. Even for the same patient, its characteristics are variable under different temporal and physical conditions. Therefore, ECG signal detection and recognition for the heart disease real-time monitoring and diagnosis are still difficult. Based on this, a wavelet self-adaptive threshold denoising combined with deep residual convolutional neural network algorithm was proposed for multiclass arrhythmias recognition. ECG signal filtering was implemented using wavelet adaptive threshold technology. A 20-layer convolutional neural network (CNN) containing multiple residual blocks, namely deep residual convolutional neural network (DR-CNN), was designed for recognition of five types of arrhythmia signals. The DR-CNN constructed by residual block local neural network units alleviated the difficulty of deep network convergence, the difficulty in tuning and so on. It also overcame the degradation problem of the traditional CNN when the network depth was increasing. Furthermore, the batch normalization of each convolution layer improved its convergence. Following the recommendations of the Association for the Advancements of Medical Instrumentation (AAMI), experimental results based on 94 091 2-lead heart beats from the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 99.034 9%, 99.498 0% and 99.334 7% for multiclass classification, ventricular ectopic beat (Veb) and supra-Veb (Sveb) recognition, respectively. Using the same platform and database, experimental results showed that under the comparable network complexity, our proposed method significantly improved the recognition accuracy, sensitivity and specificity compared to the traditional deep learning networks, such as deep Multilayer Perceptron (MLP), CNN, etc. The DR-CNN algorithm improves the accuracy of the arrhythmia intelligent diagnosis. If it is combined with wearable equipment, internet of things and wireless communication technology, the prevention, monitoring and diagnosis of heart disease can be extended to out-of-hospital scenarios, such as families and nursing homes. Therefore, it will improve the cure rate, and effectively save the medical resources.
Electrocardiogram (ECG) signal is an important basis for the diagnosis of arrhythmia and myocardial infarction. In order to further improve the classification effect of arrhythmia and myocardial infarction, an ECG classification algorithm based on Convolutional vision Transformer (CvT) and multimodal image fusion was proposed. Through Gramian summation angular field (GASF), Gramian difference angular field (GADF) and recurrence plot (RP), the one-dimensional ECG signal was converted into three different modes of two-dimensional images, and fused into a multimodal fusion image containing more features. The CvT-13 model could take into account local and global information when processing the fused image, thus effectively improving the classification performance. On the MIT-BIH arrhythmia dataset and the PTB myocardial infarction dataset, the algorithm achieved a combined accuracy of 99.9% for the classification of five arrhythmias and 99.8% for the classification of myocardial infarction. The experiments show that the high-precision computer-assisted intelligent classification method is superior and can effectively improve the diagnostic efficiency of arrhythmia as well as myocardial infarction and other cardiac diseases.
ObjectiveTo optimize the therapy protocols of high dose prednisone combined with topiramate (TPM) in children with infantile spasms (IS). MethodsSixty cases were collected in our hospital from September 2012 to September 2013 and randomly divided into two groups(n=30) and followed-up for more than 6 months.The spasms were assesses by video-electroencephalogram (VEEG) monitoring including awake and asleep states before treatment, after two weeks of therapy and the end of the courses respectively.And the Gessel developmental quotient (DQ) scores were performed before treatment and after six months of therapy. ResultsFor the unresponders to high dose prednisone in one week of therapy, there were 46.67%and 60.00% in test group higher than 31.25% and 37.50% in control group respectively in 2 week and in the end of treatment.And the rate of complete resolution of hypsarrhythmia in the test group was 46.67% and 60.00% higher than 25.00% and 37.50% in control group respectively in 2 week and in the end of treatment.But there were no statistical significances between two groups(P >0.05).The incidence of side effects(83.33% vs. 80.00%) and the relapse rate(39.14% vs. 40.00%), were not statistically significant between two groups(P >0.05).The responsive rates for the cases with the lead time within 2 months higher than beyond 2 months in two groups respectively in 2 weeks and in the end of treatment. ConclusionsThe protocol of the test group was superior to that of the control group.The responsive rates of children within 2 months of lead time were higher than beyond 2 months, which indicates that early diagnosis and early treatment would improve efficacy and have an important influence on the prognosis of IS.
Objective To investigate the risk factors for arrhythmia after robotic cardiac surgery. Methods The data of the patients who underwent robotic cardiac surgery under cardiopulmonary bypass (CPB) from July 2016 to June 2022 in Daping Hospital of Army Medical University were retrospectively analyzed. According to whether arrhythmia occurred after operation, the patients were divided into an arrhythmia group and a non-arrhythmia group. Univariate analysis and multivariate logistic analysis were used to screen the risk factors for arrhythmia after robotic cardiac surgery. ResultsA total of 146 patients were enrolled, including 55 males and 91 females, with an average age of 43.03±13.11 years. There were 23 patients in the arrhythmia group and 123 patients in the non-arrhythmia group. One (0.49%) patient died in the hospital. Univariate analysis suggested that age, body weight, body mass index (BMI), diabetes, New York Heart Association (NYHA) classification, left atrial anteroposterior diameter, left ventricular anteroposterior diameter, right ventricular anteroposterior diameter, total bilirubin, direct bilirubin, uric acid, red blood cell width, operation time, CPB time, aortic cross-clamping time, and operation type were associated with postoperative arrhythmia (P<0.05). Multivariate binary logistic regression analysis suggested that direct bilirubin (OR=1.334, 95%CI 1.003-1.774, P=0.048) and aortic cross-clamping time (OR=1.018, 95%CI 1.005-1.031, P=0.008) were independent risk factors for arrhythmia after robotic cardiac surgery. In the arrhythmia group, postoperative tracheal intubation time (P<0.001), intensive care unit stay (P<0.001) and postoperative hospital stay (P<0.001) were significantly prolonged, and postoperative high-dose blood transfusion events were significantly increased (P=0.002). Conclusion Preoperative direct bilirubin level and aortic cross-clamping time are independent risk factors for arrhythmia after robotic cardiac surgery. Postoperative tracheal intubation time, intensive care unit stay, and postoperative hospital stay are significantly prolonged in patients with postoperative arrhythmia, and postoperative high-dose blood transfusion events are significantly increased.
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.
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.
ObjectiveTo investigate the efficacy of bipolar radiofrequency ablation for left ventricular aneurysm-related ventricular arrhythmia associated with mural thrombus. MethodsFifteen patients with left ventricular aneurysm-related frequent premature ventricular contractions associated with mural thrombus were enrolled in Beijing Anzhen Hospital between June 2013 and June 2015. There were 11 male and 4 female patients with their age of 63.5±4.8 years. All patients had a history of myocardial infarction, but no cerebral infarction. All patients received bipolar radiofrequency ablation combined with coronary artery bypass grafting, ventricular aneurysm plasty and thrombectomy. Holter monitoring and echocardiography were measured before discharge and 3 months following the operation. ResultsThere was no death during the operation. Cardiopulmonary bypass time was 92.7±38.3 min. The aortic clamping time was 52.4±17.8 min.The number of bypass grafts was 3.9±0.4. All the patients were discharged 7-10 days postoperatively. None of the patients had low cardiac output syndrome, malignant arrhythmias, perioperative myocardial infarction, or cerebral infarction in this study. Echocardiography conducted before discharge showed that left ventricular end diastolic diameter was decreased (54.87±5.21 cm vs. 60.73±6.24 cm, P=0.013). While there was no significant improvement in ejection fraction (45.20%±3.78% vs. 44.47%±6.12%, P=1.00) compared with those before the surgery. The number of premature ventricular contractions[4 021.00 (2 462.00, 5 496.00)beats vs. 11 097.00 (9 327.00, 13 478.00)beats, P < 0.001] and the percentage of premature ventricular contractions[2.94% (2.12%, 4.87%) vs. 8.11% (7.51%, 10.30%), P < 0.001] in 24 hours revealed by Holter monitoring were all significantly decreased than those before the surgery. At the end of 3-month follow-up, all the patients were angina and dizziness free. Echocardiography documented that there was no statistical difference in left ventricular end diastolic diameter (55.00±4.41 mm vs. 54.87±5.21 mm, P=1.00). But there were significant improvements in ejection fraction (49.93%±4.42% vs. 45.20%±3.78%, P=0.04) in contrast to those before discharge. Holter monitoring revealed that the frequency of premature ventricular contractions[2 043.00 (983.00, 3 297.00)beats vs. 4 021.00 (2 462.00, 5 496.00)beats, P=0.03] were further lessened than those before discharge, and the percentage of premature ventricular contractions[2.62% (1.44%, 3.49%)vs. 8.11% (7.51%, 10.30%), P < 0.001] was significantly decreased than those before the surgery, but no significant difference in contrast to those before discharge. ConclusionThe recoveries of cardiac function benefit from integrated improvements in myocardial ischemia, ventricular geometry, pump function, and myocardial electrophysiology. Bipolar radiofrequency ablation can correct the electrophysiological abnormality, significantly decrease the frequency of premature ventricular contractions, and further improve the heart function.