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.
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 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.
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.
Objective To evaluate the efficacy and clinical significance of bipolar radiofrequency ablation in the treatment of left ventricular aneurysm with ventricular arrhythmias guided by CARTO mapping system. Methods From September 2009 to December 2015, 56 patients with ventricular aneurysm following myocardial infarction were enrolled. All patients suffered different levels of angina pectoris symptoms evaluated by Holter (the frequencies of ventricular arrhythmias more than 3 000 per day). They were divided into two groups according to random ballot and preoperative communication with patients' family members: a bipolar radiofrequency ablation group (n=28, 20 males, 8 females, mean age of 61.21±1.28 years) receiving off-pump coronary artery bypass grafting (OPCABG), ventricular aneurysm surgery combined with bipolar radiofrequency ablation, and a non-bipolar radiofrequency ablation group (n=28, 22 males, 6 females, mean age of 57.46±1.30 years) receiving OPCABG and single ventricular aneurysm surgery. The grade of cardiac function and ventricular arrhythmia was compared between the two groups during pre-operation, discharge and follow-up. Results All patients were discharged successfully. There was no in-hospital death in both two groups. One patient in the non-radiofrequency group had cerebral infarction. All patients were re-checked with Holter before discharge and the frequency of ventricular arrhythmias significantly decreased compared to that of pre-operation in both groups, and was more significant in bipolar radiofrequency ablation group (1 197.00±248.20 times/24 h vs. 1 961.00±232.90 times/24 h, P<0.05). There was significant difference in duration of mechanical ventilation and ICU stay between the two groups (P<0.05). The left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDD) and left ventricular end-systolic diameter (LVESD) significantly improved (P<0.05) after operation in both groups. Conclusion The clinical efficacy of bipolar radiofrequency ablation in the treatment of ventricular aneurysm with ventricular arrhythmia guided by CARTO mapping is safe and effective, but its long-term outcomes still need further follow-up.
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.
ObjectiveTo compare and analyze the electroencephalographic (EEG) characteristics of infants with infantile epileptic spasms syndrome (IESS) and healthy infants during sleep using power spectral density (PSD) analysis. MethodsInfants aged 5 to 9 months with IESS were included, along with an equal number of age-matched healthy controls. EEG signals during sleep were recorded using the Nihon Kohden EEG-1200C system. The energy distribution in the theta (θ), alpha (α), sigma (σ), and beta (β) frequency bands, as well as the morphology and values of PSD within the 4 ~ 30 Hz range, were analyzed. Additionally, spectral entropy (SpEn) was calculated to evaluate signal complexity. Results A total of 10 IESS patients and 10 healthy infants were included. There were no significant differences in gender or age between the two groups (P=0.64, P=0.88). In both groups, PSD values showed a linear decreasing trend with increasing frequency. However, the IESS group showed notable differences in PSD morphology, amplitude, and energy distribution compared to controls. These included the absence of a σ-band peak, greater PSD dispersion across electrodes, significant alterations in energy distribution across θ, α, σ, and β bands, and significantly higher PSD values in the 4 ~ 30 Hz range (P<0.000 1). SpEn analysis revealed significantly elevated spectral entropy across the sigma band in the IESS group, indicating a lack of dominant frequencies, increased complexity, reduced rhythmicity, and enhanced disorder. In contrast, healthy controls exhibited elevated SpEn in the alpha band, reflecting the physiological reduction or disappearance of dominant alpha rhythms during sleep. Conclusion Infants with IESS demonstrate distinct EEG characteristics in both PSD and SpEn analyses compared to healthy infants. These quantitative spectral features reflect the underlying abnormalities of EEG in IESS and provide objective insights that complement conventional visual assessment, offering a novel perspective for early diagnosis and therapeutic monitoring.
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.
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.