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.
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.
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 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.
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.
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 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.
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.
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 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.