Hybrid multilayered perceptron network for classification of bundle branch blocks

Bundle branch and intraventricular block in acute coronary artery occlusion

ECG Interpretation Part 2: Determination of Bundle Branch and Fascicular Blocks

BACKGROUNDCoronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a worldwide health crisis since it first appeared. Numerous studies demonstrated the virus’s predilection to cardiomyocytes; however, the effects that COVID-19 has on the cardiac conduction system still need to be fully understood.AIMTo analyze the impact that COVID-19 has on the odds of major cardiovascular complications in patients with new onset heart blocks or bundle branch blocks (BBB).METHODSThe 2020 National Inpatient Sample (NIS) database was used to identify patients admitted for COVID-19 pneumonia with and without high-degree atrioventricular blocks (HDAVB) and right or left BBB utilizing ICD-10 codes. The patients with pre-existing pacemakers, suggestive of a prior diagnosis of HDAVB or BBB, were excluded from the study. The primary outcome was inpatient mortality. Secondary outcomes included total hospital charges (THC), the length of hospital stay (LOS), and other major cardiac outcomes detailed in the Results section. Univariate and multivariate regression analyses were used to adjust for confounders with Stata version 17.RESULTSA total of 1058815 COVID-19 hospitalizations were identified within the 2020 NIS database, of which 3210 (0.4%) and 17365 (1.6%) patients were newly diagnosed with HDAVB and BBB, respectively. We observed a significantly higher odds of in-hospital mortality, cardiac arrest, cardiogenic shock, sepsis, arrythmias, and acute kidney injury in the COVID-19 and HDAVB group. There was no statistically significant difference in the odds of cerebral infarction or pulmonary embolism. Encounters with COVID-19 pneumonia and newly diagnosed BBB had a higher odds of arrythmias, acute kidney injury, sepsis, need for mechanical ventilation, and cardiogenic shock than those without BBB. However, unlike HDAVB, COVID-19 pneumonia and BBB had no significant impact on mortality compared to patients without BBB.CONCLUSIONIn conclusion, there is a significantly higher odds of inpatient mortality, cardiac arrest, cardiogenic shock, sepsis, acute kidney injury, supraventricular tachycardia, ventricular tachycardia, THC, and LOS in patients with COVID-19 pneumonia and HDAVB as compared to patients without HDAVB. Likewise, patients with COVID-19 pneumonia in the BBB group similarly have a higher odds of supraventricular tachycardia, atrial fibrillation, atrial flutter, ventricular tachycardia, acute kidney injury, sepsis, need for mechanical ventilation, and cardiogenic shock as compared to those without BBB. Therefore, it is essential for healthcare providers to be aware of the possible worse predicted outcomes that patients with new-onset HDAVB or BBB may experience following SARS-CoV-2 infection.

Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low‐cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K‐Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.

The relative risk of spontaneous complete atriventricular block in elderly patients with impaired intra-ventricular conduction

Artificial Intelligence aims to mimic the human cognitive powers. AI is also into healthcare where it's been used for automating the diagnosis processes powered by increased availability of healthcare data. This research work develops an application, which can be used in diagnostic centers for multiple diseases diagnosis like Arrhythmia classification using ECG Image analysis, Malaria using Cell images, Brain tumor detection using MRI Scans and many more. At Present, this research work attempts to develop an Arrhythmia classification using ECG Image analysis and would add other diagnosis later on. Basically, ECG signals are used for analysis of Arrhythmia. The irregularities of heart beat are commonly called Arrhythmia. Here, a Convolutional Neural Network model is proposed for the classification of signals from ECG images to different classes. There are many different classes which are normal beat, Premature ventricular contraction beat, paced beat, atrial bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat, etc.

Pathology of the conduction system in acquired heart disease complete right bundle branch block

This paper presents a novel diagnosis method for the classification of cardiac arrhythmia based on electrocardiogram (ECG). The recognition of abnormal heart beat was performed using the context-independent hidden Markov model (HMM) and context-dependent HMM. Gaussian mixture model (GMM) was used as the state emission probability density function. The input feature of the recognition system was the raw heartbeat waveform. There were no other feature transform steps. Six classes of heartbeats, including normal beat, paced beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and ventricular escape beat, were categorized by the system. The performance was evaluated on the MIT-BIH Arrhythmia Database and the European ST-T Database. The experimental results showed that the context-dependent HMM was more precise than the context-independent HMM. The context-dependent HMM system achieved a mean level of 95.26%, 90.67% and 95.60% in heart beats recognition accuracy, sensibility and specificity respectively. The results confirmed the importance of the generative model and the raw waveform feature strategy in ECG study.

Multi-lead ECG heartbeat classification of heart disease based on HOG local feature descriptor

Electrophysiological investigations were performed in 30 patients with symptomatic sinus node disease (SND) to assess the extent and distribution of associated functional disturbances in the conduction system. The tests were performed before and after inhibition of autonomous tone with propranolol, 0.1 mg/kg, and atropine, 0.02 mg/kg. Surface ECG had shown bundle branch blocks (BBB) in 5 patients and fascicular blocks in 2. AV block I had been recorded in 4 patients, while none had shown high-degree AV block. Malfunction was most often detected in the AV junction, 17 patients showing a prolonged conduction time or an abnormal effective AV node refractory period. Intraventricular conduction delay was present in 7 patients, with a prolonged HV interval in 3 and a complete permanent BBB in the others. Rate-dependent BBBs were demonstrated in a further 4 patients. Long cardiac arrests following interruption of atrial pacing, suggesting impaired automaticity also of subsidiary escape pacemakers, were seen in 11 patients. Only 6 patients, 20%, showed no signs of associated malfunction of the conduction system. Thus, detailed electrophysiological assessment demonstrated associated conduction abnormalities in the majority of these SND patients. The results agree with histopathological studies and show that sinus node malfunction is often the clinically apparent manifestation of a widespread degenerative process in the cardiac conduction system.

Shallow stitching close to the rim of the ventricular septal defect eliminates injury to the right bundle branch

The cardiac conduction system in progressive systemic sclerosis: Clinical and pathologic features of 35 patients

In this paper, QRS morphological features and the artificial neural network method was used for Electrocardiogram (ECG) pattern classification. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. Authors propose a set of six ECG morphological features to reduce the feature vector size considerably and make the training process fast in addition to a simple but effective ECG heartbeat extraction scheme. Three types of artificial neural network models, MLP, RBF neural networks and SOFM were separately trained and tested for ECG pattern recognition and the experimental results of the different models have been compared. The MLP network exhibited the best performance and reached an overall test accuracy of 99.65%, and RBF and SOFM network both reached 99.1%. The performance of these classifiers was also evaluated in presence of additive Gaussian noise. MLP network was found to be more robust in this respect.

Automatic ECG beat classification plays an important role in detecting cardiac disease. In this paper, we propose an automatic recognition model for ECG signals based on discrete wavelet transform (DWT), principal component analysis (PCA), kernel principal component analysis (KPCA), and adaptive kernel principal component analysis (AKPCA). We extracted different ECG features using DWT, PCA, KPCA, and AKPCA, respectively. These features were combined and used as support vector machine (SVM) input to classify the ECG. ECG records taken from the MIT-BIH arrhythmia database are selected to test the proposed method. The following five heartbeat types were classified using this method: normal beats (N), premature ventricular beats (V), right bundle branch block beats (R), left bundle branch block beats (L), and premature atrial beats (A). The sensitivity, accuracy, precision, and specificity reached 99.95%, 99.86%, 99.53%, and 99.70%, respectively. These results indicate the proposed method is reliable and efficient for ECG beat classification.

The electrocardiogram (ECG) is a diagnostic procedure that uses a skin electrode to record the heart's electrical activity. Heart diseases are the leading cause of mortality globally, and they have significant monetary cost. In the clinic, automated detection and classification technology of abnormalities in heart beats can assist physicians in making prompt and accurate medical diagnoses. This paper concentrates on two proposed models: Model A: 1-D 8-Layer CNN Model and Model B: 1- D CNN and the LSTM model to characterise ECG data into five categories: normal beat, right bundle branch block beat, left bundle branch block beat, premature ventricular contraction beat, and atrial premature beat. The data used to create and validate the models is obtained from the MIT-BIH Dataset, which comprises of 48 half-hour samples of two-lead continuous ECG signals obtained from 48 individuals. The dataset is divided into two files-.csv and.txt. For each sample, the.csv files include the readings collected from both leads. The experimental results show that Model A has an Accuracy = 99.68%, Precision = 99.23%, and a F1 score = 99.22%. And Model B has an Accuracy= 99.51%, Precision = 98.76%, and a F1 score = 98.76%. Our study aims to assist the medical sector by reducing the diagnoses time by automating the process of detecting arrythmias.