Left bundle branch block (LBBB) during anaesthesia is uncommon. During general anaesthesia, LBBB may be related to hypertension or tachycardia and its acute onset makes the diagnosis of acute myocardial ischemia or infarction difficult. We would like to present a case report of a healthy patient who developed LBBB intra operatively. Acute LBBB should lead to suspicion of acute coronary syndrome until proven otherwise. Inability to exclude an acute cardiac event resulted in postponement of surgery twice after general anaesthesia was administered. Cardiological investigation of our patient showed physiological left ventricular hypertrophy (LVH), "athlete's heart" which was the most likely cause of the LBBB under anaesthesia.
Device occlusion of perimembranous ventricular septal defect is gaining popularity with the emergence of newer, softer occluders and improved technical know-how. We report a 26-year-old lady with a moderate size perimembranous ventricular septal defect who had a new onset of bundle branch block shortly after device closure. The patient subsequently developed a complete atrio-ventricular heart block.
Atrial septal defect (ASD) is the most common congenital heart disease observed in adult. Several ECG findings are considered sensitive for the diagnosis of ASD. We describe a 50 years old man who displayed Crochetage sign, incomplete right bundle branch block (IRBBB) and right ventricular strain pattern on ECG. Crochetage sign is highly specific for ASD and it correlates with shunt severity. The diagnostic specificity for ASD increases if the R waves have both Crochetage patterns and IRBBB. It is important not to confuse Crochetage signs with IRBBB abnormalities on ECG. Our patient was ultimately diagnosed with a large ASD measuring 3 cm with bidirectional shunt and concomitant pulmonary thrombosis. This illustrates that high suspicion of the ASD with the use of good-old ECG signs remains relevant in this modern era. This also reminds us that patients with Eisenmenger syndrome are at higher risk for pulmonary thrombosis.
Abnormality of the cardiac conduction system can induce arrhythmia - abnormal heart rhythm - that can frequently lead to other cardiac diseases and complications, and are sometimes life-threatening. These conduction system perturbations can manifest as morphological changes on the surface electrocardiographic (ECG) signal. Assessment of these morphological changes can be challenging and time-consuming, as ECG signal features are often low in amplitude and subtle. The main aim of this study is to develop an automated computer aided diagnostic (CAD) system that can expedite the process of arrhythmia diagnosis, as an aid to clinicians to provide appropriate and timely intervention to patients. We propose an autoencoder of ECG signals that can diagnose normal sinus beats, atrial premature beats (APB), premature ventricular contractions (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB). Apart from the first, the rest are morphological beat-to-beat elements that characterize and constitute complex arrhythmia. The novelty of this work lies in how we modified the U-net model to perform beat-wise analysis on heterogeneously segmented ECGs of variable lengths derived from the MIT-BIH arrhythmia database. The proposed system has demonstrated self-learning ability in generating class activations maps, and these generated maps faithfully reflect the cardiac conditions in each ECG cardiac cycle. It has attained a high classification accuracy of 97.32% in diagnosing cardiac conditions, and 99.3% for R peak detection using a ten-fold cross validation strategy. Our developed model can help physicians to screen ECG accurately, potentially resulting in timely intervention of patients with arrhythmia.
Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.
Cardiac arrhythmias are common in patients with thyrotoxicosis. Conduction abnormalities have been seen in a few thyrotoxic patients, but these, in particular high grade atrioventricular (AV) block, often occur in the presence of other conditions. Three thyrotoxic patients with conduction abnormalities are described: two were associated with severe hypokalaemia and the third had congestive cardiac failure. Conditions predisposing to conduction abnormality should be identified when this occurs in a thyrotoxic patient as their correction may help resolve or explain the conduction abnormality.
A 35-year-old multiparous woman was found unresponsive, tachypnoeic, hypoxic and in shock 4 h postpartum. The ECG revealed S1 Q3 T3, a right bundle branch block pattern and right-axis deviation. The computed tomography of her pulmonary arteries revealed bilateral pulmonary artery thrombosis with dilated right ventricle. She was fibrinolyzed with intravenous Tenecteplase 30 mg bolus. Her saturation and tachypnoea improved and her ECG reverted to sinus rhythm subsequently. We discuss our use of off-label Tenecteplase in postpartum pulmonary embolism and review the literature.