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.
Brugada syndrome is an inherited cardiac arrhythmia condition characterized by (i) coved ST-elevation and J point elevation of at least 2 mm in at least two of the right precordial ECG leads (V1-V3) and (ii) ventricular arrhythmias, syncope, and sudden death. Patients with Brugada syndrome or suspected mutation carriers can have normal ECG recordings at other times. In these cases, a diagnostic challenge with a sodium channel blocker such as ajmaline, flecainide, or pilsicainide may induce the full-blown type 1 ECG pattern and support the diagnosis. However, recently, many other pharmacological agents not related to class I anti-arrhythmic agents have been reported to induce Brugada ECG patterns including tricyclic antidepressants, fluoxetine, lithium, trifluoperazine, antihistamines, and cocaine. As published reports of the drug-induced Brugada sign have become increasingly prevalent, there is growing interest in the mechanisms responsible for this acquired ECG pattern and its clinical significance. It is possible that drug-induced Brugada syndrome may be due to an individual susceptibility that favours drug-induced ECG abnormalities, possibly as a result of an increase in a latent ion channel dysfunction similar to that in drug-induced long QT syndrome. However, further evidence is needed to confirm this postulation. In this paper, we will review the cases and evidence of drug-induced Brugada syndrome reported in the literature.
Objective: This case report highlights the risk of Torsade de Pointes (TdP), a life threatening cardiac arrhythmia in a heroin dependent patient receiving methadone substitution therapy who was prescribed erythromycin for upper respiratory tract infection. Method: We report a case of a 35-year-old Malay man on methadone maintenance treatment who developed TdP possibly due to drug interaction between methadone and erythromycin. Results: The
patient reported feeling unwell, chest pain and feeling dizzy after consuming 2 doses of erythromycin. ECG monitoring showed prolonged rate-corrected QT interval leading to TdP. The patient was admitted to the ward where the cardiac arrhythmia ceased following methadone discontinuation. This cardiac arrhythmia was most likely due to drug interaction between methadone and erythromycin (an enzyme inhibitor) which led to an increase in methadone concentration and potentiated the adverse effects. Conclusion: As methadone is a beneficial treatment for heroin dependent patients, the risk of cardiac arrhythmia is of great concern. To avoid complications of drug interaction, patients on methadone therapy should be advised to seek medical assessment before taking other drugs. As TdP is life threatening, it is thus important that physicians and psychiatrists involved in the treatment of
heroin dependent patients on methadone substitution therapy be made aware of this risk.