Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

Separating ventricular activity in thoracic EIT using 4D image-based FEM simulations

One challenge in central hemodynamic monitoring based on electrical impedance tomography (EIT) is to robustly detect ventricular signal components and the corresponding EIT image region without external monitoring information. Current stimulation and voltage measurement of EIT were simulated with finite element porcine torso models in presence of a multitude of thoracic blood volume shifts. The simulated measurement data was examined for linear dependence on changes in stroke volume. Based on the results the EIT measurement information regarding stroke volume changes is sparse

Novelties in cardiac pacing. Left bundle branch pacing, a step-by-step guide

Left bundle branch pacing (LBBP) technique is a new method for conduction system pacing that is useful for both bradyarrhythmia and heart failure indications. LBBP, while less physiological than His bundle pacing, offers several practical advantages. Namely, lower and stable pacing thresholds, good sensing of the intrinsic ventricular activity and easiness in localizing the pacing target. The LBBP method more often than His bundle pacing results in engagement of the conduction system distal to the area of the block. A step-by-step approach to LBBP was described. Attention was given to the following phases of the procedure: 1) localization of the target area on the septum, 2) the lead rotation technique with an interpretation of the lead responses (drill effect, screwdriver effect, entanglement effect), 3) methods for monitoring the lead depth in the septum to avoid perforation (fixation beats, continuous pace mapping, impedance), and 4) methods to differentiate between LBBP and left ventricular septal pacing.

Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques

Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.

Arrhythmias and Conduction Disturbances in Autoimmune Rheumatic Disorders

Rhythm and conduction disturbances and sudden cardiac death are important manifestations of cardiac involvement in autoimmune rheumatic diseases (ARD), which have a serious impact on morbidity and mortality. While the underlying arrhythmogenic mechanisms are multifactorial, myocardial fibrosis plays a pivotal role. It accounts for a substantial portion of cardiac mortality and may manifest as atrial and ventricular arrhythmias, conduction system abnormalities, biventricular cardiac failure or sudden death. In patients with ARD, myocardial fibrosis is considered to be the hallmark of cardiac involvement as a result of inflammatory process or to coronary artery occlusive disease. Myocardial fibrosis constitutes the pathological substrates for reentrant circuits. The presence of supraventricular extra systoles, tachyarrhythmias, ventricular activity and conduction disturbances are not uncommon in patients with ARDs, more often in systemic lupus erythematosus, systemic sclerosis, rheumatoid arthritis, inflammatory muscle disorders and anti-neutrophil cytoplasm antibody-associated vasculitis. In this review, the type, the relative prevalence and the underlying mechanisms of rhythm and conduction disturbances in the emerging field of cardiorheumatology are provided.

Epicardial voltage mapping in patients with postinfarction ventricular tachycardia: a pilot study

Introduction. Radiofrequency ablation (RFA) is an established treatment of post-myocardial infarction ventricular tachycardia (VT). Endocardial VT ablation can be insufficient for VT termination when the scar is intramural/epicardial.Purpose: to assess the extent of epicardial electrophysiological VT substrate in patients with remote myocardial infarction.Materials and methods. Thirteen patients with sustained postinfarction VT, who signed an informed consent, were included into the study. All patients underwent full clinical evaluation. Electroanatomical voltage bi- and unipolar mapping of endocardial and epicardial surfaces was performed. Maps were evaluated for the presence of low-voltage areas and local abnormal ventricular activity (LAVA). RFA was performed at LAVA sites. The end-point of the procedure was scar LAVA abolition and VT noninducibility (procedure success). VT recurrence was detected using an implantable cardioverter-defibrillator and/or ECG monitoring.Results. Epicardial access was successful in 12 patients. Epicardial access was performed at a first procedure in 7 patients, 4 patients had a history of previous endocardial ablation. Epicardial LAVA sites were detected in 9 patients. Endocardial and epicardial arrhythmogenic substrate localization coincided in 8 patients. One patient had only epicardial scar, 1 patient had only septal endocardial scar. In one patient LAVA sites had different localizations on epicardial and endocardial maps. Acute ablation success was noted in 12 patients.Conclusion. In our patient group transmural scar and epicardial electrophysiological arrhythmogenic substrate was detected in 82% of cases. Isolated endocardial ablation may be unsuccessful, in such cases epicardial mapping and ablation might be useful.