Qrs-Complex Fragmentations and Right Ventricular Infarction in the Presence of Inferior Infarction with Triple-Vessels Disease; Bad Initials but a Good Outcome

Rationale: The term “fragmentation of the QRS complex” denotes the existence of high-frequency potentials (spikes) in the QRS-complex. It is either a marker for cardiac structural diseases inducing biventricular hypertrophy or any condition interfering with the normally homogeneous depolarization status inside the myocardium. An associated right ventricular infarction with inferior infarction maybe carry a risk impact and serious complications. Patient concerns: A 64-year-old married, farmer, heavy smoker, Egyptian male patient presented with acute severe chest pain and inferior with right ventricular ST-segment elevation myocardial infarction and fragmentation of the QRS complex. Diagnosis: QRS-complex fragmentations and right ventricular infarction in the presence of inferior infarction with the triple-vessels disease. Interventions: Electrocardiography, oxygenation, streptokinase intravenous infusion, echocardiography, and percutaneous transluminal coronary angioplasty. Outcomes: Dramatic response of acute inferior with right ventricular ST-segment elevation myocardial infarction and QRS-complex fragmentations to streptokinase. Lessons: Despite the presence of inferior and right ventricular ST-segment elevation myocardial infarction with QRS-complex fragmentations, but there is no correlation with the severity of the disease. Dramatic clinical and electrocardiographic response signifying the role of streptokinase and fibrinolytic. The presence of fragmentation of the QRS-complex may have a bidirectional impact from seriousness to complications.

Research on the duration of electrocardiogram components in pregnant goats

The electrocardiogram is a graph recording of heart’s electric activity, so it is used in medical practice mainly in order to observe the heart’s activity. The values of the main components of the electrocardiogram in pregnant goats were determined within the current research. All of these were performed in three different stages of pregnancy (the beginning, the middle and the ending), being focused on the variation of these values during the pregnancy. The gestation diagnosis was confirmed by ultrasound-exam. During the pregnancy, the following values for the duration of the main ECG’s components were found: the P wave (0.045-0.044 s), the P-R segment (0.061-0.048 s), of the P-R range (0.105-0.086 s), of the QRS complex (0.042-0.040 s), of the Q-T range (0.242-0.218 s), of the P-T range (0.377-0.368 s), of the R-R range (0.465-0.431 s), the T wave (0.091-0.104 s) and of the T-P segment (0.097-0.101 s). Our results show that during the pregnancy the duration of: the P wave, the P-R segment, the P-R range, the QRS complex, the Q-T range, the P-T range and the R-R range, decrease, while the duration of the T wave and the T-P segment increase.

ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals

In healthcare, diagnostic tools of cardiac diseases are commonly known by the electrocardiogram (ECG) analysis. Atypical electrical activity can produce a cardiac arrhythmia. Various difficulties can be imposed to clinicians e.g., myocardial infarction arrhythmia via the non-stationarity and irregularity heart beat signals. Through the assistance of computer-aided diagnosis methods, timely specification of arrhythmia diseases reduces the mortality rate of affected patients. In this study, a 1 Lead QRS complex -layer deep convolutional neural network is proposed for the recognition of arrhythmia datasets. By the use of this CNN model, we planned a complete structure of the classification architecture after a pre-processing stage counting the denoising and QRS complex signals detection procedure. The chief benefit of the new proposed methodology is that the automatically training the QRS complexes without requiring all original extracted ECG signals. The proposed model was trained on the increased ECG database and separated into five classes. Experimental results display that the established CNN method has improved performance when compared to the state-of-the-art studies.

Discrete Cosine Transform based ECG Signal Analysis and Processing

Cardiovascular disease (CADs) is considered the primary leading cause of death. Irregular activity of heart, these disease can be detected and classified by Electrocardiogram (ECG), which is constructed from using electrodes placed on human skin to record the electrical activity of the heart. Because QRS complex represents the basic part of the ECG signal, these components should be recognized in order to analysis the other characteristics of the signal. Different methods and algorithms are proposed to analysis and processing the ECG signal. In this paper, a new QRS complex recognition method are proposed based on discrete cosine transform (DCT) with variable adaptive threshold method, which is used to determine threshold based on characteristic of each ECG signal to detect upper and lower levels of threshold to detect the peak of the signal. At first, the DCT is applied to the ECG signal to isolate it into different coefficients and eliminate or reduce the noises of the signal based on processing of high frequency components of DCT coefficients, which have less information, then the ECG is reconstructed by cropping the most important coefficients to be used in threshold determination. The basic idea is that the reconstructed signal have high differences between the components of the signal, and this facilitates the process of calculating the threshold value, which is used later to find peaks of ECG signal. The proposed method is tested and its performance are determined based on three different datasets, which are MITBIH Arrhythmia dataset, (LTSTDB) and (EDB) and the performance are evaluated using different metrics, which are Detection rate, accuracy, specificity and sensitivity. The experimental results show that the proposed method is performed or outperformed other works, therefore it can be used in peak detection applications.

A Fully-Coupled Electro-Mechanical Whole-Heart Computational Model: Influence of Cardiac Contraction on the ECG

The ECG is one of the most commonly used non-invasive tools to gain insights into the electrical functioning of the heart. It has been crucial as a foundation in the creation and validation of in silico models describing the underlying electrophysiological processes. However, so far, the contraction of the heart and its influences on the ECG have mainly been overlooked in in silico models. As the heart contracts and moves, so do the electrical sources within the heart responsible for the signal on the body surface, thus potentially altering the ECG. To illuminate these aspects, we developed a human 4-chamber electro-mechanically coupled whole heart in silico model and embedded it within a torso model. Our model faithfully reproduces measured 12-lead ECG traces, circulatory characteristics, as well as physiological ventricular rotation and atrioventricular valve plane displacement. We compare our dynamic model to three non-deforming ones in terms of standard clinically used ECG leads (Einthoven and Wilson) and body surface potential maps (BSPM). The non-deforming models consider the heart at its ventricular end-diastatic, end-diastolic and end-systolic states. The standard leads show negligible differences during P-Wave and QRS-Complex, yet during T-Wave the leads closest to the heart show prominent differences in amplitude. When looking at the BSPM, there are no notable differences during the P-Wave, but effects of cardiac motion can be observed already during the QRS-Complex, increasing further during the T-Wave. We conclude that for the modeling of activation (P-Wave/QRS-Complex), the associated effort of simulating a complete electro-mechanical approach is not worth the computational cost. But when looking at ventricular repolarization (T-Wave) in standard leads as well as BSPM, there are areas where the signal can be influenced by cardiac motion of the heart to an extent that should not be ignored.

Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset

Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw ECG signal, entropy-based features computed from raw ECG signals, extracted QRS complexes, and entropy-based features computed from extracted QRS complexes. The tests were based on the classification of 2, 5, and 20 classes of heart diseases. The research was carried out on the data contained in a PTB-XL database. An innovative method of extracting QRS complexes based on the aggregation of results from established algorithms for multi-lead signals using the k-mean method, at the same time, was presented. The obtained results prove that adding entropy-based features and extracted QRS complexes to the raw signal is beneficial. Raw signals with entropy-based features but without extracted QRS complexes performed much worse.

49 A case report of cardiac contractility modulation therapy for heart failure treatment in patient with amyloidosis: is to be considered a valid therapeutic option?

Aims Cardiac amyloidosis (CA) is primarily associated with fibril deposits in many cardiac structures, causing biventricular wall thickness and stiffness. CA may result in arrhythmias and particularly in an aggressive form of heart failure (HF). Cardiac contractility modulation (CCM) showed to be a concrete therapeutic option in patients with symptomatic HF despite optimal medical therapy (OMT), with Left Ventricular Ejection Fraction (LVEF) between 25% and 45%, with narrow QRS complex (<130 ms). This case aims to further explore the effectiveness of CCM therapy in a patient affected by concomitant ischaemic cardiomyopathy and CA. Methods and results A 42-year-old man with Chronic HF secondary to both post-ischaemic due to spontaneous coronary artery dissection (SCAD) and post alcoholic dilated cardiomyopathy was hospitalized at our department in February 2020 due to worsening HF (3rd HF hospitalization in the same year). The patient was a NYHA class III, with chronic kidney failure, a narrow QRS complex (100 ms) and a LVEF of 27% with familiar history of sudden death, already implanted with ICD. The patient resulted untreatable with sacubitril/valsartan, as it elicited strong hypotension. During current hospitalization the BNP value was 942.60 pg/ml, and the Quality of Life (QoL) evaluated from Minnesota Living with Heart Failure Questionnaire (MLHFQ) score was 72 points. Moreover, the patient underwent umbilical biopsy that confirmed the presence of amyloidosis. Thus, the CCM therapy device (Optimizer® Smart, Impulse Dynamics) was implanted to try to reduce HF symptoms and hospitalizations. The therapy was programmed for 10 h per day, with delivery of CCM from both septal leads with amplitude of 6.5 V at 20.56 ms pulses duration. Figure 1A and B shows the septal position of leads and a surface ECG with the CCM therapy spike after QRS. The patient significantly improved as early as the first period after implantation. The 10-month in-office FU performed on December 2020 revealed in addition to the absence of new HF hospitalizations, a significant improvement in QoL and HF-symptoms, with a MLWHFQ score of 42, an enhancement to NHYA class II, and even a slight decrease of BNP of 767 pg/ml. The echo exam revealed no significant changes in the EF, with an improvement of global longitudinal strain and no worsening of other haemodynamic parameters. A further FU performed in June 2021 showed continuous improvement of QoL with a MLWHFQ score of 25 e no HF hospitalizations. Conclusions In this patient affected by multiple cardiomyopathies, including CA, CCM therapy proved to improve its QoL with no HF hospital admissions since the implantation. The absence of significant echocardiographic worsening is a positive aspect, considering the patient’s status, the concomitant aetiologies, and the presence of amyloidosis, given its progressive and infiltrative nature.