Fetal arrhythmias are mostly benign and transient. However, some of them are associated with structural defects or can cause heart failure, fetal hydrops, and can lead to intrauterine death. The analysis of fetal heart rhythm is based on ultrasound (M-mode and Doppler echocardiography). Irregular rhythm due to atrial ectopic beats is the most common type of fetal arrhythmia and is generally benign. Tachyarrhythmias are diagnosed when the fetal heart rate is persistently above 180 beats per minute (bpm). The most common fetal tachyarrhythmias are paroxysmal supraventricular tachycardia and atrial flutter. Most fetal tachycardias can be terminated or controlled by transplacental or direct administration of anti-arrhythmic drugs. Fetal bradycardia is diagnosed when the fetal heart rate is slower than 110 bpm. Persistent bradycardia outside labor or in the absence of placental pathology is mostly due to atrioventricular (AV) block. Approximately half of fetal heart blocks are in cases with structural heart defects, and AV block in cases with structurally normal heart is often caused by maternal anti-Ro/SSA antibodies. The efficacy of prenatal treatment for fetal AV block is limited. Our review aims to provide a practical guide for the diagnosis and management of common fetal arrythmias, from the joint perspective of the fetal medicine specialist and the cardiologist.

Fetal arrhythmia refers to an abnormal heart rhythm in a fetus, characterized by irregular, too fast (tachycardia), or too slow (bradycardia) heartbeats. Diagnosis is typically made using ultrasound or non-invasive fetal electrocardiography (NI-FECG), which monitors the electrical activity of the fetal heart. Accurate detection and management are crucial, as severe arrhythmias may lead to complications that affect the fetus’s health during pregnancy and delivery. This study aims to develop an effective screening method for detecting arrhythmia (ARR) to aid physicians in diagnosing potential heart disease in fetal during pregnancy. This study presents a new cross-domain feature extraction method that incorporates temporal relationships between consecutive windows, improving feature representation by examining the correlation between neighboring windows. First, the original waveform signals from six sensors were transformed into a multi-level decomposition using the HAAR wavelet. Subsequently, a sample expansion was applied using a various-sized window sliding approach to each ARR and normal signal. Second, feature selection was implemented to reduce data dimensionality by selecting features highly relevant to the class labels. Finally, we applied oversampling techniques to address the issue of imbalanced data. Based on the results of an in-depth experimental analysis, it was found that the application of window sampling for data expansion produced favorable outcomes, particularly at a window size of 500. The combination of dimensionality reduction using Mutual Information and oversampling with the Radius-SMOTE method achieved the highest performance, yielding an accuracy of 96.5%, precision of 95.2%, recall of 96.3%, and an F1-measure of 96.2%, while reducing the feature dimensionality to 100. Moreover, the proposed method demonstrated improved performance compared to state-of-the-art approaches. With further development, this method is expected to serve as a foundational model for the advancement of diagnostic tools for physicians.

Abstract Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia characterized by consistently irregular atrial and ventricular contractions. Heart rate variability (HRV) refers to the changes in the intervals between consecutive ventricular heartbeats. In sinus rhythm, HRV may be subtle and is quantitatively reflecting the dynamic interplay of the cardiac autonomic nervous system, which plays a crucial role in the onset, development, and maintenance of AF. HRV metrics, consisting of time-domain, frequency-domain, and nonlinear parameters, have been verified to vary significantly before and after AF episodes, and AF treatment-related procedures such as electrical cardioversion, ablation, and surgery of AF. Therefore, HRV may serve as a digital biomarker in predicting AF risk in long-term and acute risk period, identification of patients with AF risk in sinus rhythm and recurrence risk stratification after procedures. HRV in AF rhythm, predominantly influenced by dynamic atrioventricular node conduction under the onslaught of irregular atrial impulses, shows a huge disparity compared to that in sinus rhythm. Despite this, HRV in AF rhythm still provides valuable prognostic information, as reduced HRV may indicate a poor heart function and outcomes in patients with AF. Despite being influenced by lots of variables, HRV can still serve as an independent digital biomarker in the clinical management of AF throughout its entire lifecycle.

Cardiac arrhythmias, characterized by irregular heart function, disrupt normal blood circulation and are commonly detected using electrocardiograms (ECGs). ECG is widely preferred due to its cost-effectiveness, ease of application, and high reliability in diagnosing heart rhythm disorders. To enhance diagnostic efficiency, automatic arrhythmia detection systems have been increasingly developed. In this study, we propose a novel deep learning-based classification framework that integrates a Modified Spindle Autoencoder (MSCAE) with a Convolutional Neural Network (CNN). Unlike traditional autoencoders, the Spindle Autoencoder utilizes deeper and symmetric hidden layers to extract complex and meaningful representations from ECG signals. These learned features are then analyzed by the CNN to capture spatial relationships through its convolutional layers. The proposed model was trained and evaluated on the MIT-BIH Arrhythmia Database, which contains both normal and abnormal heartbeat recordings. The ECG signals were preprocessed, segmented into individual beats, and subjected to feature extraction to improve classification performance. The integrated MSCAE-CNN architecture achieved an accuracy of 98.78 % under various experimental conditions. Performance metrics demonstrate that the proposed method outperforms existing approaches in classifying arrhythmias. These results underline the clinical potential of the model in providing rapid and accurate ECG-based arrhythmia detection for medical decision-making.

Dysrhythmia is a term referring to the occurrence of spontaneous and repetitive changes in potentials with parameters deviating from those considered normal. The term refers to heart anomalies but has a broader meaning. Dysrhythmias may concern the heart, neurological system, digestive system, and sensory organs. Ion currents conducted through ion channels are a universal phenomenon. The occurrence of channel abnormalities will therefore result in disorders with clinical manifestations depending on the affected tissue, but phenomena from other tissues and organs may also manifest themselves. A similar problem concerns the implementation of pharmacotherapy, the mechanism of which is related to the impact on various ion currents. Treatment in this case may cause unfavorable effects on other tissues and organs. Drugs acting through the modulation of ion currents are characterized by relatively low tissue specificity. To assess a therapy's efficacy and safety, the risk of occurrences in other tissues with similar mechanisms of action must be considered. In the present review, the focus is shifted prominently onto a comparison of abnormal electrical activity within different tissues and organs. This review includes an overview of the types of dysrhythmias and the basic techniques of clinical examination of electrophysiological disorders. It also presents a concise overview of the available pharmacotherapy in particular diseases. In addition, the authors review the relevant ion channels and their research technique based on patch clumping.

AFib or A-fib) is an abnormal heart rhythm (arrhythmia) characterized by rapid and irregular beating of the atrial chambers of the heart. It often begins Atrial fibrillation (AF, AFib or A-fib) is an abnormal heart rhythm (arrhythmia) characterized by rapid and irregular beating of the atrial chambers of the heart. It often begins as short periods of abnormal beating, which become longer or continuous over time. It may also start as other forms of arrhythmia, such as atrial flutter, that transform into AF. Episodes can be asymptomatic. Symptomatic Atr… Atrial fibrillation is diagnosed on an electrocardiogram (ECG), an investigation performed routinely whenever an irregular heartbeat is suspected. Characteristic findings are the absence of P waves, with disorganized electrical activity in their place, and irregular R–R intervals due to irregular conduction of impulses to the ventricles. At very fast heart rates, atrial fibrillation may look more regular, which may make it more difficult to separate from other supraventricular tachycardias or ventricular tachycardia. QRS complexes should be narrow, signifying that they are initiated by normal conduction of atrial electrical activity through the intraventricular conduction system. Wide QRS complexes are worrisome for ventricular tachycardia, although, in cases where there is a disease of the conduction system, wide complexes may be present in A-fib with a rapid ventricular response. If paroxysmal AF is suspected, but an ECG during an office visit shows only a regular rhythm, AF episodes may be detected and documented with the use of ambulatory Holter monitoring (e.g., for a day). If the episodes are too infrequent to be detected by Holter monitoring with reasonable probability, then the person can be monitored for longer periods (e.g., a month) with an ambulatory event monitor.

Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. As a result, we present a significant approach for identifying arrhythmias using ECG signals. In this study, we proposed an approach based on Deep Learning (DL) technology that is a framework of nine-layer one-dimension Conventional Neural Network (1D CNN) for classifying automatically ECG signals into four cardiac conditions named: normal (N), Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The practical test of this work was executed with the benchmark MIT-BIH database. We achieved an average accuracy of 99%, precision of 98%, recall of 96.5%, specificity of 99.08%, and an F1-score of 95.75%. The obtained results were compared with some relevant models, and they showed that the proposed

cardiac pacemaker provides an inadequate or irregular heartbeat, or because there is a block in the heart's electrical conduction system. Modern pacemakers are A pacemaker, also known as an artificial cardiac pacemaker, is an implanted medical device that generates electrical pulses delivered by electrodes to one or more of the chambers of the heart. Each pulse causes the targeted chamber(s) to contract and pump blood, thus regulating the function of the electrical conduction system of the heart. The primary purpose of a pacemaker is to maintain an even A…

We present the results from a white-box machine learning approach to detect cardiac arrhythmias using electrocardiographic data. A C5.0 is trained to recognize four classes using common features. The four classes are (i) atrial fibrillation and atrial flutter, (ii) tachycardias (iii), sinus bradycardia and (iv) sinus rhythm. Data from 10,646 subjects, 83% of whom have at least one arrhythmia and 17% of whom exhibit a normal sinus rhythm, are used. The C5.0 is trained using 10-fold cross-validation and is able to achieve a balanced accuracy of 95.35%. By using the white-box machine learning app