The role of electrocardiography (ECG) in pre-participation screening (PPS) has long been debated, but accumulating evidence supports its importance in detecting cardiovascular diseases associated with sudden cardiac death (SCD). Its systematic use has contributed to a reduction in SCD incidence among athletes. Over the past two decades, interpretation standards have evolved, culminating in the International Criteria, which classify ECG findings as normal, borderline, or abnormal. Training-related and vagal tone-related changes are recognized as physiological variants, borderline alterations represent intermediate findings that require careful contextual interpretation, and abnormal patterns constitute true red flags that always warrant further investigation. Additional challenges concern special populations, including children, women, and master athletes, where training-induced remodeling and ECG expression may differ from young male cohorts on which the current criteria are based. While the International Criteria remain the reference framework, future updates are needed to refine thresholds, incorporate emerging evidence, and adapt to diverse athletic populations, with the ultimate goal of improving diagnostic accuracy and preventing SCD.

Atrial depolarization is represented by the P wave on the electrocardiogram (ECG), while atrial repolarization (Ta wave) is usually obscured by the QRS complex. In patients with third-degree atrioventricular block, the Ta wave can be observed and studied. Although atrial repolarization has been described in healthy subjects and in those with paroxysmal atrial fibrillation, it has not been studied in left ventricular dysfunction.

The coexistence of atrial fibrillation (AF) and ventricular fibrillation (VF) in a structurally normal heart is a rare presentation. We describe a case of a previously healthy, 30-year-old man presenting with a family history of sudden cardiac death. The ECG during admission showed early repolarization pattern and he spontaneously developed AF, junctional rhythm and VF and dynamic QT shortening with prominent U waves during the arrhythmic storm. Genetic testing revealed novel heterozygous missense substitution p.Ile73Val in exon 2 of the CACNA1C gene.

Observational studies indicate a link between arrhythmias, electrocardiogram (ECG) characteristics, and non-alcoholic fatty liver disease (NAFLD). However, it is uncertain whether NAFLD acts merely as a risk marker or directly contributes to the development and progression of these arrhythmic disorders. Mendelian randomization (MR) was employed to investigate the causal relationship of NAFLD on arrhythmias and ECG traits.

To evaluate the diagnostic accuracy and clinical impact of 18‑lead electrocardiography compared with conventional 12‑lead ECG in patients with suspected acute coronary syndromes (ACS).

Early detection of interstitial septal fibrosis remains challenging. We describe a novel ECG pattern that may represent an early marker of fibrotic conduction changes.

To investigate errors in the assessment of ST-elevation (STEs) myocardial infarctions on ECGs to determine if non-conscious processes successfully detect missed STEs, as evidenced by changes in how long and often physicians look at leads with STEs.

One of the most difficult but important steps in assessing the fetus's health is the diagnosis of fetal cardiac abnormalities using fetal electrocardiograms (FECG). In order to provide accurate information regarding the fetus's condition, FECG monitoring is required. Severe fetal arrhythmia can cause heart failure or even death. This paper presents a Non-Causal Adaptive Filter that extracts the FECG through multiple error estimation. The maternal channel ECG in the chest will be used as the reference input, and the abdominal ECG will be used as the primary input for this filter. The clean FECG signals will be transformed into time-frequency (T-F) images using a fractional Stockwell transform after the FECG signals have been extracted. Using the Stockwell and fractional Fourier transforms, it can simultaneously display the time and fractional-frequency data in the time-fractional-frequency plane. The ability to detect fetal ECG arrhythmias with a clear physical interpretation is more significant. The resulting images are fed into the Atrous Convolutions aided Vision Transformer based Capsule Network (AConvVTCapNet) model, which detects fetal ECG arrhythmias. In this instance, Atrous convolutions efficiently compute dense feature maps, allowing the network to have wider receptive fields. The proposed model's parameters are adjusted using a new Opposition based Fire Hawk Optimization (OFHO) technique, which is carried out by the capsule network during the classification process. The proposed method obtained 98.23 % accuracy and 98.25 % specificity in the fetal ECG arrhythmia detection process.

Advancements in computerized electrocardiogram (ECG) interpretation have improved efficiency, but concerns remain regarding diagnostic accuracy for atrial fibrillation (AF). Misclassification may result in inappropriate treatment and adverse outcomes.

Electrocardiographic criteria for left ventricular hypertrophy (ECG-LVH) show poor diagnostic performance. Distance factors, such as the chest wall - left ventricle (CWLV) distance and subcutaneous adipose tissue (SAT) have been proposed as alternative modulators of ECG amplitudes both on a theoretical and a clinical basis. We aimed to investigate the independent associations of CWLV, SAT, and LVM with ECG amplitudes.

Many people died or were injured in two consecutive huge earthquakes in Turkey in February 2023. The earthquakes caused serious psychiatric disorders including post-traumatic stress disorder (PTSD). This study aimed to compare various electrocardiography (ECG) parameters including fragmented QRS (fQRS) and complete blood count (CBC) parameters including basophil to lymphocyte ratio (BLR) of earthquake-exposed with and without post-traumatic stress disorder (PTSD) subjects.

ST-segment depression in the electrocardiogram (ECG) of acute coronary syndrome (ACS) patients has been associated with higher mortality. But still, its association to sudden cardiac deaths (SCDs) or sudden cardiac arrests (SCAs) has not yet been investigated. We analyzed the association between ST-segment depression in the ECG after angiography and the long-term incidence of SCD and SCA among ACS patients.

We describe an unusual case of focal atrial tachycardia (FAT) from the left inferior pulmonary vein (PV) in a 12- year old boy treated successfully by high power short duration (HPSD) ablation. The patient was referred to our center for ablation of incessant FAT with recurrent transient degeneration into atrial fibrillation. The electrophysiological study revealed FAT with a micro-reentrant like activation from the left inferior PV. To reduce the risk of complications to surrounding structures, isolation of the left pulmonary veins was performed using HPSD (65 W, 6-15 s) ablation. During a follow-up period of 12 months, the patient remained in stable sinus rhythm.

Assessing pediatric ECG variations to enhance screening accuracy, thus helps address challenges in distinguishing normal developmental changes from potential cardiac pathology. This study aims to establish age- and sex-specific ECG reference patterns in healthy young athletes to improve clinical interpretation during pre-participation screening, thereby reducing false positives and optimizing referral practices.

Differences in electrocardiogram (EKG) measurements exist between cisgender men and cisgender women, with men typically exhibiting shorter QTc intervals, longer QRS durations, and longer PR intervals. Limited data describe the effects of gender-affirming hormone replacement therapy (HRT) on EKG parameters in transgender individuals.

We report a 26-year patient with univentricular physiology who underwent bidirectional Glenn with left pulmonary artery plasty followed by pulmonary artery stenting and finally underwent fenestrated extra-cardiac Fontan operation at age of 5 years. He was asymptomatic with an interesting ECG with atrial ectopic at presentation. We try to explain the basic mechanism and patterns of arrythmias in post-Fontan patients.

Electrocardiography (ECG) provides valuable information for early diagnosis of myocardial infarction. It is known that the atria receive their blood supply primarily from branches originating from the proximal segments of the coronary arteries. This study aimed to evaluate whether P wave parameters on admission ECG can help determine proximal versus distal right coronary artery (RCA) occlusion in patients presenting with inferior myocardial infarction (IMI).

Numerous electrocardiographic (ECG) criteria have been reported to predict the culprit artery in cases of inferior myocardial infarction (MI). This study aimed to identify the most reliable predictive criteria and assess their applicability in a cohort of patients with multivessel coronary disease.

Electrocardiogram (ECG) signal compression is paramount in continuously monitoring cardiac patients, as it reduces data storage and transmission costs. Deep neural networks, particularly autoencoders, offer significant potential for compressing ECG signals by mapping them to lower-dimensional spaces. This paper presents a novel multichannel convolutional autoencoder model designed to compress ECG signals efficiently. The proposed approach encodes the ECG signal into a four-channel lower-dimensional space using a convolutional encoder, which is subsequently reconstructed by a deconvolutional decoder. Unlike traditional autoencoder-based methods, the first channel in the model remains unconstrained, while increasing levels of sparsity constraints are imposed on the remaining channels. Different quantization levels are applied to each channel to optimize compression further, reflecting the varying numerical ranges caused by the sparsity constraints. The quantized channels are then encoded using Huffman coding, resulting in a higher compression ratio. The model's effectiveness is evaluated on a popular benchmark dataset, using normalized percent root mean square difference (PRDN) error and compression ratio as performance metrics. The proposed method achieves an average compression ratio of 20.23:1, with an average PRDN error of 9.86%, demonstrating its capability to compress ECG signals efficiently while maintaining reconstruction accuracy.