QTc Interval is Associated with Atrial Fibrillation in Individuals with Metabolic Syndrome Phenotype.

Purpose: Manifestations of metabolic syndrome (MetS) carry risks for atrial fibrillation (AF). The study determined whether any electrocardiographic parameter can reflect increased AF risk in individuals with MetS. Patients and Methods: From our University Hospital database, we examined the presence of AF and its correlation with MetS manifestations, renal function, lipid profiles, and electrocardiographic parameters (P wave duration, PR interval, QRS width, and QTc intervals). Between January 2008 and December 2015, data from 4479 adults (women 41.6% vs men 58.4%) were identified. Results: The overall prevalence of AF was 12.4%, without sex differences (women, 12.8% vs men, 12.1%). Patients with AF were older (age 73.9 ± 11.8 vs non-AF 67 ± 13.5 years), with lower lipid levels (TG, total cholesterol, and LDL-cholesterol, all p < 0.0001), and at lower eGFR level (64.1 ± 30.9 vs non-AF 68.8 ± 41.4 mL/min/1.73m2, p < 0.0001). Besides, sex differences were present in all electrocardiographic parameters (all p < 0.05). Hypertension had the highest odds ratio (1.33; p = 0.026) for AF. Comparing AF to non-AF, the QTc of quartiles was significantly different (p < 0.0089). The shortest and longest QTc quartiles had an increased incidence of AF. Conclusion: AF risk in patients with MetS phenotypes can be reflected by QTc quartiles.

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the atria, and may cause heart thrombosis, stroke, arterial disease, thromboembolism, and heart failure. Prior to the onset of atrial fibrillation, most people experience atrial cardiomyopathy which, if effectively managed, can be prevented from progressing to atrial fibrillation. Electrocardiogram (ECG) can show changes in the heartbeats, and is a common and painless tool to detect heart problems. P-waves in exercise ECGs change more drastically than those in regular ECG, and are more effective in the detection of atrial myocardial diseases. In this paper, we propose a deep learning system to help clinicians to early detect if a patient has atrial enlargement or fibrillation. Firstly, a Convolutional Recurrent Neural Network is employed to locate the P-waves in the patient's exercise ECGs taken in the exercise ECG test process. Relevant parameters are then calculated from the located P-waves. Then a Parallel Bi-directional Long Short-Term Memory Network is applied to analyze the obtained parameters and make a diagnosis for the patient. With our proposed deep learning system, the changes of P-waves collected in different phases in the exercise ECG test can be analyzed simultaneously to get more stable and accurate results. The system can take data of different length as input, and is also applicable to any number of ECG collections. We conduct various experiments to show the effectiveness of our proposed system. We also show that the more ECG data collected in the exercise phase are involved, the more effective our system is in diagnosis of the diseases.