Comparison of electrocardiograms (ECG) waveforms and centralized ECG measurements between a simple 6-lead mobile ECG device and a standard 12-lead ECG.

Background: Interval duration measurements (IDMs) were compared between standard 12-lead electrocardiograms (ECGs) and 6-lead ECGs recorded with AliveCor's KardiaMobile 6L, a hand-held mobile device designed for use by patients at home.

Methods: Electrocardiograms were recorded within, on average, 15 min from 705 patients in Mayo Clinic's Windland Smith Rice Genetic Heart Rhythm Clinic. Interpretable 12-lead and 6-lead recordings were available for 685 out of 705 (97%) eligible patients.

Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram.

Importance: Long QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is the hallmark feature of LQTS, approximately 40% of patients with genetically confirmed LQTS have a normal corrected QT (QTc) at rest. Distinguishing patients with LQTS from those with a normal QTc is important to correctly diagnose disease, implement simple LQTS preventive measures, and initiate prophylactic therapy if necessary.

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

Background: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities.

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram.

Importance: For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.

Objective: To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD.

Debatable issues in automated ECG reporting.

Although automated ECG analysis has been available for many years, there are some aspects which require to be re-assessed with respect to their value while newer techniques which are worthy of review are beginning to find their way into routine use. At the annual International Society of Computerized Electrocardiology conference held in April 2017, four areas in particular were debated. These were a) automated 12 lead resting ECG analysis; b) real time out of hospital ECG monitoring; c) ECG imaging; and d) single channel ECG rhythm interpretation.

The important role of material and chemical characterisation in device evaluation.

Parts 18 and 19 of ISO 10993, Biological Evaluation of Medical Devices, currently in draft, are receiving even more emphasis as they become an integral part of the biological safety evaluation of biomaterials and medical devices. An important step in this process is the characterisation of the material and identification of chemicals that can migrate or extract from the polymer components. This article discusses which tests will meet the requirements.