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Objectives: The WATCH AF (SmartWATCHes for Detection of Atrial Fibrillation) trial compared the diagnostic accuracy to detect atrial fibrillation (AF) by a smartwatch-based algorithm using photoplethysmographic (PPG) signals with cardiologists' diagnosis by electrocardiography (ECG)., Background: Timely detection of AF is crucial for stroke prevention., Methods: In this prospective, 2-center, case-control trial, a PPG pulse wave recording using a commercially available smartwatch was obtained along with Internet-enabled mobile ECG in 672 hospitalized subjects. PPG recordings were analyzed by a novel automated algorithm. Cardiologists' diagnoses were available for 650 subjects, although 142 (21.8%) datasets were not suitable for PPG analysis, among them 101 (15.1%) that were also not interpretable by the automated Internet-enabled mobile ECG algorithm, resulting in a sample size of 508 subjects (mean age 76.4 years, 225 women, 237 with AF) for the main analyses., Results: For the PPG algorithm, we found a sensitivity of 93.7% (95% confidence interval [CI]: 89.8% to 96.4%), a specificity of 98.2% (95% CI: 95.8% to 99.4%), and 96.1% accuracy (95% CI: 94.0% to 97.5%) to detect AF., Conclusions: The results of the WATCH AF trial suggest that detection of AF using a commercially available smartwatch is in principle feasible, with very high diagnostic accuracy. Applicability of the tested algorithm is currently limited by a high dropout rate as a result of insufficient signal quality. Thus, achieving sufficient signal quality remains challenging, but real-time signal quality checks are expected to improve signal quality. Whether smartwatches may be useful complementary tools for convenient long-term AF screening in selected at-risk patients must be evaluated in larger population-based samples. (SmartWATCHes for Detection of Atrial Fibrillation [WATCH AF]:; NCT02956343)., (Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Our present knowledge about cardiac electrophysiology is based on numerous experiments and discoveries going back to the Greek antique and ancient Egypt. Exploration of cardiac anatomy was followed by the description of circulation and cardiac physiology in the 17th century. In the early 20th century cardiac electrophysiology became the new field of interest and was studied with the help of numerous animal experiments (squid, rays, dogs, goats, mice and other species). We ought to be grateful for the knowledge and possibilities in modern medicine that were made possible by the great number of researchers, patients and animals that contributed to this.

Background: Loss of left ventricular capture in patients with cardiac resynchronization devices may account for worsening heart failure and can be difficult to diagnose without a programmer., Objective: To determine whether distinct morphologic changes on the surface electrocardiogram indicate loss of left ventricular capture., Design: After analysis of the R-S spike ratio in the 12-lead electrocardiogram during right ventricular and biventricular pacing in 10 patients, an algorithm to detect loss of left ventricular capture was developed., Setting: University hospital., Patients: 54 patients with a cardiac resynchronization device and underlying left bundle-branch block., Measurements: Leads V1 and I of a 12-lead electrocardiogram were assessed after biventricular pacing was confirmed and after the device was programmed to right ventricular pacing only (simulating loss of left ventricular capture)., Results: The sensitivity of the algorithm to correctly identify loss of left ventricular capture was 94% (95% CI, 88.2% to 97.7%), and the specificity was 93% (CI, 86.3% to 95.8%). The likelihood ratio of a positive test result was 12.8 (CI, 6.443 to 23.310), and the likelihood ratio of a negative test result was 0.06 (CI, 0.024 to 0.137)., Limitations: The algorithm was tested in patients in whom the right ventricular electrode was placed in the apex of the right ventricle only., Conclusion: Presence of biventricular capture--the prerequisite for successful cardiac resynchronization therapy--and loss of left ventricular capture can be accurately detected by an algorithm based on analysis of the R-S ratio on leads V1 and I of the surface electrocardiogram.

Background:Atrial fibrillation (AF), the most common cardiac arrhythmia, can be detected by smartphones and smartwatches. Introduction:Single-lead ECGs (iECGs) and photoplethysmography (PPG) sensors provide the opportunity for a broad, simple, and easily repeatable cardiac rhythm analysis. To reduce unnecessary medical follow-up testing due to false positive results, our aim was to find a screening approach applicable on smart devices with a focus on high specificity. Methods:We used PPG measurements from smartphones and smartwatches and iECG data from two previous validation trials. Two AF detection algorithms (A and B) were applied on the iECG dataset and compared directly. Further, we used 1-min PPG measurements as a first-pass filter for arrhythmia detection and simulated a sequential testing: Once an arrhythmia was detected in the PPG, the iECG counterpart of the patient was analyzed by algorithm A, B, or A + B combined although algorithm B was primarily designed for PPG analysis. Results:The iECGs from 1,288 participants were analyzed. Algorithm A did not show a diagnosis in 16.1%. In the remaining, sensitivity and specificity were 99.6%, and 97.4% respectively. Accuracy was 98.5%, and correct classification rate (CCR) was 82.7%. Algorithm B always differentiated between normal and arrhythmic and reached an overall sensitivity of 95.4%, a specificity of 91.6%, and an accuracy and CCR of 93.3%. Sequential testing by combining both algorithms into a three-phase test (Test positive PPG, then iECG analysis by A and B combined) resulted in a 100% specificity. Conclusion:Algorithm B performed strongly in PPG analysis as well as iECG analysis. PPG signals and consecutive iECG combined when an arrhythmia was detected by PPG resulted in a specificity that was higher than 99%. Discussion:The analysis allows a direct comparison of iECG algorithms without possible dilution by different measurement procedures or recording-devices. We improved specificity in AF-screening approaches with wearables by simulating a novel approach. Results rely on signal quality. [ABSTRACT FROM AUTHOR]

Introduction: This study investigates the accuracy of a heart rate (HR) measurement algorithm applied to a pulse wave. This was based on video signals recorded with a smartphone. The results of electrocardiographic HR and standard linear heart rate variability (HRV) analysis were used for reference. Materials and Methods: On a total of 68 subjects, an electrocardiogram (ECG) and the pulse curve were simultaneously recorded on an Apple iPhone 4S. The HR was measured using an algorithm developed by the authors that works according to a method combining the detection of the steepest slope of every pulse wave with the correlation to an optimized pulse wave pattern. Results: The results of the HR measured by pulse curves were extremely consistent (R> 0.99) with the HR measured on ECGs. For most standard linear HRV parameters as well, high correlations of R≥ 0.90 in the analysis were achieved in the time and frequency domain. Conclusion: In conclusion, the overall accuracy of HR and HRV indices of pulse wave analysis, based on video signals of a smartphone, with the developed algorithm was sufficient for preclinical screening applications. [ABSTRACT FROM AUTHOR]