High-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning.

This study uses video and a pretrained deep convolutional neural network to analyze facial photoplethysmographic signals in detection of atrial fibrillation.

Figure Correction: Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study.

[This corrects the article DOI: 10.2196/mhealth.7275.

An Additive Manufacturing Technique for the Facile and Rapid Fabrication of Hydrogel-based Micromachines with Magnetically Responsive Components.

Polyethylene glycol (PEG)-based hydrogels are biocompatible hydrogels that have been approved for use in humans by the FDA. Typical PEG-based hydrogels have simple monolithic architectures and often function as scaffolding materials for tissue engineering applications. More sophisticated structures typically take a long time to fabricate and do not contain moving components.

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms.

Objective: To evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.

Methods: We trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists.

Diagnostic Accuracy of a Novel Mobile Phone Application for the Detection and Monitoring of Atrial Fibrillation.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in adults, associated with significant morbidity, increased mortality, and rising health-care costs. Simple and available tools for the accurate detection of arrhythmia recurrence in patients after electrical cardioversion (CV) or ablation procedures for AF can help to guide therapeutic decisions. We conducted a prospective, single-center study to evaluate the accuracy of Cardiio Rhythm Mobile Application (CRMA) for AF detection.

Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study.

Background: Modern smartphones allow measurement of heart rate (HR) by detecting pulsatile photoplethysmographic (PPG) signals with built-in cameras from the fingertips or the face, without physical contact, by extracting subtle beat-to-beat variations of skin color.

Objective: The objective of our study was to evaluate the accuracy of HR measurements at rest and after exercise using a smartphone-based PPG detection app.

Methods: A total of 40 healthy participants (20 men; mean age 24.

Validation of a Standalone Smartphone Application for Measuring Heart Rate Using Imaging Photoplethysmography.

Background: The aim of this study was to determine the accuracy of a freely available smartphone application, Cardiio app (Cardiio, Inc., Cambridge, MA), to measure heart rate from the finger or face using imaging photoplethysmography, by comparing against an FDA-cleared pulse oximeter at rest, and after moderate to vigorous exercise.

Methods: A total of 40 healthy adults participated in this study.

Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting.

Background: Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera-based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real-world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting.