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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.A total of 40 healthy adults participated in this study. Participants engaged in a period of rest, followed by 3 min of moderate to vigorous intensity cycling on a stationary bicycle. Heart rate measurements were obtained from both the finger and face of participants using the Cardiio app at rest, immediately after exercise, 1-2 min after exercise, and 2-3 min after exercise. Concurrent heart rate readings using an FDA-cleared finger pulse oximeter served as the reference measurement.There was a very strong agreement between heart rate measurements obtained using the Cardiio app and the pulse oximeter, both at rest (r = 0.99 for finger, r = 0.97 for face) and after exercise (r = 0.99 for finger, r = 0.97 for face). At rest, the accuracy of the Cardiio app was ±1.58 beats per minute (bpm) (or ±2.27%) using the finger mode and ±2.28 bpm (or ±3.17%) for the face mode, compared to the pulse oximeter. After moderate to vigorous exercise, the accuracy of the Cardiio app was ±2.97 bpm (or ±2.79%) using the finger mode and ±5.31 bpm (or ±4.50%) for the face mode, compared to the pulse oximeter.The Cardiio app provided accurate heart rate measurements from the finger and face, both at rest and after exercise.

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

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. This protocol describes a photolithography method that allows for facile and rapid microfabrication of PEG structures and devices. This strategy involves an in-house developed fabrication stage that allows for the rapid fabrication of 3D structures by building upwards in a layer-by-layer fashion. Independent moving components can also be aligned and assembled onto support structures to form integrated devices. These independent components are doped with superparamagnetic iron oxide nanoparticles that are sensitive to magnetic actuation. In this manner, the fabricated devices can be actuated using external magnets to yield movement of the components within. Hence, this technique allows for the fabrication of sophisticated MEMS-like devices (micromachines) that are composed entirely out of a biocompatible hydrogel, able to function without an onboard power source, and respond to a contact-less method of actuation. This manuscript describes the fabrication of both the fabrication set-up as well as the step-by-step method for the microfabrication of these hydrogels-based MEMS-like devices.

Background We aimed to evaluate a novel method of atrial fibrillation ( AF ) screening using an iP hone camera to detect and analyze photoplethysmographic signals from the face without physical contact by extracting subtle beat‐to‐beat variations of skin color that reflect the cardiac pulsatile signal. Methods and Results Patients admitted to the cardiology ward of the hospital for clinical reasons were recruited. Simultaneous facial and fingertip photoplethysmographic measurements were obtained from 217 hospital inpatients (mean age, 70.3±13.9 years; 71.4% men) facing the front camera and with an index finger covering the back camera of 2 independent iP hones before a 12‐lead ECG was recorded. Backdrop and background light intensity was monitored during signal acquisition. Three successive 20‐second (total, 60 seconds) recordings were acquired per patient and analyzed for heart rate regularity by Cardiio Rhythm (Cardiio Inc, Cambridge, MA ) smartphone application. Pulse irregularity in ≥1 photoplethysmographic readings or 3 uninterpretable photoplethysmographic readings were considered a positive AF screening result. AF was present on 12‐lead ECG in 34.6% (n=75/217) patients. The Cardiio Rhythm facial photoplethysmographic application demonstrated high sensitivity (95%; 95% confidence interval, 87%–98%) and specificity (96%; 95% confidence interval, 91%–98%) in discriminating AF from sinus rhythm compared with 12‐lead ECG . The positive and negative predictive values were 92% (95% confidence interval, 84%–96%) and 97% (95% confidence interval, 93%–99%), respectively. Conclusions Detection of a facial photoplethysmographic signal to determine pulse irregularity attributable to AF is feasible. The Cardiio Rhythm smartphone application showed high sensitivity and specificity, with low negative likelihood ratio for AF from facial photoplethysmographic signals. The convenience of a contact‐free approach is attractive for community screening and has the potential to be useful for distant AF screening.

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. Patients >18 years of age who were scheduled for elective CV for AF were enrolled in the study. CRMA finger pulse recordings, utilizing an iPhone camera, were obtained before (pre-CV) and after (post-CV) the CV. The findings were validated against surface electrocardiograms. Ninety-eight patients (75.5% men), mean age of 67.7 ± 10.5 years, were enrolled. No electrocardiogram for validation was available in 1 case. Pre-CV CRMA readings were analyzed in 97 of the 98 patients. Post-CV CRMA readings were analyzed for 92 of 93 patients who underwent CV. One patient left before the recording was obtained. The Cardiio Rhythm Mobile Application correctly identified 94 of 101 AF recordings (93.1%) as AF and 80 of 88 non-AF recordings (90.1%) as non-AF. The sensitivity was 93.1% (95% confidence interval [CI] = 86.9% to 97.2%) and the specificity was 90.9% (95% CI = 82.9% to 96.0%). The positive predictive value was 92.2% (95% CI = 85.8% to 95.8%) and the negative predictive value was 92.0% (95% CI = 94.8% to 95.9%). In conclusion, the CRMA demonstrates promising potential in accurate detection and discrimination of AF from normal sinus rhythm in patients with a history of AF.

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. Methods and Results Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single‐lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI ] 77–99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51–87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI : 97–99%] versus 99.4% [95% CI 99–100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38–67%] versus 76.9% [95% CI 56–91%]); both had a very high negative predictive value (99.8% [95% CI 99–100%] versus 99.2% [95% CI 98–100%]). Conclusions The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population‐based screening for AF .