Utility of photoplethysmography for heart rate estimation among inpatients

Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation

BACKGROUND

Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated.

OBJECTIVE

The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings.

METHODS

Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set.

RESULTS

The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE.

CONCLUSIONS

In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models.

The Accuracy of Wrist-Worn Photoplethysmogram-Measured Heart and Respiratory Rates in Abdominal Surgery Patients: Observational Prospective Clinical Validation Study

BACKGROUND

Postoperative deterioration is often preceded by abnormal vital parameters. Therefore, vital parameters of postoperative patients are routinely measured by nursing staff. Wrist-worn sensors could potentially provide an alternative tool for the measurement of vital parameters in low-acuity settings. These devices would allow more frequent or even continuous measurements of vital parameters without relying on time-consuming manual measurements, provided their accuracy in this clinical population is established.

OBJECTIVE

This study aimed to assess the accuracy of heart rate (HR) and respiratory rate (RR) measures obtained via a wearable photoplethysmography (PPG) wristband in a cohort of postoperative patients.

METHODS

The accuracy of the wrist-worn PPG sensor was assessed in 62 post-abdominal surgery patients (mean age 55, SD 15 years; median BMI 34, IQR 25-40 kg/m²). The wearable obtained HR and RR measurements were compared to those of the reference monitor in the postanesthesia or intensive care unit. Bland-Altman and Clarke error grid analyses were performed to determine agreement and clinical accuracy.

RESULTS

Data were collected for a median of 1.2 hours per patient. With a coverage of 94% for HR and 34% for RR, the device was able to provide accurate measurements for the large majority of the measurements as 98% and 93% of the measurements were within 5 bpm or 3 rpm of the reference signal. Additionally, 100% of the HR and 98% of the RR measurements were clinically acceptable on Clarke error grid analysis.

CONCLUSIONS

The wrist-worn PPG device is able to provide measurements of HR and RR that can be seen as sufficiently accurate for clinical applications. Considering the coverage, the device was able to continuously monitor HR and report RR when measurements of sufficient quality were obtained.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03923127; https://www.clinicaltrials.gov/ct2/show/NCT03923127.

Comparison of 2 Smart Watch Algorithms for Detection of Atrial Fibrillation and the Benefit of Clinician Interpretation: SMART WARS Study

BACKGROUND

Smart watches and wearable technology capable of heart rhythm assessment have increased in use in the general population. The Apple Watch Series 4 (AW4) and KardiaBand (KB) are devices capable of obtaining single-lead electrocardiographic recordings, presenting a novel opportunity for the detection of paroxysmal arrhythmias.

OBJECTIVES

The aim of this study was to assess the diagnostic utility of the AW4 and KB in an elderly outpatient population.

METHODS

Consecutive recordings were taken from patients attending cardiology outpatient clinic from the AW4 and KB concurrently with 12-lead electrocardiography. Automated diagnoses and blinded single-lead electrocardiographic tracing interpretations by 2 cardiologists were analyzed. Analysis was also conducted to assess the effect of combined device and clinician interpretation.

RESULTS

One hundred twenty-five patients were prospectively recruited (mean age 76 ± 7 years, 62% men). The accuracy of the automated rhythm assessment was higher with the KB than the AW4 (74% vs 65%). For the detection of atrial fibrillation, the sensitivity and negative predictive value of the KB were 89% and 97%, respectively, and of the AW4 were 19% and 82%, respectively. Using hybrid automated and clinician interpretation, the overall accuracy of the KB and AW4 was 91% and 87%, respectively.

CONCLUSIONS

The KB automated algorithm outperformed the AW4 in its accuracy and sensitivity for detecting atrial fibrillation in the outpatient setting. Clinician assessment of the single-lead electrocardiogram improved accuracy. These findings suggest that although these devices' tracings are of sufficient quality, automated diagnosis alone is not sufficient for making clinical decisions about atrial fibrillation diagnosis and management.

Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks

Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis

Background

Significant morbidity, mortality, and financial burden are associated with cardiac rhythm abnormalities. Conventional investigative tools are often unsuccessful in detecting cardiac arrhythmias because of their episodic nature. Smartwatches have gained popularity in recent years as a health tool for the detection of cardiac rhythms.

Objective

This study aims to systematically review and meta-analyze the diagnostic accuracy of smartwatches in the detection of cardiac arrhythmias.

Methods

A systematic literature search of the Embase, MEDLINE, and Cochrane Library databases was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies reporting the use of a smartwatch for the detection of cardiac arrhythmia. Summary estimates of sensitivity, specificity, and area under the curve were attempted using a bivariate model for the diagnostic meta-analysis. Studies were examined for quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.

Results

A total of 18 studies examining atrial fibrillation detection, bradyarrhythmias and tachyarrhythmias, and premature contractions were analyzed, measuring diagnostic accuracy in 424,371 subjects in total. The signals analyzed by smartwatches were based on photoplethysmography. The overall sensitivity, specificity, and accuracy of smartwatches for detecting cardiac arrhythmias were 100% (95% CI 0.99-1.00), 95% (95% CI 0.93-0.97), and 97% (95% CI 0.96-0.99), respectively. The pooled positive predictive value and negative predictive value for detecting cardiac arrhythmias were 85% (95% CI 0.79-0.90) and 100% (95% CI 1.0-1.0), respectively.

Conclusions

This review demonstrates the evolving field of digital disease detection. The current diagnostic accuracy of smartwatch technology for the detection of cardiac arrhythmias is high. Although the innovative drive of digital devices in health care will continue to gain momentum toward screening, the process of accurate evidence accrual and regulatory standards ready to accept their introduction is strongly needed.

Trial Registration

PROSPERO International Prospective Register of Systematic Reviews CRD42020213237; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=213237.

Cardiac events monitoring

Cardiac events recorders have been developed in order to record the heart rhythm during symptoms such as palpitations or presyncope, to first make a diagnosis, and subsequently drive the treatment strategy. In other circumstances, they can be also used in asymptomatic patients (to record silent atrial fibrillation for instance). Because they are non-invasive, potentially cost-saving and relatively easy to use, the external rhythm recording devices have shown some great advances in the last years, spreading from photoplethysmographic technique to real ECG reconstruction. Technological advances in the field of microelectronics, as well as in the field of data transmission have contributed to their widespread use in cardiology. The trend for miniaturization was also expanded to the implantable recorders. This paper will review will review advantages and limitations of the different existing available well-established recording devices, as well as the last technological developments in terms of ECG recordings.

Wearables in der Schlaganfallmedizin

Unter Wearables versteht man in die Kleidung oder in tragbare Geräte integrierte Sensoren, die eine kontinuierliche Langzeitmessung von physiologischen Parametern, wie Herzfrequenz, Blutdruck, Atmung, Bewegung, Hautwiderstand usw. und/oder Bewegungsmustern ermöglichen. In der Schlaganfallmedizin eröffnen Wearables neue Optionen in der Diagnostik, Prävention und Rehabilitation.

Apple Watch, Wearables, and Heart Rhythm: where do we stand?

Atrial fibrillation (AF) poses a major health concern in the United States by affecting over 5 million people accounting for at least 15% to 25% of strokes. It can be asymptomatic or subclinical with its first presentation being stroke in 18%, and AF being only detected at the time of stroke. With evidence of subclinical AF associated with increased risk of ischemic stroke, recent developments indeed point towards wearables, especially smart watches, being quite effective and representing a novel method for screening for silent AF in the general population, and thereby reducing mortality and morbidity associated with it. This manuscript aims to review whether the photoplethysmography (PPG) technology, employed in the wearables to monitor heart rate, is accurate enough to aid in the diagnosis of AF that may remain asymptomatic or paroxysmal. It also explores the option of actually employing this method in the general population, the feasibility of this mode of diagnosis, sensitivity and specificity of this method compared to the conventional electrocardiogram (EKG), and the actual follow up with a practitioner and subsequent treatment of AF, if diagnosed. We conducted a Medline search using various combinations of "smart watch" "atrial fibrillation" "wearables", and "Kardia" to identify pivotal randomized trials published before June 1, 2019, for inclusion in this review. Concurrently, major practice guidelines, trial bibliographies, and pertinent reviews were examined to ensure inclusion of relevant trials. A consensus among the authors was used to choose items for narrative inclusion. The following section reviews data from pivotal trials to determine the effectiveness of smart watch technology in detecting AF in the general population. Trials reviewed evaluated apple watch, Kardia, Samsung wearables in diagnosis of AF. The fact that there is an increase in consumer use of wearables, smart devices, which can serve as health monitoring devices that can be used as a non-invasive, ambulatory assessment of heart rate and rhythm, is definitely novel. Intermittent short EKG recordings repeated over a longer-term period produced significantly better sensitivity for AF detection, with 4 times as many cases diagnosed compared with a single time-point measurement. Since there are limitations and further research into this new field is required, the wearable technology may not serve as the ultimate tool for diagnosis of AF, rather a nidus for the general population to seek medical advice for confirmation on being notified of having an irregular rhythm leading to prevention of morbidity and mortality associated with it.

Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study

BACKGROUND

Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, noninvasive monitoring.

OBJECTIVE

This study aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch.

METHODS

A total of 40 adults presenting to a cardiology clinic wore a smartwatch and Holter monitor and performed scripted movements to simulate activities of daily living (ADLs). Participants' clinical and sociodemographic characteristics were abstracted from medical records. Participants completed a questionnaire assessing different domains of the device's usability. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared with gold-standard Holter monitoring.

RESULTS

The average age of participants was 71 (SD 8) years; most participants had AF risk factors and 23% (9/39) were in AF. About half of the participants owned smartphones, but none owned smartwatches. Participants wore the smartwatch for 42 (SD 14) min while generating motion noise to simulate ADLs. The algorithm determined 53 of the 314 30-second noise-free pulse segments as consistent with AF. Compared with the gold standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively.

CONCLUSIONS

A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable.