Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation

Fair non-contact blood pressure estimation using imaging photoplethysmography

Hypertension is typically manifested as a latent symptom that requires detection through specialized equipment. This poses an inconvenience for individuals who need to undergo long-term blood pressure monitoring in their daily lives. Therefore, there is a need for a portable, non-contact method for estimating blood pressure. However, current non-contact blood pressure estimation methods often rely on relatively narrow datasets, lacking a broad range of blood pressure distributions. Additionally, their applicability is confined to controlled experimental environments. This study proposes a non-contact blood pressure estimation method suitable for various life scenarios, encompassing multiple age groups, diverse ethnicities, and individuals with different skin tones. The aim is to enhance the practicality and accuracy of existing non-contact blood pressure estimation methods. The research extracts the imaging photoplethysmogram (IPPG) signal from facial videos and processes the signal through four layers of filtering operations to obtain an IPPG signal reflecting pulse wave variations. A CNN+BiLSTM+GRU network structure is constructed to improve the accuracy of current non-contact blood pressure estimation methods. In comparison to existing approaches, the mean absolute error (MAE) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) is reduced by 13.6% and 16.4%, respectively.

The influence of cardiac arrhythmias on the detection of heartbeats in the photoplethysmogram: benchmarking open-source algorithms

Objective.Cardiac arrhythmias are a leading cause of mortality worldwide. Wearable devices based on photoplethysmography give the opportunity to screen large populations, hence allowing for an earlier detection of pathological rhythms that might reduce the risks of complications and medical costs. While most of beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, the performance of these algorithms in patients with other cardiac arrhythmias, such as ventricular tachycardia or bigeminy, remain unknown to date.Approach. ThePPG-beatsopen-source framework, developed by Charlton and colleagues, evaluates the performance of the beat detectors namedQPPG,MSPTDandABDamong others. We applied thePPG-beatsframework on two newly acquired datasets, one containing seven different types of cardiac arrhythmia in hospital settings, and another dataset including two cardiac arrhythmias in ambulatory settings.Main Results. In a clinical setting, theQPPGbeat detector performed best on atrial fibrillation (with a medianF1score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked 2nd for bigeminy (75.7%) behindABDdetector (76.1%). In an ambulatory setting, theMSPTDbeat detector performed best on normal sinus rhythm (94.6%), and theQPPGdetector on atrial fibrillation (91.6%) and bigeminy (80.0%).Significance. Overall, the PPG beat detectorsQPPG,MSPTDandABDconsistently achieved higher performances than other detectors. However, the detection of beats from wrist-PPG signals is compromised in presence of bigeminy or ventricular tachycardia.

Machine Learning for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Scoping Review of Current Literature

Postoperative atrial fibrillation (POAF) occurs in up to 20% to 55% of patients who underwent cardiac surgery. Machine learning (ML) has been increasingly employed in monitoring, screening, and identifying different cardiovascular clinical conditions. It was proposed that ML may be a useful tool for predicting POAF after cardiac surgery. An electronic database search was conducted on Medline, EMBASE, Cochrane, Google Scholar, and ClinicalTrials.gov to identify primary studies that investigated the role of ML in predicting POAF after cardiac surgery. A total of 5,955 citations were subjected to title and abstract screening, and ultimately 5 studies were included. The reported incidence of POAF ranged from 21.5% to 37.1%. The studied ML models included: deep learning, decision trees, logistic regression, support vector machines, gradient boosting decision tree, gradient-boosted machine, K-nearest neighbors, neural network, and random forest models. The sensitivity of the reported ML models ranged from 0.22 to 0.91, the specificity from 0.64 to 0.84, and the area under the receiver operating characteristic curve from 0.67 to 0.94. Age, gender, left atrial diameter, glomerular filtration rate, and duration of mechanical ventilation were significant clinical risk factors for POAF. Limited evidence suggest that machine learning models may play a role in predicting atrial fibrillation after cardiac surgery because of their ability to detect different patterns of correlations and the incorporation of several demographic and clinical variables. However, the heterogeneity of the included studies and the lack of external validation are the most important limitations against the routine incorporation of these models in routine practice. Artificial intelligence, cardiac surgery, decision tree, deep learning, gradient-boosted machine, gradient boosting decision tree, k-nearest neighbors, logistic regression, machine learning, neural network, postoperative atrial fibrillation, postoperative complications, random forest, risk scores, scoping review, support vector machine.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study

BACKGROUND

Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)-driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment.

OBJECTIVE

The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV).

METHODS

Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram.

RESULTS

A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12%) and 7/143 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98%, 96%, 96%, 99%, 97%, and 97%, 100%, 100%, 97%, and 99%, respectively, at an AF prevalence of ~50%.

CONCLUSIONS

This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment.

Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting

Aims

The aim was to validate the performance of a monitoring system consisting of a wrist-worn device and a data management cloud service intended to be used by medical professionals in detecting atrial fibrillation (AF).

Methods

Thirty adult patients diagnosed with AF alone or AF with concomitant flutter were recruited. Continuous photoplethysmogram (PPG) and intermittent 30 s Lead I electrocardiogram (ECG) recordings were collected over 48 h. The ECG was measured four times a day at prescheduled times, when notified due to irregular rhythm detected by PPG, and when self-initiated based on symptoms. Three-channel Holter ECG was used as the reference.

Results

The subjects recorded a total of 1,415 h of continuous PPG data and 3.8 h of intermittent ECG data over the study period. The PPG data were analyzed by the system's algorithm in 5-min segments. The segments containing adequate amounts, at least ~30 s, of adequate quality PPG data for rhythm assessment algorithm, were included. After rejecting 46% of the 5-min segments, the remaining data were compared with annotated Holter ECG yielding AF detection sensitivity and specificity of 95.6 and 99.2%, respectively. The ECG analysis algorithm labeled 10% of the 30-s ECG records as inadequate quality and these were excluded from the analysis. The ECG AF detection sensitivity and specificity were 97.7 and 89.8%, respectively. The usability of the system was found to be good by both the study subjects and the participating cardiologists.

Conclusion

The system comprising of a wrist device and a data management service was validated to be suitable for use in patient monitoring and in the detection of AF in an ambulatory setting.Clinical Trial Registration: ClinicalTrials.gov/, NCT05008601.

Accuracy of continuous photoplethysmography-based 1 min mean heart rate assessment during atrial fibrillation

AIMS

Although mobile health tools using photoplethysmography (PPG) technology have been validated for the detection of atrial fibrillation (AF), their utility for heart rate assessment during AF remains unclear. Therefore, we aimed to evaluate the accuracy of continuous PPG-based 1 min mean heart rate assessment during AF.

METHODS AND RESULTS

Persistent AF patients were provided with Holter electrocardiography (ECG) (for ≥24 h) simultaneously with a PPG-equipped smartwatch. Both the PPG-based smartwatch and Holter ECG automatically and continuously monitored patients' heart rate/rhythm. ECG and PPG recordings were synchronized and divided into 1 min segments, from which a PPG-based and an ECG-based average heart rate estimation were extracted. In total, 47 661 simultaneous ECG and PPG 1 min heart rate segments were analysed in 50 patients (34% women, age 73 ± 8 years). The agreement between ECG-determined and PPG-determined 1 min mean heart rate was high [root mean squared error (RMSE): 4.7 bpm]. The 1 min mean heart rate estimated using PPG was accurate within ±10% in 93.7% of the corresponding ECG-derived 1 min mean heart rate segments. PPG-based 1 min mean heart rate estimation was more often accurate during night-time (97%) than day-time (91%, P < 0.001) and during low levels (96%) compared to high levels of motion (92%, P < 0.001). A neural network with a 10 min history of the recording did not further improve the PPG-based 1 min mean heart rate assessment [RMSE: 4.4 (95% confidence interval: 3.5-5.2 bpm)]. Only chronic heart failure was associated with a lower agreement between ECG-derived and PPG-derived 1 min mean heart rates (P = 0.040).

CONCLUSION

During persistent AF, continuous PPG-based 1 min mean heart rate assessment is feasible in 60% of the analysed period and shows high accuracy compared with Holter ECG for heart rates <110 bpm.

Evaluation of a wrist-worn photoplethysmography monitor for heart rate variability estimation in patients recovering from laparoscopic colon resection

To evaluate the accuracy of heart rate variability (HRV) parameters obtained with a wrist-worn photoplethysmography (PPG) monitor in patients recovering from minimally invasive colon resection to investigate whether PPG has potential in postoperative patient monitoring. 31 patients were monitored for three days or until discharge or reoperation using a wrist-worn PPG monitor (PulseOn, Finland) with a Holter monitor (Faros 360, Bittium Biosignals, Finland) as a reference measurement device. Beat-to-beat intervals (BBI) and HRV information collected by PPG were compared with RR intervals (RRI) and HRV obtained from the ECG reference after removing artefacts and ectopic beats. The beat-to-beat mean error (ME) and mean absolute error (MAE) of good quality heartbeat intervals obtained by wrist PPG were estimated as - 1.34 ms and 10.4 ms respectively. A significant variation in the accuracy of the HRV parameters was found. In the time domain, SDNN (9.11%), TRI (11.4%) and TINN (11.1%) were estimated with low relative MAE, while RMSSD (34.3%), pNN50 (139%) and NN50 (188%) had higher errors. The logarithmic parameters in the frequency domain (VLF Log, LF Log and HF Log) exhibited the lowest relative error, and for non-linear parameters, SD2 (7.5%), DFA α1 (8.25%) and DFA α2 (4.71%) were calculated much more accurately than SD1 (34.3%). The wrist PPG shows some potential for use in a clinical setting. The accuracy of several HRV parameters analyzed post hoc was found sufficient to be used in further studies concerning postoperative recovery of patients undergoing laparoscopic colon resection, although there were large errors in many common HRV parameters such as RMSSD, pNN50 and NN50, rendering them unusable.ClinicalTrials.gov Identifier: NCT04996511, August 9, 2021, retrospectively registered.

Investigating the Use of Changes in Facial Features as Indicators of Physical Workload

OCCUPATIONAL APPLICATIONSPhysical workload may lead to negative outcomes, including musculoskeletal disorders. In this study, we found that there were observable changes in facial features over the length of a low intensity, prolonged assembly task, and that these changes were correlated to other measures of physical workload. This method can be implemented by practitioners to evaluate physical workload.

Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study

Background

Some attempts have been made to detect atrial fibrillation (AF) with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions.

Objective

This study is the second part of a 2-phase study aimed at developing a method for immediate detection of paroxysmal AF, using a wearable device with built-in photoplethysmography (PPG). The objective of this study is to develop an algorithm to immediately diagnose AF by an Apple Watch equipped with a PPG sensor that is worn by patients undergoing cardiac surgery and to use machine learning on the pulse data output from the device.

Methods

A total of 80 patients who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative AF, using a telemetry-monitored electrocardiogram (ECG) and an Apple Watch. AF was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on the pulse rate data output from the Apple Watch.

Results

One of the 80 patients was excluded from the analysis due to redness caused by wearing the Apple Watch. Of 79 patients, 27 (34.2%) developed AF, and 199 events of AF including brief AF were observed. Of them, 18 events of AF lasting longer than 1 hour were observed, and cross-correlation analysis showed that pulse rate measured by Apple Watch was strongly correlated (cross-correlation functions [CCF]: 0.6-0.8) with 8 events and very strongly correlated (CCF>0.8) with 3 events. The diagnostic accuracy by machine learning was 0.9416 (sensitivity 0.909 and specificity 0.838 at the point closest to the top left) for the area under the receiver operating characteristic curve.

Conclusions

We were able to safely monitor pulse rate in patients who wore an Apple Watch after cardiac surgery. Although the pulse rate measured by the PPG sensor does not follow the heart rate recorded by telemetry-monitored ECGs in some parts, which may reduce the accuracy of AF diagnosis by machine learning, we have shown the possibility of clinical application of using only the pulse rate collected by the PPG sensor for the early detection of AF.

Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection

Objective: To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG).

Approach: The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases. Manually annotated real PPG signals from the PhysioNet/CinC 2015 Challenge Database are used for performance evaluation. The performance is compared to that of a pulse-based reference detector.

Results: The sensitivity/specificity were found to be 98.1%/97.9 and 76.6%/96.8% for the CNN-based detector, respectively, whereas the corresponding results for the pulse-based detector were 94.7%/99.8 and 67.1%/93.8%, respectively.

Significance: The proposed detector may be useful for continuous, long-term monitoring of bradycardia and tachycardia using wearable devices, e.g., wrist-worn devices, especially in situations where sensitivity is favored over specificity. The study demonstrates that simulated PPG signals are suitable for training and validation of a CNN.

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia

OBJECTIVE

We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR.

METHODS

Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung's Gear S3 and Galaxy Watch 3.

RESULTS

The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors-30% and 66% lower-and mean heart rate and mean interbeat interval estimation errors-60% and 77% lower-when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias.

CONCLUSION

The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks.

SIGNIFICANCE

By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.

Orthogonality-Constrained CNMF-Based Noise Reduction with Reduced Degradation of Biological Sound

The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow progression of the disease. Hence, a device that enables the early diagnosis of both diseases is necessary. In our previous study, a sensor for monitoring biological sounds such as vascular and respiratory sounds was developed and a noise reduction method based on semi-supervised convolutive non-negative matrix factorization (SCNMF) was proposed for the noisy environments of users. However, SCNMF attenuated part of the biological sound in addition to the noise. Therefore, this paper proposes a novel noise reduction method that achieves less distortion by imposing orthogonality constraints on the SCNMF. The effectiveness of the proposed method was verified experimentally using the biological sounds of 21 subjects. The experimental results showed an average improvement of 1.4 dB in the signal-to-noise ratio and 2.1 dB in the signal-to-distortion ratio over the conventional method. These results demonstrate the capability of the proposed approach to measure biological sounds even in noisy environments.

Accuracy of heart rate variability estimated with reflective wrist-PPG in elderly vascular patients

Optical heart rate monitoring (OHR) with reflective wrist photoplethysmography is a technique mainly used in the wellness application domain for monitoring heart rate levels during exercise. In the absence of motion, OHR technique is also able to estimate individual beat-to-beat intervals relatively well and can therefore also be used, for example, in monitoring of cardiac arrhythmias, stress, or sleep quality through heart rate variability (HRV) analysis. HRV analysis has also potential in monitoring the recovery of patients, e.g. after a medical intervention. However, in order to detect subtle changes, the calculated HRV parameters should be sufficiently accurate and very few studies exist that asses the accuracy of OHR derived HRV in non-healthy subjects. In this paper, we present a method to estimate beat-to-beat-intervals (BBIs) from reflective wrist PPG signal and evaluated the accuracy of the proposed method in estimating BBIs in a cross-sectional study with 29 hospitalized patients (mean age 70.6 years) in 24-h recordings performed after peripheral vascular surgery or endovascular interventions. Finally, we evaluate the accuracy of more than 30 commonly used HRV parameters and find that the accuracy of certain metrics, for example SDNN and triangular index, shown in the literature to be associated with the deterioration of the status of the patients during recovery from surgical intervention, could be adequate for patient monitoring. On the other hand, the parameters more affected by the high-frequency content of the HRV and especially the LF/HF-ratio should be used with caution.

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.

Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data

INTRODUCTION

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the developed world. Using photoplethysmography (PPG) and software algorithms, AF can be detected with high accuracy using smartphone camera-derived data. However, reports of diagnostic accuracy of standalone algorithms using wristband-derived PPG data are sparse, while this provides a means to perform long-term AF screening and monitoring. This study evaluated the diagnostic accuracy of a well-known standalone algorithm using wristband-derived PPG data.

MATERIALS AND METHODS

Subjects recruited from a community senior care organization were instructed to wear the Wavelet PPG wristband on one arm and the Alivecor KardiaBand one-lead-ECG wristband on the other. Three consecutive measurements (duration per measurement: 60 s for PPG and 30 s for one-lead ECG) were performed with both devices, simultaneously. The PPG data were analyzed by the Fibricheck standalone algorithm and the ECG data by the Kardia algorithm. The results were compared to a reference standard (interpretation of the one-lead ECG by two independent cardiologists).

RESULTS

A total of 180 PPGs and one-lead ECGs were recorded in 60 subjects, with a mean age of 70±17. AF was identified in 6 (10%) of the users, two users (3%) were not classifiable by the PPG algorithm and 1 user (2%) was not classifiable by the one-lead ECG algorithm. The diagnostic performance (sensitivity/specificity/positive predictive value/negative predictive value/accuracy) on user level was 100/96/75/100/97% for the PPG wristband and 100/98/86/100/98% for the one-lead ECG wristband.

CONCLUSIONS

In a small real-world cohort of elderly people, the standalone Fibricheck AF algorithm can accurately detect AF using Wavelet wristband-derived PPG data. Results are comparable to the Alivecor Kardia one-lead ECG device, with an acceptable unclassifiable/bad quality rate. This opens the door for long-term AF screening and monitoring.

Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review

Early detection and diagnosis of atrial fibrillation (AF) is essential in order to prevent stroke and other severe health consequences. The challenges in diagnosing AF arise from its intermittent and asymptomatic nature. Wrist-worn devices that use monitoring based on photoplethysmography have been proposed recently as a possible solution because of their ability to monitor heart rate and rhythm for long periods of time at low cost. Long-term continuous monitoring with implantable devices has been shown to increase the percentage of detected AF episodes, but the additional value of wrist-worn devices has yet to be determined. In this review, we present the state of the art in AF detection with wrist-worn devices, discuss the potential of the technology and current knowledge gaps, and propose directions for future research. The state-of-the-art methods show excellent accuracy for AF detection. However, most of the studies were conducted in hospital settings, and more studies showing the accuracy of the technology for ambulatory long-term monitoring are needed. Objective comparison of results and methodologies among different studies currently is difficult due to the lack of adequate public datasets.

Validity of Wrist-Worn photoplethysmography devices to measure heart rate: A systematic review and meta-analysis

Heart rate (HR), when combined with accelerometry, can dramatically improve estimates of energy expenditure and sleep. Advancements in technology, via the development and introduction of small, low-cost photoplethysmography devices embedded within wrist-worn consumer wearables, have made the collection of heart rate (HR) under free-living conditions more feasible. This systematic review and meta-analysis compared the validity of wrist-worn HR estimates to a criterion measure of HR (electrocardiography ECG or chest strap). Searches of PubMed/Medline, Web of Science, EBSCOhost, PsycINFO, and EMBASE resulted in a total of 44 articles representing 738 effect sizes across 15 different brands. Multi-level random effects meta-analyses resulted in a small mean difference (beats per min, bpm) of -0.40 bpm (95 confidence interval (CI) -1.64 to 0.83) during sleep, -0.01 bpm (-0.02 to 0.00) during rest, -0.51 bpm (-1.60 to 0.58) during treadmill activities (walking to running), while the mean difference was larger during resistance training (-7.26 bpm, -10.46 to -4.07) and cycling (-4.55 bpm, -7.24 to -1.87). Mean difference increased by 3 bpm (2.5 to 3.5) per 10 bpm increase of HR for resistance training. Wrist-worn devices that measure HR demonstrate acceptable validity compared to a criterion measure of HR for most common activities.

Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG

OBJECTIVE

To validate the accuracy of the Oura ring in the quantification of resting heart rate (HR) and heart rate variability (HRV).

BACKGROUND

Wearable devices have become comfortable, lightweight, and technologically advanced for assessing health behavior. As an example, the novel Oura ring integrates daily physical activity and nocturnal cardiovascular measurements. Ring users can follow their autonomic nervous system responses to their daily behavior based on nightly changes in HR and HRV, and adjust their behavior accordingly after self-reflection. As wearable photoplethysmogram (PPG) can be disrupted by several confounding influences, it is crucial to demonstrate the accuracy of ring measurements.

APPROACH

Nocturnal HR and HRV were assessed in 49 adults with simultaneous measurements from the Oura ring and the gold standard ECG measurement. Female and male participants with a wide age range (15-72 years) and physical activity status were included. Regression analysis between ECG and the ring outcomes was performed.

MAIN RESULTS

Very high agreement between the ring and ECG was observed for nightly average HR and HRV (r² = 0.996 and 0.980, respectively) with a mean bias of -0.63 bpm and -1.2 ms. High agreement was also observed across 5 min segments within individual nights in (r² = 0.869 ± 0.098 and 0.765 ± 0.178 in HR and HRV, respectively).

SIGNIFICANCE

Present findings indicate high validity of the Oura ring in the assessment of nocturnal HR and HRV in healthy adults. The results show the utility of this miniaturised device as a lifestyle management tool in long-term settings. High quality PPG signal results prompt future studies utilizing ring PPG towards clinically relevant health outcomes.

Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study

Background

Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke.

Objective

This study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputted from those devices were analyzed for precision and accuracy in reference to the heart rate data from electrocardiography (ECG) during AF.

Methods

A total of 40 subjects from patients who underwent cardiac surgery at a single center between September 2017 and March 2018 were monitored for postoperative AF using telemetric ECG and PPG devices. AF was diagnosed using a 12-lead ECG by qualified physicians. Each subject was given a pair of smart watches, Apple Watch and FBT, for simultaneous pulse rate monitoring. The heart rate of all subjects was also recorded on the telemetry system. Time series pulse rate trends and heart rate trends were created and analyzed for trend pattern similarities. Those trend data were then used to determine the accuracy of PPG-based pulse rate measurements in reference to ECG-based heart rate measurements during AF.

Results

Of the 20 AF events in group FBT, 6 (30%) showed a moderate or higher correlation (cross-correlation function>0.40) between pulse rate trend patterns and heart rate trend patterns. Of the 16 AF events in group Apple Watch (workout [W] mode), 12 (75%) showed a moderate or higher correlation between the 2 trend patterns. Linear regression analyses also showed a significant correlation between the pulse rates and the heart rates during AF in the subjects with Apple Watch. This correlation was not observed with FBT. The regression formula for Apple Watch W mode and FBT was X=14.203 + 0.841Y and X=58.225 + 0.228Y, respectively (where X denotes the mean of all average pulse rates during AF and Y denotes the mean of all corresponding average heart rates during AF), and the coefficient of determination (R²) was 0.685 and 0.057, respectively (P<.001 and .29, respectively).

Conclusions

In this validation study, the detection precision of AF and measurement accuracy during AF were both better with Apple Watch W mode than with FBT.

Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification

Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world's population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time-frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%).

Validation of Single Centre Pre-Mobile Atrial Fibrillation Apps for Continuous Monitoring of Atrial Fibrillation in a Real-World Setting: Pilot Cohort Study

Background

Atrial fibrillation is the most common recurrent arrhythmia in clinical practice, with most clinical events occurring outside the hospital. Low detection and nonadherence to guidelines are the primary obstacles to atrial fibrillation management. Photoplethysmography is a novel technology developed for atrial fibrillation screening. However, there has been limited validation of photoplethysmography-based smart devices for the detection of atrial fibrillation and its underlying clinical factors impacting detection.

Objective

This study aimed to explore the feasibility of photoplethysmography-based smart devices for the detection of atrial fibrillation in real-world settings.

Methods

Subjects aged ≥18 years (n=361) were recruited from September 14 to October 16, 2018, for screening of atrial fibrillation with active measurement, initiated by the users, using photoplethysmography-based smart wearable devices (ie, a smart band or smart watches). Of these, 200 subjects were also automatically and periodically monitored for 14 days with a smart band. The baseline diagnosis of “suspected” atrial fibrillation was confirmed by electrocardiogram and physical examination. The sensitivity and accuracy of photoplethysmography-based smart devices for monitoring atrial fibrillation were evaluated.

Results

A total of 2353 active measurement signals and 23,864 periodic measurement signals were recorded. Eleven subjects were confirmed to have persistent atrial fibrillation, and 20 were confirmed to have paroxysmal atrial fibrillation. Smart devices demonstrated >91% predictive ability for atrial fibrillation. The sensitivity and specificity of devices in detecting atrial fibrillation among active recording of the 361 subjects were 100% and about 99%, respectively. For subjects with persistent atrial fibrillation, 127 (97.0%) active measurements and 2240 (99.2%) periodic measurements were identified as atrial fibrillation by the algorithm. For subjects with paroxysmal atrial fibrillation, 36 (17%) active measurements and 717 (19.8%) periodic measurements were identified as atrial fibrillation by the algorithm. All persistent atrial fibrillation cases could be detected as “atrial fibrillation episodes” by the photoplethysmography algorithm on the first monitoring day, while 14 (70%) patients with paroxysmal atrial fibrillation demonstrated “atrial fibrillation episodes” within the first 6 days. The average time to detect paroxysmal atrial fibrillation was 2 days (interquartile range: 1.25-5.75) by active measurement and 1 day (interquartile range: 1.00-2.00) by periodic measurement (P=.10). The first detection time of atrial fibrillation burden of <50% per 24 hours was 4 days by active measurement and 2 days by periodic measurementThe first detection time of atrial fibrillation burden of >50% per 24 hours was 1 day for both active and periodic measurements (active measurement: P=.02, periodic measurement: P=.03).

Conclusions

Photoplethysmography-based smart devices demonstrated good atrial fibrillation predictive ability in both active and periodic measurements. However, atrial fibrillation type could impact detection, resulting in increased monitoring time.

Trial Registration

Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191.

Accuracy of wrist-worn heart rate monitors for rate control assessment in atrial fibrillation

BACKGROUND

Wrist-worn heart rate (HR) monitors are increasingly popular. A paucity of data exists on their accuracy in atrial fibrillation (AF) in ambulatory patients. We sought to assess the HR accuracy of two commercially available smart watches [SW] (Fitbit Charge HR [FB] and Apple Watch Series 3 [AW]) compared with Holter monitoring in an ambulant patient cohort.

METHODS

Thirty-two participants ≥18 years referred for 24-hour Holter monitoring were prospectively recruited. Each participant was randomly allocated to wear either a FB or AW along with their Holter monitor.

RESULTS

Across all devices, 53,288 heart rate values were analysed from 32 participants. Twenty wore the AW (17 had persistent AF and 3 had sinus rhythm [SR]) while 12 participants wore the FB (9 in persistent AF and 3 in SR). Participants in SR demonstrated strong agreement compared to Holter monitoring (bias <1 beat, limits of agreement [LoA] -11 to 11 beats). In AF, both devices underestimated HR measurements (bias -9 beats, LoA -41 to 23). The degree of underestimation was more pronounced when HR > 100 bpm (bias of -28 beats for HR range 100-120 bpm, -48 for 120-140 bpm, and -69 for >140 bpm) compared to a slower HR (bias of -6 for HR range 80-100 bpm, <1 for 60-80 bpm, and -1 for <60 bpm).

CONCLUSION

In ambulatory patients, smartwatches underestimated HR in AF particularly at HR ranges >100 bpm. Further improvements in device technology are needed before integrating them into the clinical management of rate control in AF.

Non-invasive human vital signs monitoring based on twin-core optical fiber sensors

Twin-core fiber (TCF)-based sensor was proposed for non-invasive vital sign monitoring, including respiration and heartbeat. The TCF was homemade and the corresponding sensor was fabricated by sandwiching single-mode fiber (SMF) on both ends. The offset distance between SMF and TCF was optimized while the length of TCF was identified from preliminary vital sign measurement results. Then, the TCF-based sensor was attached under a mattress to realize non-invasive vital sign monitoring. Both respiration and heartbeat signal can be obtained simultaneously, which is consistent with the reference signals. For further application, post-exercise physiological activitity characterization were realized based on this vital sign monitoring system. In discussion, mode coupling in TCF was analyzed and utilized for curvature sensing with achieved sensitivity as high as 18 nm/m^-1, which supported its excellent performance for vital signs monitoring. In conclusion, the TCF-based vital signs monitors can be a promising candidate for healthcare and biomedical applications.