Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation.

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network-Long Short-Term Memory-Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system's promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.

"But for the blind spot": Accuracy and diagnostic performance of smart watch cardiac features in pediatric patients.

BACKGROUND: The Apple Watch™ (AW) offers heart rate (HR) tracking by photoplethysmography (PPG) and single-lead electrocardiographic (ECG) recordings. The accuracy of AW-HR and diagnostic performance of AW-ECGs among children during both sinus rhythm and arrhythmias have not been explored. OBJECTIVE: The purposes of this study were to assess the accuracy of AW-HR measurements compared to gold standard modalities in children during sinus rhythm and arrhythmias and to identify non-sinus rhythms using AW-ECGs. METHODS: Subjects ≤18 years wore an AW during (1) telemetry admission, (2) electrophysiological study (EPS), or (3) exercise stress test (EST). AW-HRs were compared to gold standard modality values. Recorded AW-ECGs were reviewed by 3 blinded pediatric electrophysiologists. RESULTS: Eighty subjects (median age 13 years; interquartile range 1.0-16.0 years; 50% female) wore AW (telemetry 41% [n = 33]; EPS 34% [n = 27]; EST 25% [n = 20]). A total of 1090 AW-HR measurements were compared to time-synchronized gold standard modality HR values. Intraclass correlation coefficient (ICC) was high 0.99 (0.98-0.99) for AW-HR during sinus rhythm compared to gold standard modalities. ICC was poor comparing AW-HR to gold standard modality HR in tachyarrhythmias (ICC 0.24-0.27) due to systematic undercounting of AW-HR values. A total of 126 AW-ECGs were reviewed. Identification of non-sinus rhythm by AW-ECG showed sensitivity of 89%-96% and specificity of 78%-87%. CONCLUSIONS: We found high levels of agreement for AW-HR values with gold standard modalities during sinus rhythm and poor agreement during tachyarrhythmias, likely due to hemodynamic effects of tachyarrhythmias on PPG-based measurements. AW-ECGs had good sensitivity and moderate specificity in identification of non-sinus rhythm in children.

A Comparative Analysis of Various Machine Learning Algorithms to Improve the Accuracy of HbA1c Estimation Using Wrist PPG Data.

Due to the inconvenience of drawing blood and the possibility of infection associated with invasive methods, research on non-invasive glycated hemoglobin (HbA1c) measurement methods is increasing. Utilizing wrist photoplethysmography (PPG) with machine learning to estimate HbA1c can be a promising method for non-invasive HbA1c monitoring in diabetic patients. This study aims to develop a HbA1c estimation system based on machine learning algorithms using PPG signals obtained from the wrist. We used a PPG based dataset of 22 subjects and algorithms such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), Categorical Boost (CatBoost) and random forest (RF) to estimate the HbA1c values. Note that the AC-to-DC ratios for three wavelengths were newly adopted as features in addition to the previously acquired 15 features from the PPG signal and a comparative analysis was performed between the performances of several algorithms. We showed that feature-importance-based selection can improve performance while reducing computational complexity. We also showed that AC-to-DC ratio (AC/DC) features play a dominant role in improving HbA1c estimation performance and, furthermore, a good performance can be obtained without the need for external features such as BMI and SpO2. These findings may help shape the future of wrist-based HbA1c estimation (e.g., via a wristwatch or wristband), which could increase the scope of noninvasive and effective monitoring techniques for diabetic patients.

Smart-watch-programmed green-light-operated percutaneous control of therapeutic transgenes.

Wearable smart electronic devices, such as smart watches, are generally equipped with green-light-emitting diodes, which are used for photoplethysmography to monitor a panoply of physical health parameters. Here, we present a traceless, green-light-operated, smart-watch-controlled mammalian gene switch (Glow Control), composed of an engineered membrane-tethered green-light-sensitive cobalamin-binding domain of Thermus thermophilus (TtCBD) CarH protein in combination with a synthetic cytosolic TtCBD-transactivator fusion protein, which manage translocation of TtCBD-transactivator into the nucleus to trigger expression of transgenes upon illumination. We show that Apple-Watch-programmed percutaneous remote control of implanted Glow-controlled engineered human cells can effectively treat experimental type-2 diabetes by producing and releasing human glucagon-like peptide-1 on demand. Directly interfacing wearable smart electronic devices with therapeutic gene expression will advance next-generation personalized therapies by linking biopharmaceutical interventions to the internet of things.

An Inkjet-Printed PEDOT:PSS-Based Stretchable Conductor for Wearable Health Monitoring Device Applications.

A stretchable conductor is one of the key components in soft electronics that allows the seamless integration of electronic devices and sensors on elastic substrates. Its unique advantages of mechanical flexibility and stretchability have enabled a variety of wearable bioelectronic devices that can conformably adapt to curved skin surfaces for long-term health monitoring applications. Here, we report a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS)-based stretchable polymer blend that can be patterned using an inkjet printing process while exhibiting low sheet resistance and accommodating large mechanical deformations. We have systematically studied the effect of various types of polar solvent additives that can help induce phase separation of PEDOT and PSS grains and change the conformation of a PEDOT chain, thereby improving the electrical property of the film by facilitating charge hopping along the percolating PEDOT network. The optimal ink formulation is achieved by adding 5 wt % ethylene glycol into a pristine PEDOT:PSS aqueous solution, which results in a sheet resistance of as low as 58 Ω/□. Elasticity can also be achieved by blending the above solution with the soft polymer poly(ethylene oxide) (PEO). Thin films of PEDOT:PSS/PEO polymer blends patterned by inkjet printing exhibits a low sheet resistance of 84 Ω/□ and can resist up to 50% tensile strain with minimal changes in electrical performance. With its good conductivity and elasticity, we have further demonstrated the use of the polymer blend as stretchable interconnects and stretchable dry electrodes on a thin polydimethylsiloxane (PDMS) substrate for photoplethysmography (PPG) and electrocardiography (ECG) recording applications. This work shows the potential of using a printed stretchable conducting polymer in low-cost wearable sensor patches for smart health applications.

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.

Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch.

BACKGROUND: Consumer devices with broad reach may be useful in screening for atrial fibrillation (AF) in appropriate populations. However, currently no consumer devices are capable of continuous monitoring for AF. OBJECTIVE: The purpose of this study was to estimate the sensitivity and specificity of a smartwatch algorithm for continuous detection of AF from sinus rhythm in a free-living setting. METHODS: We studied a commercially available smartwatch with photoplethysmography (W-PPG) and electrocardiogram (W-ECG) capabilities. We validated a novel W-PPG algorithm combined with a W-ECG algorithm in a free-living setting, and compared the results to those of a 28-day continuous ECG patch (P-ECG). RESULTS: A total of 204 participants completed the free-living study, recording 81,944 hours with both P-ECG and smartwatch measurements. We found sensitivity of 87.8% (95% confidence interval [CI] 83.6%-91.0%) and specificity of 97.4% (95% CI 97.1%-97.7%) for the W-PPG algorithm (every 5-minute classification); sensitivity of 98.9% (95% CI 98.1%-99.4%) and specificity of 99.3% (95% CI 99.1%-99.5%) for the W-ECG algorithm; and sensitivity of 96.9% (95% CI 93.7%-98.5%) and specificity of 99.3% (95% CI 98.4%-99.7%) for W-PPG triggered W-ECG with a single W-ECG required for confirmation of AF. We found a very strong correlation of W-PPG in quantifying AF burden compared to P-ECG (r = 0.98). CONCLUSION: Our findings demonstrate that a novel algorithm using a commercially available smartwatch can continuously detect AF with excellent performance and that confirmation with W-ECG further enhances specificity. In addition, our W-PPG algorithm can estimate AF burden. Further research is needed to determine whether this algorithm is useful in screening for AF in select at-risk patients.

Validation of a non-invasive imaging photoplethysmography device to assess plantar skin perfusion, a comparison with laser speckle contrast analysis.

Assessing skin perfusion is an established and reliable method to study impaired lower limb blood flow. Laser Speckle Contrast Analysis (LASCA) has been identified as the current gold standard to measure skin perfusion. Imaging photoplethysmography (iPPG) is a new low-cost imaging technique to assess perfusion. However, it is unclear how results obtained from this technique compare against that of LASCA at plantar skin. Therefore, the aim of this study was to investigate the association between the skin perfusion at the plantar surface of the foot using iPPG and LASCA. Perfusion at six plantar locations (Hallux, 1st 3rd 5th metatarsal heads, midfoot, heel) was simultaneously measured using LASCA and iPPG in 20 healthy participants. Skin thickness and skin temperature were also collected at the same plantar locations. Spearman's rank tests showed significant associations with medium strength between the perfusion values measured with LASCA and iPPG for most tested sites. No improvement in the relationship between iPPG and LASCA data was observed when controlling for either skin thickness or skin temperature. Skin perfusion values obtained using iPPG were found to be significantly associated with the corresponding values obtained using the gold standard LASCA device. Additionally, the measurement of perfusion using iPPG is shown to be robust.

Remote Photoplethysmography for Evaluation of Cutaneous Sensory Nerve Fiber Function.

About 2% of the world's population suffers from small nerve fiber dysfunction, neuropathy, which can result in severe pain. This condition is caused by damage to the small nerve fibers and its assessment is challenging, due to the lack of simple and objective diagnostic techniques. The present study aimed to develop a contactless photoplethysmography system using simple instrumentation, for objective and non-invasive assessment of small cutaneous sensory nerve fiber function. The approach is based on the use of contactless photoplethysmography for the characterization of skin flowmotions and topical heating evoked vasomotor responses. The feasibility of the technique was evaluated on volunteers (n = 14) using skin topical anesthesia, which is able to produce temporary alterations of cutaneous nerve fibers function. In the treated skin region in comparison to intact skin: neurogenic and endothelial component of flowmotions decreased by ~61% and 41%, the local heating evoked flare area decreased by ~44%, vasomotor response trend peak and nadir were substantially reduced. The results indicate for the potential of the remote photoplethysmography in the assessment of the cutaneous nerve fiber function. It is believed that in the future this technique could be used in the clinics as an affordable alternative to laser Doppler imaging technique.

Smartphone Biosensor With App Meets FDA/ISO Standards for Clinical Pulse Oximetry and Can Be Reliably Used by a Wide Range of Patients.

BACKGROUND: Millions of smartphones contain a photoplethysmography (PPG) biosensor (Maxim Integrated) that accurately measures pulse oximetry. No clinical use of these embedded sensors is currently being made, despite the relevance of remote clinical pulse oximetry to the management of chronic cardiopulmonary disease, and the triage, initial management, and remote monitoring of people affected by respiratory viral pandemics, such as severe acute respiratory syndrome coronavirus 2 or influenza. To be used for clinical pulse oximetry the embedded PPG system must be paired with an application (app) and meet US Food and Drug Administration (FDA) and International Organization for Standardization (ISO) requirements. RESEARCH QUESTION: Does this smartphone sensor with app meet FDA/ISO requirements? Are measurements obtained using this system comparable to those of hospital reference devices, across a wide range of people? STUDY DESIGN AND METHODS: We performed laboratory testing addressing ISO and FDA requirements in 10 participants using the smartphone sensor with app. Subsequently, we performed an open-label clinical study on 320 participants with widely varying characteristics, to compare the accuracy and precision of readings obtained by patients with those of hospital reference devices, using rigorous statistical methodology. RESULTS: "Breathe down" testing in the laboratory showed that the total root-mean-square deviation of oxygen saturation (Spo2) measurement was 2.2%, meeting FDA/ISO standards. Clinical comparison of the smartphone sensor with app vs hospital reference devices determined that Spo2 and heart rate accuracy were 0.48% points (95% CI, 0.38-0.58; P < .001) and 0.73 bpm (95% CI, 0.33-1.14; P < .001), respectively; Spo2 and heart rate precision were 1.25 vs reference 0.95% points (P < .001) and 5.99 vs reference 3.80 bpm (P < .001), respectively. These small differences were similar to the variation found between two FDA-approved reference instruments for Spo2: accuracy, 0.52% points (95% CI, 0.41-0.64; P < .001) and precision, 1.01 vs 0.86% points (P < .001). INTERPRETATION: Our findings support the application for full FDA/ISO approval of the smartphone sensor with app tested for use in clinical pulse oximetry. Given the immense and immediate practical medical importance of remote intermittent clinical pulse oximetry to both chronic disease management and the global ability to respond to respiratory viral pandemics, the smartphone sensor with app should be prioritized and fast-tracked for FDA/ISO approval to allow clinical use. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT04233827; URL: www.clinicaltrials.gov.

In Vivo Evaluation of a Subcutaneously Injectable Implant with a Low-Power Photoplethysmography ASIC for Animal Monitoring.

Photoplethysmography is an extensively-used, portable, and noninvasive technique for measuring vital parameters such as heart rate, respiration rate, and blood pressure. The deployment of this technology in veterinary medicine has been hindered by the challenges in effective transmission of light presented by the thick layer of skin and fur of the animal. We propose an injectable capsule system to circumvent these limitations by accessing the subcutaneous tissue to enable reliable signal acquisition even with lower light brightness. In addition to the reduction of power usage, the injection of the capsule offers a less invasive alternative to surgical implantation. Our current prototype combines two application-specific integrated circuits (ASICs) with a microcontroller and interfaces with a commercial light emitting diode (LED) and photodetector pair. These ASICs implement a signal-conditioning analog front end circuit and a frequency-shift keying (FSK) transmitter respectively. The small footprint of the ASICs is the key in the integration of the complete system inside a 40-mm long glass tube with an inner diameter of 4 mm, which enables its injection using a custom syringe similar to the ones used with microchip implants for animal identification. The recorded data is transferred wirelessly to a computer for post-processing by means of the integrated FSK transmitter and a software-defined radio. Our optimized LED duty cycle of 0.4% at a sampling rate of 200 Hz minimizes the contribution of the LED driver (only 0.8 mW including the front-end circuitry) to the total power consumption of the system. This will allow longer recording periods between the charging cycles of the batteries, which is critical given the very limited space inside the capsule. In this work, we demonstrate the wireless operation of the injectable system with a human subject holding the sensor between the fingers and the in vivo functionality of the subcutaneous sensing on a pilot study performed on anesthetized rat subjects.

A 280 µW, 108 dB DR PPG-Readout IC With Reconfigurable, 2nd-Order, Incremental ΔΣM Front-End for Direct Light-to-Digital Conversion.

This paper reports on a low-power readout IC (ROIC) for high-fidelity recording of the photoplethysmogram (PPG) signal. The system comprises a highly reconfigurable, continuous-time, second-order, incremental delta-sigma modulator (I-ΔΣM) as a light-to-digital converter (LDC), a 2-channel 10b light-emitting diode (LED) driver, and an integrated digital signal processing (DSP) unit. The LDC operation in intermittent conversion phases coupled with digital assistance by the DSP unit allow signal-aware, on-the-fly cancellation of the dc and ambient light-induced components of the photodiode current for more efficient use of the full-scale input range for recording of the small-amplitude, ac, PPG signal. Fabricated in TSMC 0.18 µm 1P/6M CMOS, the PPG ROIC exhibits a high dynamic range of 108.2 dB and dissipates on average 15.7 µW from 1.5 V in the LDC and 264 µW from 2.5 V in one LED (and its driver), while operating at a pulse repetition frequency of 250 Hz and 3.2% duty cycling. The overall functionality of the ROIC is also demonstrated by high-fidelity recording of the PPG signal from a human subject fingertip in the presence of both natural light and indoor light sources of 60 Hz.

On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks.

Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements using a stack denoising autoencoder (SDAE) and multilayer perceptron neural network (MLPNN). The missing segments were predicted by a personalized convolutional neural network (CNN) and long-short term memory (LSTM) model using a short history of the same channel data. Forty sets of volunteers' data, consisting of equal share of healthy and cardiovascular subjects were used for validation and testing. The PPG reliability assessment model (PRAM) achieved over 95% accuracy for correctly identifying acceptable PPG beats out of total 5000 using expert annotated data. Disagreement with experts' annotation was nearly 3.5%. The missing segment prediction model (MSPM) achieved a root mean square error (RMSE) of 0.22, and mean absolute error (MAE) of 0.11 for 40 missing beats prediction using only four beat history from the same channel PPG. The two models were integrated in a standalone device based on quad-core ARM Cortex-A53, 1.2 GHz, with 1 GB RAM, with 130 MB memory requirement and latency ∼0.35 s per beat prediction with a 30 s frame. The present method also provides improved performance with published works on PPG quality assessment and missing data prediction using two public datasets, CinC and MIMIC-II under PhysioNet.

In-Ear SpO2: A Tool for Wearable, Unobtrusive Monitoring of Core Blood Oxygen Saturation.

The non-invasive estimation of blood oxygen saturation (SpO2) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger-the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings.

Wearable monitoring of sleep-disordered breathing: estimation of the apnea-hypopnea index using wrist-worn reflective photoplethysmography.

A large part of the worldwide population suffers from obstructive sleep apnea (OSA), a disorder impairing the restorative function of sleep and constituting a risk factor for several cardiovascular pathologies. The standard diagnostic metric to define OSA is the apnea-hypopnea index (AHI), typically obtained by manually annotating polysomnographic recordings. However, this clinical procedure cannot be employed for screening and for long-term monitoring of OSA due to its obtrusiveness and cost. Here, we propose an automatic unobtrusive AHI estimation method fully based on wrist-worn reflective photoplethysmography (rPPG), employing a deep learning model exploiting cardiorespiratory and sleep information extracted from the rPPG signal trained with 250 recordings. We tested our method with an independent set of 188 heterogeneously disordered clinical recordings and we found it estimates the AHI with a good agreement to the gold standard polysomnography reference (correlation = 0.61, estimation error = 3±10 events/h). The estimated AHI was shown to reliably assess OSA severity (weighted Cohen's kappa = 0.51) and screen for OSA (ROC-AUC = 0.84/0.86/0.85 for mild/moderate/severe OSA). These findings suggest that wrist-worn rPPG measurements that can be implemented in wearables such as smartwatches, have the potential to complement standard OSA diagnostic techniques by allowing unobtrusive sleep and respiratory monitoring.

A 119dB Dynamic Range Charge Counting Light-to-Digital Converter For Wearable PPG/NIRS Monitoring Applications.

This paper presents a low power, high dynamic range (DR), reconfigurable light-to-digital converter (LDC) for photoplethysmogram (PPG), and near-infrared spectroscopy (NIRS) sensor readouts. The proposed LDC utilizes a current integration and a charge counting operation to directly convert the photocurrent to a digital code, reducing the noise contributors in the system. This LDC consists of a latched comparator, a low-noise current reference, a counter, and a multi-function integrator, which is used in both signal amplification and charge counting based data quantization. Furthermore, a current DAC is used to further increase the DR by canceling the baseline current. The LDC together with LED drivers and auxiliary digital circuitry are implemented in a standard 0.18 µm CMOS process and characterized experimentally. The LDC and LED drivers consume a total power of 196 µW while achieving a maximum 119 dB DR. The charge counting clock, and the pulse repetition frequency of the LED driver can be reconfigured, providing a wide range of power-resolution trade-off. At a minimum power consumption of 87 µW, the LDC still achieves 95 dB DR. The LDC is also validated with on-body PPG and NIRS measurement by using a photodiode (PD) and a silicon photomultiplier (SIPM), respectively.

Binary CorNET: Accelerator for HR Estimation From Wrist-PPG.

Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly successful in mitigating the effect of motion artifacts (MA) in ambulatory environment for HR estimation. Recently, a learning framework, CorNET, employing two-layer convolution neural networks (CNN) and two-layer long short-term network (LSTM) was successfully reported for estimating HR from MA-induced PPG signals. However, such a network topology with large number of parameters presents a challenge, towards low-complexity hardware implementation aimed at on-node processing. In this paper, we demonstrate a fully binarized network (bCorNET) topology and its corresponding algorithm-to-architecture mapping and energy-efficient implementation for HR estimation. The proposed framework achieves a MAE of 6.67 ± 5.49 bpm when evaluated on 22 IEEE SPC subjects. The design, synthesized with ST65 nm technology library achieving 3 GOPS @ 1 MHz, consumes 56.1 µJ per window with occupied 1634K NAND2 equivalent cell area and had a latency of 32 ms when estimating HR every 2 s from PPG signals.

A digital biomarker of diabetes from smartphone-based vascular signals.

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.

Role of wearable rhythm recordings in clinical decision making-The wEHRAbles project.

BACKGROUND: Multiple wearable devices for rhythm analysis have been developed using either photoplethysmography (PPG) or handheld ECG. HYPOTHESIS: The aim of this survey was to assess impact of these technologies on physicians' clinical decision-making regarding initiation of diagnostic steps, drug therapy, and invasive strategies. METHODS: The online survey included 10 questions on types of devices, advantages, and disadvantages of wearable devices as well as case scenarios for patients with supraventricular arrhythmias and atrial fibrillation (AF). RESULTS: A total of 417 physicians (median age 37 [IQR 32-43] years) from 42 countries world-wide completed the survey. When presented a tracing of a regular tachycardia by a symptomatic patient, most participants would trigger further diagnostic steps (90% for single-lead ECG vs 83% for PPG, P < .001), while a single-lead ECG would be sufficient to perform an invasive EP study in approximately half of participants (51% vs 22% for PPG, P < .001). When presented with a single-lead ECG tracing suggesting AF, most participants (90%) would trigger further diagnostic steps. A symptomatic AF patient would trigger anticoagulation treatment to a higher extent as an asymptomatic patient (59% vs 21%, P < .001). PPG tracings would only rarely lead to therapeutic steps regardless of symptoms. Most participants would like scientific society recommendations on the use of wearable devices (62%). CONCLUSIONS: Tracings from wearable rhythm devices suggestive of arrhythmias are most likely to trigger further diagnostic steps, and in the case of PPG recordings rarely therapeutic interventions. A majority of participants expect these devices to facilitate diagnostics and arrhythmia screening but fear data overload and expect scientific society recommendations on the use of wearables.

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.