PPG Measurements Research Articles

Remote heart rhythm assessment using smartphone-based photoplethysmography in primary care to detect undiagnosed atrial fibrillation in patients with chronic obstructive pulmonary disease

Abstract Introduction Atrial fibrillation (AF) is the most common cardiac arrhythmia leading to healthcare consultation in primary care. The increased risk to develop AF when suffering from chronic obstructive pulmonary disease (COPD) has been extensively described in literature. However, it remains challenging to perform in-office, opportunistic screening for AF in COPD patients during daily routine using pulse checks or single-spot ECGs due to the increasing pressure on primary care. Purpose The goal of this study was to evaluate the accuracy and effectiveness of a smartphone-based photoplethysmography (PPG) technology to enable remote heart rhythm monitoring and new AF detection in patients with COPD. Methods Diagnosed COPD patients, without an established AF diagnosis, were identified via the electronic medical record. Patients who fulfilled these criteria and hadn’t had an ECG in the past two years were selected to take part in this study. Other inclusion criteria were access to a smartphone and the physical ability to place their fingertip on a smartphone camera. Eligible patients were contacted via text message and after providing informed consent, the study team invited them to activate a 7-day invitation code to access a medically certified smartphone application. Patients were instructed to perform PPG measurements twice daily and when experiencing symptoms. Those with possible AF based on the PPG recordings were invited for a confirmatory ECG to establish the AF diagnosis. Results In total, 90/270 eligible patients (30%) provided informed consent and activated the on-demand invitation code to remotely monitor their heart rhythm using a PPG-based smartphone application. Of these, 50 (55.6%) were female and the median age was 68 years old (IQR: 59-74). The newly discovered AF detection yield was 5.6% (5/90). In-office 12-lead ECG confirmed AF in 3/5 patients (60%). The remaining patients, with a negative in-office 12-lead ECG, were invited for long-term intermittent ECG monitoring using a handheld ECG device (Zenicor). One patient accepted the proposal and paroxysmal AF was confirmed after 4 weeks of intermittent, symptom-triggered ECG monitoring. The remaining patient was not willing to perform long-term monitoring after the negative in-office ECG. However, this patient was diagnosed with paroxysmal AF as well after presentation at the emergency department while suffering from a transient ischemic attack three months after the initial AF detection using PPG technology. Conclusion Remote heart rhythm assessment using smartphone-based PPG in primary care within a COPD population was feasible and resulted in the detection of newly diagnosed AF in COPD patients. All patients identified with possible AF based on PPG monitoring were confirmed and diagnosed via ECG which enabled the initiation of guideline-based AF management.

Validation of a novel cuffless photoplethysmography-based wristband for measuring blood pressure according to the regulatory standard

Abstract Background Blood pressure is a key paramater in acute and chronic cardiovascular diseases (CVD) [1]. However, obtaining reliable and reproducible cuff blood pressure (BP) measurements is challenging. We demonstrate that a wristband-based PPG measurement in combination with a custom developed BP algorithm represents a possible alternative to cuff BP measurements. Purpose This study aimed to validate a novel cuffless photopletysmography (PPG)-based non-invasive wristband for continuous BP monitoring in accordance with ISO 81060-2:2019. Methods The study compared PPG-guided BP algorithm predictions with subclavian arterial reference measurements taken during cardiac catheterization. Eligible patients were consecutively included, and eligibility was screened following ISO 81060-2:2019 requirements. Reference measurements were performed using a validated invasive BP monitoring device with a sampling rate of 100Hz. PPG signals were collected using six light emission diodes and two photodiodes at a sampling rate of 128Hz. Three sequential initialization measurements were taken using a validated blood pressure cuff before the cardiac catheterization exam. These measurements, along with approximately 100 additional features (PPG-derived and based on patient demographics), were used as input for the machine learning-based BP algorithm. Correlation, mean error, and standard deviation (SD) were determined for systolic and diastolic BP measurements between the BP algorithm predictions and invasive reference measurements Results The study included 97 patients from whom 420 individual 30-second samples were obtained. The mean age, weight, and height of the analysed subjects were 67.1 (SD 11.1), 83.4 (SD 16.1), and 174.1 (SD 10.0), respectively. In 48 samples (11%), systolic BP was ≤100mmHg, while in 106 samples (25%), systolic BP was ≥160mmHg. Diastolic BP was ≤70 mmHg in 222 samples (53%) and ≥85 mmHg in 99 samples (24%). The BP algorithm predictions showed a high correlation with invasive reference measurements for systolic (r = 0.985) and diastolic (r = 0.961) BP measurements. The mean error of the BP algorithm predictions compared to the invasive reference measurements was ±3.7 mmHg (SD 4.4 mmHg) and ±2.5 mmHg (SD 3.7 mmHg) for systolic and diastolic BP, respectively. Results were similar within each gender and skin colour category (Fitzpatrick I-VI). Conclusion This study demonstrates that a wristband-based PPG measurement in combination with the developed BP algorithm can provide accurate continuous BP measurements across a wide range of BP distributions. Therefore, wristband BP monitoring may serve as a valid and less burdensome alternative to cuff BP measurements for both in-hospital and at-home BP monitoring. However, further research is necessary to evaluate the precision of the BP algorithm during movement and the stability of the predictions over time.figure of BP

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation.

Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%. Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.

Non-invasive estimation of arterial blood pressure fluctuations using a peripheral photoplethysmograph inside the MRI scanner.

The blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is modulated by neural activity through the neurovascular coupling effect, as well as non-neural factors of physiological origin such as heart rate, respiration, and arterial blood pressure (ABP). While the former two effects have been previously characterized, the modulation of the BOLD signal by ABP fluctuations is still poorly understood. This is largely due to the difficulty of obtaining reliable ABP measurements in the MRI environment. Here, we propose a combined experimental and mathematical modeling framework to estimate ABP fluctuations inside the MRI scanner using photoplethysmography (PPG). Specifically, we used concurrent PPG and ABP measurements obtained outside the scanner to train the mathematical model and applied it to PPG measurements obtained inside the MRI scanner. Our results suggest good agreement between the model-predicted and experimentally measured ABP fluctuations and region specific correlations with the BOLD fluctuations.

Novel Multi-Parametric Sensor System for Comprehensive Multi-Wavelength Photoplethysmography Characterization.

Photoplethysmography (PPG) is widely used to assess cardiovascular health. However, its usage and standardization are limited by the impact of variable contact force and temperature, which influence the accuracy and reliability of the measurements. Although some studies have evaluated the impact of these phenomena on signal amplitude, there is still a lack of knowledge about how these perturbations can distort the signal morphology, especially for multi-wavelength PPG (MW-PPG) measurements. This work presents a modular multi-parametric sensor system that integrates continuous and real-time acquisition of MW-PPG, contact force, and temperature signals. The implemented design solution allows for a comprehensive characterization of the effects of the variations in these phenomena on the contour of the MW-PPG signal. Furthermore, a dynamic DC cancellation circuitry was implemented to improve measurement resolution and obtain high-quality raw multi-parametric data. The accuracy of the MW-PPG signal acquisition was assessed using a synthesized reference PPG optical signal. The performance of the contact force and temperature sensors was evaluated as well. To determine the overall quality of the multi-parametric measurement, an in vivo measurement on the index finger of a volunteer was performed. The results indicate a high precision and accuracy in the measurements, wherein the capacity of the system to obtain high-resolution and low-distortion MW-PPG signals is highlighted. These findings will contribute to developing new signal-processing approaches, advancing the accuracy and robustness of PPG-based systems, and bridging existing gaps in the literature.

Diffuse transmittance visible spectroscopy using smartphone flashlight for photoplethysmography and vital signs measurements

Photoplethysmography (PPG), with its wide range of applications, has become one of the most promising modalities for healthcare monitoring technology. In this work, we present a new PPG measurement technique based on diffuse transmittance spectroscopy (DTS) with the help of a smartphone built-in flashlight as an alternative broadband light source. The blood Volume Pulse (BVP) signal was extracted from recorded transmittance spectra at 620 nm. The results were compared with the ground truth and conventional contact finger PPG sensors. A very high correlation was found between the diffuse transmittance signal and the reference PPG signals (r = 0.997, p < 0.0001). The accuracy and root mean square error (RMSE) were 99.23% and 0.8 bpm, respectively. In addition, a Bland-Altman analysis showed a good agreement between both techniques, with a very small bias between mean paired differences of heart rate observations. A simple forward model for diffuse transmittance spectra for different levels of blood oxygen saturation is developed and supported by experimental measurements. It was also found that blood oxygen saturation (SpO2) can be estimated with the aid of DTS based smartphone flash by tracking the wavelength corresponding to the oxygenation level in the visible range between orange and red regions of the visible spectrum particularly in the range between 610 and 635 nm for 26 healthy subjects. 624 nm on average seems to be the wavelength that corresponds with the normal blood oxygenation level. These findings show the potential of DTS PPG to reliably extract cardiac frequency and estimate SpO2 with adequate accuracy. The results also demonstrate the capability of smartphone flash as a miniature visible light source for recording multispectral PPG signals and quantifying vital signs in the transmission mode at the fingertip with acceptable signal quality over a wide range of wavelengths from 550 nm to 650 nm.

Photoplethysmography in Wearable Devices: A Comprehensive Review of Technological Advances, Current Challenges, and Future Directions

Photoplethysmography (PPG) is an affordable and straightforward optical technique used to detect changes in blood volume within tissue microvascular beds. PPG technology has found widespread application in commercial medical devices, enabling measurements of oxygen saturation, blood pressure, and cardiac output; the assessment of autonomic nerve function; and the diagnosis of peripheral vascular disease. Recently, the growing demand for non-invasive, portable, cost-effective technology, along with advancements in small semiconductor components, has led to the integration of PPG into various wrist-worn wearable devices. Multiple sensor structures have been proposed and, through appropriate signal processing and algorithmic application, these wearable devices can measure a range of health indicators during daily life. This paper begins by addressing the market status of wrist-worn wearable devices, followed by an explanation of the fundamental principles underlying light operation and its interaction with living tissue for PPG measurements. Moving on to technological advancements, the paper addresses the analog front end for the measurement of the PPG signal, sensor configurations with multiple light emitters and receivers, the minimum sampling rate required for low-power systems, and the measurement of stress, sleep, blood pressure, blood glucose, and activity using PPG signals. Several challenges in the field are also identified, including selecting the appropriate wavelength for the PPG sensor’s light source, developing low-power interpolation methods to extract high-resolution inter-beat intervals at a low sampling rate, and exploring the measurement of physiological phenomena using multi-wavelength PPG signals simultaneously collected at the same location. Lastly, the paper presents future research directions, which encompass the development of new, reliable parameters specific to wearable PPG devices and conducting studies in real-world scenarios, such as 24-h long-term measurements.

Deep learning identification of coronary artery disease from bilateral finger photoplethysmography sensing: A proof-of-concept study

BackgroundA proof-of-concept study assessing a novel approach to identify patients with coronary artery disease (CAD) using deep learning analysis of bilateral-site photoplethysmography (PPG) waveforms (“DL-PPG”). MethodologyDL-PPG was studied in 37 participants (with 21 having CAD). Scalogram ‘spectral’ images were derived from right and left index finger PPG measurements collected using a 3-phase protocol (baseline, unilateral arm pressure cuff occlusion, reactive hyperaemia flush). Artificial Intelligence (AI) analysis, namely deep learning, was employed for scalogram image classification using a Convolutional Neural Network (CNN, “GoogLeNet”), with classification performance obtained using 10-fold stratified cross validation (CV). A conventional machine learning (ML) classifier (K-nearest neighbour, K-NN, K = 9) was also evaluated for comparison with the CNN deep learning methodology. Blood samples were also collected giving 2 biochemical biomarkers of endothelial function. Test sensitivities, specificities, accuracies, and Kappa statistics were determined. ResultsDL-PPG sensitivity was 80.9 % (95% CI, 78.6–83.0), specificity 87.7% (85.5–89.7), accuracy 83.8 % (82.2–85.3), and Kappa 0.68 (0.65–0.71). Comparative K-NN ML performance was 69.4% (95% CI, 68.7–70.1), 37.5% (36.7–38.2), 53.9% (53.3–54.4), and 0.069 (0.058–0.079), respectively. No differences between patients and controls were found for the biochemical biomarkers of endothelial function. ConclusionSubstantial overall agreement was found between DL-PPG classification and CAD angiography, with DL-PPG performance clearly better than for a conventional ML technique. Our deep learning classification approach, using only basic pre-processing of the PPG pulse waveforms before classification, could offer significant benefits for the diagnosis of CAD in a variety of clinical settings needing low-cost portable and easy-to-use diagnostics.

Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism

Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME ± STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21 ± 7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure.

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

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. 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). 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. 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%. 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.

Correlation analysis of HbA1c versus random, fasting, and postprandial glucose levels as predictors of glycemic control in type 2 diabetes patients

Background: Glycated Hemoglobin (HbA1c) is used for assessing glycemic control over the past 8–12 weeks. This is critical for determining the efficacy of diabetes treatment and predicting the progression of microvascular complications. However, in health-care situations where tests for HbA1c are either unavailable or unfeasible for any reason, clinicians rely only on plasma glucose values for assessing the glycemic control of the patient. Aims and Objectives: The purpose of this study was to determine the relationship between HbA1c and fasting, postprandial, and random plasma glucose levels. Materials and Methods: Routine blood samples obtained from the laboratory to measure HbA1c and plasma glucose (fasting/postprandial/random) were used for study. A total of 207 samples were used to investigate the relationship between HbA1c and fasting and postprandial glucose (PPG/PP). The correlation of HbA1c with random glucose values was investigated using 112 samples. HbA1c was estimated by immunoturbidimetry and glucose was estimated by hexokinase method. Pearson’s correlation analysis was done by SPSS version 20 software. Results: The Pearson correlation coefficient (r) with regard to PPG/PP and HbA1c was 0.75 (P=0.01, 95% CI), fasting glucose and HbA1c was 0.73 (P=0.01, 95% CI), and random glucose and HbA1c was 0.59 (P=0.01, 95% CI). Conclusion: PPG/PP correlates with HbA1c better than fasting or random glucose. When it is impossible to perform HbA1c measurements, PPG measurements should be used instead.

A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram

Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.

Brief research report: Photoplethysmography pulse sensors designed to detect human heart rates are ineffective at measuring horse heart rates

This study sought to evaluate the accuracy of a PPG (photoplethysmography) sensor designed to measure human heart rates in monitoring the distal limb pulse of healthy adult horses. We hypothesized that the PPG sensor is sensitive to placement location and orientation, and that measurement accuracies depend on placement and orientation on the limb. To evaluate this hypothesis, a completely randomized block design with a factorial treatment structure was used. Horses were considered as the block. Limb type (right front, left front, right hind, and left hind) and position of sensor (medial or lateral) were treatments, with levels arranged in a complete (4x2) factorial design. Data were collected by placing the PPG sensor on the limb of each horse (n = 6), with placement location according to the treatment (limb type and location) combination, and taking pulse readings for 60 seconds. Manual heart rates were collected concurrently using a stethoscope. Data were analyzed by calculating root mean square errors (RMSE) for the PPG measurements with the manual heart rates as a gold standard. Variation in RMSE associated with limb and location of sensor were evaluated using a general linear model with fixed effects for limb and location and a random effect for horse. Our results indicated that the PPG sensor was ineffective at measuring horse heart rates, and that the device was insensitive to placement location and orientation. Future work should focus on developing alternative analytics to interpret the data from PPG sensors to better reflect horse heart rates.

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor.

Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to 0.95 were obtained for the SCL component, and a mean consensus of 69% for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of 99.46% and 87.85% were obtained, respectively. Moreover, an HR estimation error of 17.40±14.80 Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring.

A DSP-Based EBI, ECG, and PPG Measurement Platform

A DSP-Based EBI, ECG, and PPG Measurement Platform

Assessment of a Non-Invasive Brain Pulse Monitor to Measure Intra-Cranial Pressure Following Acute Brain Injury.

Intracranial pressure (ICP) monitoring requires placing a hole in the skull through which an invasive pressure monitor is inserted into the brain. This approach has risks for the patient and is expensive. We have developed a non-invasive brain pulse monitor that uses red light to detect a photoplethysmographic (PPG) signal arising from the blood vessels on the brain's cortical surface. The brain PPG and the invasive ICP waveform share morphological features which may allow measurement of the intracranial pressure. We enrolled critically ill patients with an acute brain injury with invasive ICP monitoring to assess the new monitor. A total of 24 simultaneous invasive ICP and brain pulse monitor PPG measurements were undertaken in 12 patients over a range of ICP levels. The waveform morphologies were similar for the invasive ICP and brain pulse monitor PPG approach. Both methods demonstrated a progressive increase in the amplitude of P2 relative to P1 with increasing ICP levels. An automated algorithm was developed to assess the PPG morphological features in relation to the ICP level. A correlation was demonstrated between the brain pulse waveform morphology and ICP levels, R2=0.66,P < 0.001. The brain pulse monitor's PPG waveform demonstrated morphological features were similar to the invasive ICP waveform over a range of ICP levels, these features may provide a method to measure ICP levels. ACTRN12620000828921.

Measuring Suite for Vascular Response Monitoring during Hyperbaric Oxygen Therapy by Means of Pulse Transit Time (PTT) Analysis.

The efficacy of hyperbaric oxygen therapy in treating wound healing disorders is well established. The obvious explanation is the presence of elevated oxygen tissue tensions during the high-pressure oxygen exposure. This explanation omits that the effective agent, elevated oxygen tension, is only present for 6.25% of the time. To investigate possible prevailing vascular changes caused by HBOT, the presented device monitors the vascular response during therapy by Pulse-Transit-Time analysis. The device allows synchronous 1 kHz ECG and PPG measurements. The data are stored in a 1 GBit flash drive and retrieved post-therapy. Normoxic measurements on the authors with and without nicotine validate the device's functionality. Measurements during HBO therapy have been successfully performed.

Transjugular intrahepatic portosystemic shunt in portal hypertension: How to go further while staying on track?

Transjugular intrahepatic portosystemic shunt in portal hypertension: How to go further while staying on track?

Atrial fibrillation can be diagnosed using a combined wrist-worn photoplethysmography and electrocardiography

Abstract Background Current ESC Clinical Practice Guideline for management of atrial fibrillation requires a minimum of 30-seconds of atrial fibrillation (AF) to be visually confirmed from an electrocardiography (ECG) recording for diagnosis. Single-channel ECG recorded from any measurement lead is sufficient. The current method of 24–48 h Holter ECG may not detect rarely occurring AF episodes, and thus extended duration recording devices are needed. Intermittent portable ECG devices intended for clinical use exist but those lack continuous rhythm monitoring and are thus unable to notify the user in case of asymptomatic episode. Purpose We evaluated the performance of a wrist-worn solution (Fig. 1) intended for clinical use that combines continuous photoplethysmography (PPG) -based rhythm assessment with intermittent ECG measurements in the detection of atrial fibrillation in ambulatory conditions. Methods 30 adult patients with previous diagnosis of AF were recruited for 48-hour measurements. Simultaneous 5-lead Holter monitoring was used as the reference. The patients were taking intermittent ECG measurements at pre-scheduled times (4 times a day), when notified by the device (irregular rhythm detected by PPG), and if feeling symptoms of AF. Results The obtained sensitivity and specificity for PPG-based AF detection were 95.5% and 99.2%, respectively when evaluated in 5-minute segments. Only the segments during which adequate amount, approximately 30-seconds, of good quality PPG data was available, were considered in the analysis. On average this equals 54% of the segments. Patients took in total 456 30-second ECG measurements. Out of these 411 and 392 were assessed as analysable quality by ECG algorithm and visually by two cardiologists, HS and KK, respectively. Automatic ECG analysis algorithm labelled ECG measurements taken during atrial fibrillation correctly as “Possible arrhythmia” with 95.7% sensitivity and 92.0% specificity. 99.5% of the analysable quality ECGs measured during AF were marked with any type of label e.g., tachycardia that highlights them to the clinician. An example ECG measurement with markings of the analysis algorithm an overall classification is shown in Fig. 2. All patients having AF during the 48-hour monitoring (14/30) were correctly identified based on the wrist device data. Conclusion Based on the obtained results, wrist-worn device combining continuous PPG and intermittent ECG measurements can be reliably used in detecting atrial fibrillation in ambulatory conditions. The user-friendliness and unobtrusiveness of wrist-worn technology enables virtually unlimited monitoring time thus facilitating the detection of rarely occurring AF episodes. The information of continuous rhythm monitoring can also be used for the estimation of AF burden. The solution used in the study has received medical device CE certification in early 2022. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): The study was funded by PulseOn company.

Separate extraction of human eccrine sweat gland activity and peripheral hemodynamics from high- and low-quality thermal imaging data

Sweat gland activity and peripheral hemodynamics, which characterize the function of sympathetic cholinergic nerve fibers and the manifestation mechanisms of vascular tone regulation, respectively, can be detected via dynamic thermography of the skin. Thus, they are useful parameters for diagnosing various forms of neuropathy and functional circulatory disorders. Both parameters affect the dynamics of the skin temperature; therefore, for an adequate description of thermographic data, it is necessary to build models that consider both these coexisting components simultaneously. PurposeThe objective of this study was to determine the spatiotemporal and statistical features of dynamic thermograms of skin areas with sweat glands and to develop methods for the extraction of temperature components mediated by sweat gland activity (Tsweat) separately from hemodynamics (Tblood) based on thermograms of high and low temperature resolutions. MethodsTo separate the Tsweat and Tblood components, simultaneous thermographic and photoplethysmographic (PPG) measurements were performed in the area of the fingers during a deep inspiratory gasp (DIG). PPG data, which were obtained solely by hemodynamics, were converted into a temperature signal (Tblood) using the spectral filtering approach. By calculating the difference between the skin (Tskin) and blood (Tblood) temperature components, the Tsweat component was determined, which characterizes sweat gland activity and the integrity of the cholinergic sympathetic nerve fibers that innervate them. The Tsweat component was compared with the active sweat pore count curve, which was determined by the adaptive detection of local temperature minima. Thermographic and PPG measurements were performed for 3 min on a group of 15 volunteers during the DIG test. The skin temperature was measured using a cooled thermal imaging camera in the spectral range of 8–9 μm with a temperature sensitivity of 0.02 °C. PPG measurements were performed using a reflectance sensor with a central wavelength of 800 nm. Wavelet analysis with the Morlet basis function was used to preliminarily determine the spectrum of spontaneous temperature oscillations in an area of the skin with and without sweat pores. Statistical parameters of the histogram, such as the standard deviation and the statistical pore activation index (SPAI) - which is proposed in this paper were used in the DIG test to detect sweat gland activity with low-temperature resolution thermograms. The temporal dynamics of the statistical parameters were compared with the dynamics of the sweat pore count. ResultsThe Tsweat component was correlated with the sweat pore count on the thermogram with a coefficient of 0.75, confirming the dependence of this temperature component on the sweat gland activity and the need for considering this activity in the analysis of the spatiotemporal dynamics of the human skin temperature. The use of the proposed SPAI and standard deviation allows the detection of sweat gland activity even with thermograms of a low temperature resolution. The use of an integrated map of the sweat gland activity will help the specialist to assess the degree of integrity of the innervation of skin areas in a single image. The primary assessment of the spectrum of temperature oscillations at rest indicated that spontaneous sweat gland activity is accompanied by high-frequency oscillations in the skin temperature localized in the area of sweat pores, within the frequency range of 0.07–0.3 Hz. This suggests the possibility of spectral separation of the temperature component mediated by sweat gland activity from the hemodynamic component, which dominates in the region of <0.1 Hz. The proposed two-component approach for the analysis of skin temperature dynamics allows separate assessment of sympathetic innervation and rhythms of hemodynamic regulation using dynamic thermograms.