Showing papers in "Physiological Measurement in 2020"

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

Abstract: 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.

Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability

Abstract: Heart rate variability has been largely used for the assessment of cardiac autonomic activity, due to the direct relationship between cardiac rhythm and the activity of the sympathetic and parasympathetic nervous system. In recent years, another technique, pulse rate variability, has been used for assessing heart rate variability information from pulse wave signals, especially from photoplethysmography, a non-invasive, non-intrusive, optical technique that measures the blood volume in tissue. The relationship, however, between pulse rate variability and heart rate variability is not entirely understood, and the effects of cardiovascular changes in pulse rate variability have not been thoroughly elucidated. In this review, a comprehensive summary of the applications in which pulse rate variability has been used, with a special focus on cardiovascular health, and of the studies that have compared heart rate variability and pulse rate variability is presented. It was found that the relationship between heart rate variability and pulse rate variability is not entirely understood yet, and that pulse rate variability might be influenced not only due to technical aspects but also by physiological factors that might affect the measurements obtained from pulse-to-pulse time series extracted from pulse waves. Hence, pulse rate variability must not be considered as a valid surrogate of heart rate variability in all scenarios, and care must be taken when using pulse rate variability instead of heart rate variability. Specifically, the way pulse rate variability is affected by cardiovascular changes does not necessarily reflect the same information as heart rate variability, and might contain further valuable information. More research regarding the relationship between cardiovascular changes and pulse rate variability should be performed to evaluate if pulse rate variability might be useful for the assessment of not only cardiac autonomic activity but also for the analysis of mechanical and vascular autonomic responses to these changes.

A comprehensive guideline for Bland-Altman and intra class correlation calculations to properly compare two methods of measurement and interpret findings.

Abstract: The rapid emergence of new measurement instruments and methods requires personnel and researchers of different disciplines to know the correct statistical methods to utilize to compare their performance with reference ones and properly interpret findings. We discuss the often-made mistake of applying the inappropriate correlation and regression statistical approaches to compare methods and then explain the concepts of agreement and reliability. Then, we introduce the intraclass correlation as a measure of inter-rater reliability, and the Bland-Altman plot as a measure of agreement, and we provide formulae to calculate them along with illustrative examples for different types of study designs, specifically single measurement per subject, repeated measurement while the true value is constant, and repeated measurement when the true value is not constant. We emphasize the requirement to validate the assumptions of these statistical approaches, and also how to deal with violations and provide formulae on how to calculate the confidence interval for estimated values of agreement and intraclass correlation. Finally, we explain how to interpret and report the findings of these statistical analyses.

Remote health diagnosis and monitoring in the time of COVID-19.

Abstract: Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.

Thoracic electrical impedance tomography in Chinese hospitals: a review of clinical research and daily applications

Abstract: Chinese scientists and researchers have a long history with electrical impedance tomography (EIT), which can be dated back to the 1980s. No commercial EIT devices for chest imaging were available until the year 2014 when the first device received its approval from the China Food and Drug Administration. Ever since then, clinical research and daily applications have taken place in Chinese hospitals. Up to this date (2019.11) 47 hospitals have been equipped with 50 EIT devices. Twenty-three SCI publications are recorded and a further 21 clinical trials are registered. Thoracic EIT is mainly used in patients before or after surgery, or in intensive care units (ICU). Application fields include the development of strategies for protective lung ventilation (e.g. tidal volume and positive end-expiratory pressure (PEEP) titration, recruitment, choice of ventilation mode and weaning from ventilator), regional lung perfusion monitoring, perioperative monitoring, and potential feedback for rehabilitation. The main challenges for promoting clinical use of EIT are the financial cost and the education of personnel. In this review, the past, present and future of EIT in China are introduced and discussed.

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Abstract: Objective Heart abnormality detection using heart sound signals (phonocardiogram (PCG)) has been an active research area for the last few decades. In this paper, automatic heart sound classification using segmented and unsegmented PCG signals is presented. Approach In this paper: (i) we perform an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance; (ii) both segmented and unsegmented PCG signals are studied and important results concerning the respective feature subsets and their classification performances are reported; and (iii) different classification algorithms, including the support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory network (LSTMs), are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. Main results It is observed that LSTM performs better on mel-frequency cepstral coefficient (MFCC) features extracted from unsegmented PCG data, with an area under curve (AUC) score of 91.39%, however, the MFCC features do not show a consistent performance with other classifiers (the second highest AUC score is 62.08% with the decision tree classifier). In contrast, in the case of time-frequency features from segmented data, the performance of all the classifiers is appreciable with AUC scores over 70%. In particular, the conventional machine learning techniques shows consistency in achieving over 80% in AUC scores. Significanc e: The results of this study highlight the importance of time and frequency domain features. Thus it is necessary to employ both the time and frequency features of segmented PCG signals to achieve improved classification.

Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test.

Abstract: Objective Smartphone devices may enable out-of-clinic assessments in chronic neurological diseases. We describe the Draw a Shape (DaS) Test, a smartphone-based and remotely administered test of Upper Extremity (UE) function developed for people with multiple sclerosis (PwMS). This work introduces DaS-related features that characterise UE function and impairment, and aims to demonstrate how multivariate modelling of these metrics can reliably predict the 9-Hole Peg Test (9HPT), a clinician-administered UE assessment in PwMS. Approach The DaS Test instructed PwMS and healthy controls (HC) to trace predefined shapes on a smartphone screen. A total of 93 subjects (HC, n = 22; PwMS, n = 71) contributed both dominant and non-dominant handed DaS tests. PwMS subjects were characterised as those with normal (nPwMS, n = 50) and abnormal UE function (aPwMS, n = 21) with respect to their average 9HPT time (≤ or > 22.7 (s), respectively). L 1-regularization techniques, combined with linear least squares (OLS, IRLS), or non-linear support vector (SVR) or random forest (RFR) regression were investigated as functions to map relevant DaS features to 9HPT times. Main results It was observed that average non-dominant handed 9HPT times were more accurately predicted by DaS features (r 2 = 0.41, [Formula: see text] 0.05; MAE: 2.08 ± 0.34 (s)) than average dominant handed 9HPTs (r 2 = 0.39, [Formula: see text] 0.05; MAE: 2.32 ± 0.43 (s)), using simple linear IRLS ([Formula: see text] 0.01). Moreover, it was found that the Mean absolute error (MAE) in predicted 9HPTs was comparable to the variability of actual 9HPT times within HC, nPwMS and aPwMS groups respectively. The 9HPT however exhibited large heteroscedasticity resulting in less stable predictions of longer 9HPT times. Significance This study demonstrates the potential of the smartphone-based DaS Test to reliably predict 9HPT times and remotely monitor UE function in PwMS.

Investigating the origin of photoplethysmography using a multiwavelength Monte Carlo model.

Abstract: Photoplethysmography (PPG) is a photometric technique used for the measurement of volumetric changes in the blood The recent interest in new applications of PPG has invigorated more fundamental research regarding the origin of the PPG waveform, which since its discovery in 1937, remains inconclusive A handful of studies in the recent past have explored various hypotheses for the origin of PPG These studies relate PPG to mechanical movement, red blood cell orientation or blood volume variations Objective Recognising the significance and need to corroborate a theory behind PPG formation, the present work rigorously investigates the origin of PPG based on a realistic model of light-tissue interactions Approach A three-dimensional comprehensive Monte Carlo model of finger-PPG was developed and explored to quantify the optical entities pertinent to PPG (eg absorbance, reflectance, and penetration depth) as the functions of multiple wavelengths and source-detector separations Complementary to the simulations, a pilot in vivo investigation was conducted on eight healthy volunteers PPG signals were recorded using a custom-made multiwavelength sensor with an adjustable source-detector separation Main results Simulated results illustrate the distribution of photon-tissue interactions in the reflectance PPG geometry The depth-selective analysis quantifies the contributions of the dermal and subdermal tissue layers in the PPG wave formation A strong negative correlation (r = -096) is found between the ratios of the simulated absorbances and measured PPG amplitudes Significance This work quantified for the first time the contributions of different tissue layers and sublayers in the formation of the PPG signal

Investigating the physiological mechanisms of the photoplethysmogram features for blood pressure estimation.

Abstract: OBJECTIVE Photoplethysmogram (PPG) signals have been widely used to estimate blood pressure (BP) cufflessly and continuously. A number of different PPG features have been proposed and extracted from PPG signals with the aim of accurately estimating BP. However, the underlying physiological mechanisms of PPG-based BP estimation still remain unclear, particularly those corresponding to various PPG features. In this study, the physiological mechanisms of PPG features for BP estimation were investigated, which may provide further insight. APPROACH Experiments with cold stimuli and an exercise trial were designed to change the total peripheral vascular resistance (TPR) and cardiac output (CO), respectively. Instantaneous BP and continuous PPG signals from 12 healthy subjects were recorded throughout the experiments. A total of 65 PPG features were extracted from the original, the first derivative, and the second derivative waves of PPG. The significance of the change of PPG features in the cold stimuli phase and in the early exercise recovery period was compared with that in the baseline phase. MAIN RESULTS Intensity-specific PPG features changed significantly (p < 0.05) in the cold stimuli phase compared with the baseline phase, demonstrating that they were TPR-correlated. Time-specific PPG features changed significantly (p < 0.05) in the early exercise recovery period compared with the baseline phase, suggesting they were CO-correlated. Most of the PPG features associated with slope and area changed obviously both in the cold stimuli phase and in the early exercise recovery period, indicating that they should be TPR-correlated and CO-correlated. SIGNIFICANCE The findings of this study explained the intrinsic physiological mechanisms underlying PPG features used for BP estimation, and provided insights for exploring more diagnostic applications of the PPG features.

Data preprocessing methods for electrical impedance tomography: a review.

Abstract: Objective Electrical impedance tomography (EIT) is a promising measurement technique in applications, especially in industrial monitoring and clinical diagnosis. However, two major drawbacks exist that limit the spatial resolution of reconstructed EIT images, i.e. the 'soft field' effect and the ill-posed problem. In recent years, apart from the development of reconstruction algorithms, some preprocessing methods for measured data or sensitivity maps have also been proposed to reduce these negative effects. It is necessary to find the optimal preprocessing method for various EIT reconstruction algorithms. Approach In this paper, seven typical data preprocessing methods for EIT are reviewed. The image qualities obtained using these methods are evaluated and compared in simulations, and their applicable ranges and combination effects are summarized. Main results The results show that all the reviewed methods can enhance the quality of EIT reconstructed images to different extents, and there is an optimal one under any given reconstruction algorithm. In addition, most of the reviewed methods do not work well when using the Tikhonov regularization algorithm. Significance This paper introduces the preprocessing method to EIT, and the quality of reconstructed images obtained using these methods is evaluated through simulations. The results can provide a reference for practical applications.

Comparison of different modulations of photoplethysmography in extracting respiratory rate: from a physiological perspective.

Abstract: OBJECTIVE Based on different physiological mechanisms, the respiratory modulations of photoplethysmography (PPG) signals differ in strength and resultant accuracy of respiratory frequency (RF) estimations. We aimed to investigate the strength of different respiratory modulations and the accuracy of resultant RF estimations in different body sites and two breathing patterns. APPROACH PPG and reference respiratory signals were simultaneously measured over 60 s from 36 healthy subjects in six sites (arm, earlobe, finger, forehead, wrist-under (volar side), wrist-upper (dorsal side)). Respiratory signals were extracted from PPG recordings using four demodulation approaches: amplitude modulation (AM), baseline wandering (BW), frequency modulation (FM) and filtering. RFs were calculated from the PPG-derived and reference respiratory signals. To investigate the strength of respiratory modulations, the energy proportion in the range that covers 75% of the total energy in the reference respiratory signal, with RF in the middle, was calculated and compared between different modulations. Analysis of variance and the Scheirer-Ray-Hare test were performed with post hoc analysis. MAIN RESULTS In normal breathing, FM was the only modulation whose RF was not significantly different from the reference RF (p > 0.05). Compared with other modulations, FM was significantly higher in energy proportion (p 0.05), but both were significantly different from the other four sites (p 0.05). The RF estimation error of FM was significantly less than that of AM or BW (p < 0.05). The energy proportion of FM was significantly higher than that of other modulations (p < 0.05). SIGNIFICANCE Of all the respiratory modulations, FM has the highest strength and is appropriate for accurate RF estimation from PPG signals recorded at different sites and for different breathing patterns.

A practical approach to storage and retrieval of high-frequency physiological signals.

Abstract: Objective Storage of physiological waveform data for retrospective analysis presents significant challenges. Resultant data can be very large, and therefore becomes expensive to store and complicated to manage. Traditional database approaches are not appropriate for large scale storage of physiological waveforms. Our goal was to apply modern time series compression and indexing techniques to the problem of physiological waveform storage and retrieval. Approach We deployed a vendor-agnostic data collection system and developed domain-specific compression approaches that allowed long term storage of physiological waveform data and other associated clinical and medical device data. The database (called AtriumDB) also facilitates rapid retrieval of retrospective data for high-performance computing and machine learning applications. Main results A prototype system has been recording data in a 42-bed pediatric critical care unit at The Hospital for Sick Children in Toronto, Ontario since February 2016. As of December 2019, the database contains over 720,000 patient-hours of data collected from over 5300 patients, all with complete waveform capture. One year of full resolution physiological waveform storage from this 42-bed unit can be losslessly compressed and stored in less than 300 GB of disk space. Retrospective data can be delivered to analytical applications at a rate of up to 50 million time-value pairs per second. Significance Stored data are not pre-processed or filtered. Having access to a large retrospective dataset with realistic artefacts lends itself to the process of anomaly discovery and understanding. Retrospective data can be replayed to simulate a realistic streaming data environment where analytical tools can be rapidly tested at scale.

Identification of patients with atrial fibrillation: a big data exploratory analysis of the UK Biobank.

Abstract: Atrial Fibrillation (AF) is the most common cardiac arrhythmia, with an estimated prevalence of around 1.6% in the adult population. The analysis of the Electrocardiogram (ECG) data acquired in the UK Biobank represents an opportunity to screen for AF in a large sub-population in the UK. The main objective of this paper is to assess ten machine-learning methods for automated detection of subjects with AF in the UK Biobank dataset. Six classical machine-learning methods based on Support Vector Machines are proposed and compared with state-of-the-art techniques (including a deep-learning algorithm), and finally a combination of a classical machine-learning and deep learning approaches. Evaluation is carried out on a subset of the UK Biobank dataset, manually annotated by human experts. The combined classical machine-learning and deep learning method achieved an F1 score of 84.8% on the test subset, and a Cohen's Kappa coefficient of 0.83, which is similar to the inter-observer agreement of two human experts. The level of performance indicates that the automated detection of AF in patients whose data have been stored in a large database, such as the UK Biobank, is possible. Such automated identification of AF patients would enable further investigations aimed at identifying the different phenotypes associated with AF.

Personalized automatic sleep staging with single-night data: a pilot study with Kullback-Leibler divergence regularization.

Abstract: Objective: Brain waves vary between people. This work aims to improve automatic sleep staging for longitudinal sleep monitoring via personalization of algorithms based on individual characteristics extracted from the first night of data. Approach: As a single night is a very small amount of data to train a sleep staging model, we propose a Kullback-Leibler (KL) divergence regularized transfer learning approach to address this problem. We employ the pretrained SeqSleepNet (i.e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model. This is done by adding the KL divergence between the output of the subject independent model and the output of the personalized model to the loss function during finetuning. In effect, KL-divergence regularization prevents the personalized model from overfitting to the single-night data and straying too far away from the subject independent model. Main results: Experimental results on the Sleep-EDF Expanded database with 75 subjects show that sleep staging personalization with a single-night data is possible with help of the proposed KL-divergence regularization. On average, we achieve a personalized sleep staging accuracy of 79.6%, a Cohen's kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of 71.8%, and a specificity of 94.2%. Significance: We find both that the approach is robust against overfitting and that it improves the accuracy by 4.5 percentage points compared to non-personalization and 2.2 percentage points compared to personalization without regularization.

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis.

Abstract: OBJECTIVE Multi-frequency symmetry difference electrical impedance tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfully applied to identify the aetiology of stroke with the aid of machine learning. METHODS Anatomically realistic four-layer finite element method models of the head based on stroke patient images are developed and used to generate EIT data over a 5 Hz-100 Hz frequency range with and without bleed and clot lesions present. Reconstruction generates conductivity maps of each head at each frequency. Application of a quantitative metric assessing changes in symmetry across the sagittal plane of the reconstructed image and over the frequency range allows lesion detection and identification. The algorithm is applied to both simulated and human (n = 34 subjects) data. A classification algorithm is applied to the metric value in order to differentiate between normal, haemorrhage and clot values. MAIN RESULTS An average accuracy of 85% is achieved when MFSD-EIT with support vector machines (SVM) classification is used to identify and differentiate bleed from clot in human data, with 77% accuracy when differentiating normal from stroke in human data. CONCLUSION Applying a classification algorithm to metrics derived from MFSD-EIT images is a novel and promising technique for detection and identification of perturbations in static scenes. SIGNIFICANCE The MFSD-EIT algorithm used with machine learning gives promising results of lesion detection and identification in challenging conditions like stroke. The results imply feasible translation to human patients.

Neural network-based supervised descent method for 2D electrical impedance tomography

Abstract: Objective: In this work, we study the application of the neural network based supervised descent method (NN-SDM) for 2D electrical impedance tomography. Approach: NN-SDM contains two stages: o_ine training and online prediction. In the o_ine stage, neural networks are iteratively applied to learn a sequence of descent directions for minimizing the objective function, where the training data set is generated in advance according to prior information or historical data; in the online stage, the trained neural networks are directly used to predict the descent directions. Main results: Numerical and experimental results are reported to assess the e_ciency and accuracy of NN-SDM for both model-based and pixel-based inversions. In addition, the performance of NN-SDM is also compared with linear SDM (LSDM), end-to-end neural network (E2E-NN) and Gauss-Newton method (GN). The results demonstrate that NN-SDM achieves faster convergence than LSDM and GN, and stronger generalization ability than E2E-NN. Signi_cance: NN-SDM combines the strong nonlinear _tting ability of neural network and good generalization capability of the supervised descent method (SDM), which also provides good exibility to incorporate prior information and accelerates the convergence of iteration.

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising.

Abstract: Objective Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. However, the low signal-to-noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Quality improvement of the fetal ECG is of great importance for providing accurate information to enable support in medical decision-making. In this paper we propose the use of artificial intelligence for the task of one-channel fetal ECG enhancement as a post-processing step after maternal ECG suppression. Approach We propose a deep fully convolutional encoder-decoder framework, learning end-to-end mappings from noise-contaminated fetal ECGs to clean ones. Symmetric skip-layer connections are used between corresponding convolutional and transposed convolutional layers to help recover the signal details. Main results Experiments on synthetic data show an average improvement of 7.5 dB in the signal-to-noise ratio (SNR) for input SNRs in the range of -15 to 15 dB. Application of the method with real signals and subsequent ECG interval analysis demonstrates a root mean square error of 9.9 and 14 ms for the PR and QT intervals, respectively, when compared with simultaneous scalp measurements. The proposed network can achieve substantial noise removal on both synthetic and real data. In cases of highly noise-contaminated signals some morphological features might be unreliably reconstructed. Significance The presented method has the advantage of preserving individual variations in pulse shape and beat-to-beat intervals. Moreover, no prior knowledge on the power spectra of the noise or the pulse locations is required.

Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform.

Abstract: Myocardial ischemia is one of the most common cardiovascular pathologies and can indicate many severe and life threatening diseases. Despite these risks, current electrocardiographic detection techniques for ischemia are mediocre at best, with reported sensitivity and specificity ranging from 50%-70% and 70%-90%, respectively. OBJECTIVE To improve this performance, we set out to develop an experimental preparation to induce, detect, and analyze bioelectric sources of myocardial ischemia and determine how these sources reflect changes in body-surface potential measurements. APPROACH We designed the experimental preparation with three important characteristics: (1) enable comprehensive and simultaneous high-resolution electrical recordings within the myocardial wall, on the heart surface, and on the torso surface; (2) develop techniques to visualize these recorded electrical signals in time and space; and (3) accurately and controllably simulate ischemic stress within the heart by modulating the supply of blood, the demand for perfusion, or a combination of both. MAIN RESULTS To achieve these goals we designed comprehensive system that includes (1) custom electrode arrays (2) signal acquisition and multiplexing units, (3) a surgical technique to place electrical recording and myocardial ischemic control equipment, and (4) an image based modeling pipeline to acquire, process, and visualize the results. With this setup, we are uniquely able to capture simultaneously and continuously the electrical signatures of acute myocardial ischemia within the heart, on the heart surface, and on the body surface. SIGNIFICANCE This novel experimental preparation enables investigation of the complex and dynamic nature of acute myocardial ischemia that should lead to new, clinically translatable results.

A CT perfusion based model predicts outcome in wake-up stroke patients treated with recombinant tissue plasminogen activator.

Abstract: OBJECTIVE Advanced neuroimaging has proved to be pivotal in the management of acute ischemic stroke. The use of CT perfusion (CTP) core and penumbra parameters to predict the outcome in wake-up stroke (WUS) patients in everyday clinical scenarios has not yet been investigated. The aim of our study was to investigate the predictive power of CTP parameters on functional and morphological outcomes in WUS patients treated with recombinant tissue plasminogen activator (rTPA). APPROACH We analyzed clinical data and processed CTP images of 83 consecutive WUS patients treated with rTPA. The predictive power of whole-brain CTP features and of the clinical stroke-related parameters to predict the National Institutes of Health Stroke Scale (NIHSS) score at the seventh day and ischemic lesion volume outcome was investigated by means of multivariate regression analysis as well as least absolute shrinkage and selection operator (LASSO) modeling. MAIN RESULTS Multivariate analysis showed that CTP core volume (β = 0.403, p = 0.000), NIHSS at admission (β = 0.323, p = 0.005) and Alberta Stroke Program Early CT (ASPECT) score (β = -0.224, p = 0.012) predict NIHSS at 7 days, while total hypoperfused volume (β = 0.542, p = 0.000) and core volume on CTP (β =0.441, p = 0.000) predict infarct lesion volume at follow-up CT. The LASSO modeling approach confirmed the significant predictive power of CTP core volume, total hypoperfused CTP volume, NIHSS at baseline and ASPECT score, producing a sparse model with adequate reliability (the root mean square error on a previously unseen testing dataset was 3.68). SIGNIFICANCE Our findings highlight the importance of CT multimodal imaging features for decision-making and prediction in the hyperacute phase of WUS. The predictive model supports the hypothesis that an irreversible necrotic core rather than the extent of the penumbra is the main prognostic factor in WUS patients treated with rTPA.

The use of multi-site photoplethysmography (PPG) as a screening tool for coronary arterial disease and atherosclerosis.

Abstract: Objective. We present the design and validation of a non-invasive smart-phone based screening tool for atherosclerosis and coronary arterial disease (CAD), which is the leading cause of mortality worldwide.Approach. We designed a three-channel photoplethysmography (PPG) device that connects to a smart phone application for measuring pulse transit time (PTT) and pulse wave velocity (PWV) using PPG probes that are simultaneously clipped onto to the ear, index finger, and big toe, respectively. Validation was performed through a clinical study with 100 participants (age 20 to 77) at a research hospital in Nagpur, India. Study subjects were stratified by age and divided into three groups corresponding to the disease severity: CAD, hypertensive ('Pre-CAD'), and Healthy.Main results. PWV measurements derived from the Ear-Toe probe measurements yielded the best performance, with median PWV values increasing monotonically as a function of disease severity and age, as follows: 14.2 m s-1for the older-patient CAD group, 12.2 m s-1for the younger-patient CAD group, 11.6 m s-1for the older-patient Pre-CAD group, 10.2 m s-1for the younger-patient Pre-CAD group, 9.7 m s-1for the older healthy controls, and 8.4 m s-1for the younger healthy controls. Using just two simple features, the PTT and patient height, we demonstrate a machine learning prediction model for CAD with a median accuracy of 0.83 (AUC).Significance. This work demonstrates the ability to predict atherosclerosis and CAD using a single simple physiological measurement with a multi-site PPG tool that is electrically powered by a mobile phone and does not require any electrocardiogram reference. Furthermore, this method only requires a single anthropometric measurement, which is the patient's height.

Validity of smartphones and activity trackers to measure steps in a free-living setting over three consecutive days.

Abstract: Objective This study assessed the validity of a consumer activity wristband, a smartphone, and a research-grade accelerometer to measure steps in a free-living setting. Approach Thirty healthy adults were equipped with two Garmin Vivofit (non-dominant wrist), one iPhone SE (right pants pocket), three ActiGraph wGT3X + (two on the hip, one on the non-dominant wrist), and one StepWatch (right ankle) and instructed to wear the devices continuously during a 3 d monitoring period. All activities of daily living were recorded in 15 min intervals in a diary. The StepWatch served as the criterion method and validity was evaluated by comparing each device with the criterion measure using mean absolute percentage errors (MAPE). Main results The MAPE for the total step count during the 3 d monitoring period was high with a general underestimation of steps by all devices of >20% compared to the criterion measure. The wrist-worn ActiGraph markedly overestimated steps during predominantly low active (public transport or driving, and standing) or even inactive (sitting and lying) activities of daily living. Significance The overall high MAPE of the devices compared to the StepWatch during step-based activities, likely caused by inaccuracies during short and intermittent bouts of activity, may limit their validity in a free-living setting.

Non-contact sensing of neonatal pulse rate using camera-based imaging: a clinical feasibility study.

Abstract: Objective Neonates and infants are patients who would benefit from less invasive vital sign sensing, especially from fewer cables and the avoidance of adhesive electrodes. Photoplethysmography imaging (PPGI) has been studied for medical applications in recent years: it is possible to assess various vital signs remotely, non-invasively, and without contact by using video cameras and light. However, studies on infants and especially on neonates in clinical settings are still rare. Hence, we conducted a single-center study to assess heart activity by estimating the pulse rate (PR) of 19 neonates. Approach Time series were generated from tracked regions of interest (ROIs) and PR was estimated via a joint time-frequency analysis using a short-time Fourier transform. Artifacts, for example, induced by movement, were detected and flagged by applying a signal quality index in the frequency domain. Main results The feasibility of PR estimation was demonstrated using visible light and near-infrared light at 850 nm and 940 nm, respectively: the estimated PR was as close as 3 heartbeats per minute in artifact-free time segments. Furthermore, an improvement could be shown when selecting the best performing ROI compared to the ROI containing the whole body. The main challenges are artifacts from motion, light sources, medical devices, and the detection and tracking of suitable regions for signal retrieval. Nonetheless, the PR extracted was found to be comparable to the contact-based photoplethysmography reference and is, therefore, a viable replacement if robust signal retrieval is ensured. Significance Neonates are seldom measured by PPGI and studies reporting measurements on darker skin tones are rare. In this work, not only a single camera was used, but a synchronized camera setup using multiple wavelengths. Various ROIs were used for signal extraction to examine the capabilities of PPGI. In addition, qualitative observations regarding camera parameters and noise sources were reported and discussed.

Age-related changes in pulse risetime measured by multi-site photoplethysmography

Abstract: OBJECTIVE It is accepted that changes in the peripheral pulse waveform characteristics occur with ageing. Pulse risetime is one important feature which has clinical value. However, it is unclear how it varies across the full age spectrum from child to senior and for different peripheral measurement sites. The objectives of this study were to determine the association between age and pulse risetime characteristics over an 8-decade age range at the ears, fingers, and toes, and to consider effects arising from differences in systolic blood pressure (SBP), height and heart rate. APPROACH Multi-site photoplethysmography (MPPG) pulse waveforms were recorded non-invasively from the right and left ears, fingers, and toes of 304 normal healthy human subjects (range 6-87 years; 156 male and 148 female). SBP, height, and heart rate were also measured. Multi-site PPG pulse risetimes, and their site differences, were determined. MAIN RESULTS Univariate regression analysis showed positive correlations with risetime for age (ears, fingers and toes: + 0.8, + 1.9, and + 1.1 ms/year, respectively), SBP (+0.5, + 1.3, and + 0.9 ms/mmHg) and height (+0.5, + 1.2, and + 1.0 ms/cm), but with a clear inverse association with heart rate (-1.8, - 2.5, and - 1.6 ms min) (P < 0.0001). No significant differences between male and female subjects were found for pulse risetime. SIGNIFICANCE Normative multi-site PPG risetime characteristics have been defined in over 300 subjects and are shown to increase with age linearly up to the 8th decade. In contrast, we have shown that heart rate has a clear inverse relationship with risetime for all measurement sites.

SmartProbe: a bioimpedance sensing system for head and neck cancer tissue detection.

Abstract: Objectives This study presents SmartProbe, an electrical bioimpedance (EBI) sensing system based on a concentric needle electrode (CNE). The system allows the use of commercial CNEs for accurate EBI measurement, and was specially developed for in-vivo real-time cancer detection. Approach Considering the uncertainties in EBI measurements due to the CNE manufacturing tolerances, we propose a calibration method based on statistical learning. This is done by extracting the correlation between the measured impedance value |Z|, and the material conductivity σ, for a group of reference materials. By utilizing this correlation, the relationship of σ and |Z| can be described as a function and reconstructed using a single measurement on a reference material of known conductivity. Main results This method simplifies the calibration process, and is verified experimentally. Its effectiveness is demonstrate by results that show less than 6% relative error. An additional experiment is conducted for evaluating the system's capability to detect cancerous tissue. Four types of ex-vivo human tissue from the head and neck region, including mucosa, muscle, cartilage and salivary gland, are characterized using SmartProbe. The measurements include both cancer and surrounding healthy tissue excised from 10 different patients operated on for head and neck cancer. The measured data is then processed using dimension reduction and analyzed for tissue classification. The final results show significant differences between pathologic and healthy tissues in muscle, mucosa and cartilage specimens. Significance These results are highly promising and indicate a great potential for SmartProbe to be used in various cancer detection tasks.

Robust blood pressure estimation from finger photoplethysmography using age-dependent linear models.

Abstract: Objective This work aims to develop an efficient and robust age-dependent multiple linear regression (MLR) model to estimate blood pressure (BP) from a single-source photoplethysmography (PPG) and biometrics, which could be embedded in the microcontroller of pulse oximeters. Approach Hemodynamic features were extracted from the PPG signal using its waveform, derivatives, and biometrics. Whole-based, feature-based, and fusion models were evaluated and compared for different age groups. Their performance was tested using 1086 subjects with a leave-one-subject-out cross-validation. The improvement by adding biometrics and the long-term calibration effect were investigated in detail. The relative importance of each feature was compared between different age groups and the implication was discussed. Main results The fusion model achieved the best performance in subjects with well-defined PPG features, whereas the feature-based method was better suited for subjects with damped signals. Adding age significantly improved both systolic BP (SBP) and diastolic BP (DBP) estimation accuracy for older subjects (> 50 years old) with well-defined features, while it only improved diastolic BP accuracy for older subjects with damped signals. For younger subjects (≤ 50 years old), the contribution of age was very small. A simple subtraction of subject-specific calibration factors significantly reduced biometric-related errors, which also improved the linearity of BP estimation. The relative importance analysis of input features suggests that separate models are indeed necessary for different age groups with different signal qualities, especially for DBP estimation in older subjects. Significance This study shows a reasonable BP estimation accuracy with age-dependent MLR models, which may help to equip current pulse oximeters with additional functionalities.

Novel instrumental markers of proximal scleroderma provided by imaging photoplethysmography.

Abstract: Objective: Instrumental identification of proximal scleroderma, which is necessary for the early diagnosis of systemic sclerosis (SSD), has not yet been developed. The aim of this study was to assess the potential diagnostic value of the imaging photoplethysmography (IPPG) method in patients with SSD. Approach: The study enrolled 19 patients with SSD and 21 healthy subjects matched by age and sex with the patients. Spatial distribution of capillary-blood-flow parameters and their dynamics was estimated in the facial area of patients and subjects. In the IPPG system, a 40 s video of the subject's face illuminated by green polarized light was recorded with a monochrome digital camera in synchronization with the electrocardiogram. Experimental data were processed using custom software allowing assessment of an arrival time of the blood pressure wave (PAT), an amplitude of pulsatile component (APC) of the photoplethysmographic (PPG) waveform, and their variability. Main results: Our study has revealed a significant increase in PAT variability in patients with SSD compared to the control group: 52 ± 47 ms vs 24 ± 13 ms (P =0.01). Similarly, the variability of the PPG-pulse shape was larger in patients with SSD: 0.13 ± 0.07% vs 0.09 ± 0.02% (P < 0.001). In addition, patients with scleroderma showed a significantly greater degree of asymmetry of the APC parameter than the control group: 17.7 ± 9.7 vs 7.9 ± 5.0 (P < 0.001). At the same time, no correlation was found between the PPG waveform parameters and either the form or duration of the disease. Also, no relationship between the characteristics of the PPG waveform and the modified Rodnan skin score was found. Significance: Novel instrumental markers found in our pilot study showed that the IPPG method can be used for diagnosing SSD in the early stages of the disease.

Multimodal remote chest monitoring system with wearable sensors: a validation study in healthy subjects.

Abstract: Objective Development of wearable medical technology for remote monitoring of patients suffering from chronic lung diseases may improve the care, therapy and outcome of these patients. Approach A multimodal system using wearable sensors for the acquisition of multiple biosignals (electrical bioimpedance of the chest for electrical impedance tomography and respiratory rate assessment, peripheral oxygen saturation, chest sounds, electrocardiography for heart rate measurement, body activity, and posture) was developed and validated in a prospective, monocentric study on 50 healthy subjects. The subjects were studied under different types of ventilation (tidal and deep breathing, forced full expiration maneuver) and during increased body activity and posture changes. The major goals were to assess the functionality by determining the presence and plausibility of the signals, comfort of wearing and safety of the vest. Main results All intended signals were recorded. Streaming of selected signals and wireless download of complete data sets were functional. Electrical impedance tomography recordings revealed good to excellent quality of detection of ventilation-related impedance changes in 34 out of 50 participants. Respiratory and heart rates were reliably detected and generally in physiological ranges. Peripheral oxygen saturation values were unphysiologically low. The chest sound recordings did not show waveforms allowing meaningful analysis of lung sounds. Body activity and posture were correctly identified. The comfort of wearing and the vest properties were positively rated. No adverse events occurred. Significance Albeit the full functionality of the current vest design was not established, the study confirmed the feasibility of remote functional chest monitoring with a marked increase in clinically relevant information compared to existing systems.

Single-channel oximetry monitor versus in-lab polysomnography oximetry analysis: does it make a difference?

Abstract: Objective Portable oximetry has been shown to be a promising candidate for large-scale obstructive sleep apnea screening. In polysomnography (PSG), the gold standard OSA diagnosis test, the oxygen desaturation index (ODI) is usually computed from desaturation events occurring during sleep periods only, i.e. overnight desaturations occurring during or overlapping with a wake state are excluded. However, for unattended home oximetry, all desaturations are taken into account since no reference electroencephalogram is available for sleep staging. We aim to evaluate the hypothesis that the predictive power of oximetry for OSA screening is not impaired when reference sleep stages are not available. Approach We used a PSG clinical database of 887 individuals from a representative Sao Paulo (Brazil) population sample. Using features derived from the oxygen saturation time series and demographic information, OxyDOSA, a published machine learning model, was trained to distinguish between non-OSA and OSA individuals using the ODI computed while including versus excluding overnight desaturations overlapping with a wake period, thus mimicking portable and PSG oximetry analyses, respectively. Main results When excluding wake desaturations, the OxyDOSA model had an AUROC = 94.9 ± 1.6, Se = 85.9 ± 2.8, Sp = 90.1 ± 2.6 and F1 = 86.4 ± 2.7. When considering wake desaturations, the OxyDOSA model had an AUROC = 94.4 ± 1.6, Se = 88.0 ± 2.0, Sp = 87.7 ± 2.9 and F1 = 86.2 ± 2.4. Non-inferiority was demonstrated (p = 0.049) at a tolerance level of 3%. In addition, analysis of the desaturations excluded by PSG oximetry analysis suggests that up to 21% of the total number of desaturations might actually be related to apneas or hypopneas. Significance This analysis of a large representative population sample provided strong evidence that the predictive power of oximetry for OSA screening using the OxyDOSA model is not impaired when reference sleep stages are not available. This finding motivates the usage of portable oximetry for OSA screening.

Reliability, reproducibility and validity of dynamic cerebral autoregulation in a large cohort with transient ischaemic attack or minor stroke.

Abstract: Objective Cerebral autoregulation (CA) is critical to maintenance of cerebral perfusion but its relevance to the risk of stroke and dementia has been under-studied due to small study sizes and a lack of consensus as to the optimal method of measurement. We determined the reliability and reproducibility of multiple CA indices and the effect of intensive data-processing in a large population with transient ischaemic attack or minor stroke. Approach Consecutive, consenting patients in the population-based Oxford Vascular Study (OXVASC) Phenotyped cohort underwent up to 10-min supine continuous blood pressure monitoring (Finometer) with bilateral middle cerebral artery (MCA) transcranial ultrasound (DWL-Dopplerbox). Un-processed waveforms (Un-A) were median-filtered, systematically reviewed, artefacts corrected and their quality blindly graded (optimal (A) to worst (E)). CA metrics were derived in time-domain (autoregulatory index (ARI), Pearson's Mx, Sx, Dx) and in very-low (VLF) and low-frequency (LF) domains (WPS-SI: wavelet phase synchronisation, transfer function analysis), stratified by recording quality. Reliability and reproducibility (Cronbach's alpha) were determined comparing MCA sides and the first vs. second 5-min of monitoring. Main results In 453 patients, following manual data-cleaning, there was good reliability of indices when comparing MCA sides (Mx: 0.77; WPS-SI-VLF: 0.85; WPS-SI-LF 0.84), or repeated five minute epochs (Mx: 0.57; WPS-SI-VLF: 0.69; WPS-SI-LF 0.90), with persistently good reliability between sides even in lower quality Groups (Group D: Mx: 0.79; WPS-SI-VLF: 0.92; WPS-SI-LF: 0.91). Reliability was greatest for Pearson's Mx and wavelet synchronisation index, with reasonable reliability of transfer function analyses, but ARI was prone to occasional, potentially defective, extreme estimates (left vs right MCA: 0.68). Significance Resting-state measures of CA were valid, reproducible and robust to moderate noise, but require careful data-processing. Mx and wavelet synchronisation index were the most reliable indices for determining the prognostic value of CA in large epidemiological cohorts and its potential as a treatment target.

Automatic sleep-stage scoring based on photoplethysmographic signals.

Abstract: Objective: Sleep-stage scoring is important for sleep-quality evaluation and the diagnosis of related diseases. In this study, an automatic sleep-stage scoring method using photoplethysmographic (PPG) signals was proposed. Approach: To construct the classification model, we extracted 14 time-domain features, 17 frequency-domain features, and 20 pulse rate variability (PRV) features along with four SpO2 features from PPG signals. An artificial neural network (ANN) classifier was used to integrate the results of 10 binary support vector machine (SVM) classifiers and realise sleep-stage classification. Leave-one-subject-out validation was applied to evaluate our proposed model. Main results: Thirty-one subjects were enrolled in the study, in which 21 subjects were with high sleep quality (sleep efficiencies ≥ 85%). Our model achieved accuracies of 57% (κ = 0.39), 62% (κ = 0.41) and 78% (κ =0.54) for the classification of five sleep stages (wake, N1, N2, N3, and REM sleeps), four sleep stages (wake, light, deep, and REM sleeps) and three sleep stages (wake, NREM, and REM sleeps), respectively. For the rest ten subjects with poor sleep quality, the results came to 55% (κ = 0.39), 62% (κ = 0.43) and 75% (κ = 0.52). Significance: The satisfactory performance of our proposed model reveals the potential of PPG signals for sleep-stage scoring, which may contribute to the automatic sleep monitoring in home environment.