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

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

Novel instrumental markers of proximal scleroderma provided by imaging photoplethysmography.

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.

A Supervised Approach to Robust Photoplethysmography Quality Assessment.

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including k-nearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data.

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

BACKGROUND: Atrial fibrillation is the most common recurrent arrhythmia in clinical practice, with most clinical events occurring outside the hospital. Low detection and nonadherence to guidelines are the primary obstacles to atrial fibrillation management. Photoplethysmography is a novel technology developed for atrial fibrillation screening. However, there has been limited validation of photoplethysmography-based smart devices for the detection of atrial fibrillation and its underlying clinical factors impacting detection. OBJECTIVE: This study aimed to explore the feasibility of photoplethysmography-based smart devices for the detection of atrial fibrillation in real-world settings. METHODS: Subjects aged ≥18 years (n=361) were recruited from September 14 to October 16, 2018, for screening of atrial fibrillation with active measurement, initiated by the users, using photoplethysmography-based smart wearable devices (ie, a smart band or smart watches). Of these, 200 subjects were also automatically and periodically monitored for 14 days with a smart band. The baseline diagnosis of "suspected" atrial fibrillation was confirmed by electrocardiogram and physical examination. The sensitivity and accuracy of photoplethysmography-based smart devices for monitoring atrial fibrillation were evaluated. RESULTS: A total of 2353 active measurement signals and 23,864 periodic measurement signals were recorded. Eleven subjects were confirmed to have persistent atrial fibrillation, and 20 were confirmed to have paroxysmal atrial fibrillation. Smart devices demonstrated >91% predictive ability for atrial fibrillation. The sensitivity and specificity of devices in detecting atrial fibrillation among active recording of the 361 subjects were 100% and about 99%, respectively. For subjects with persistent atrial fibrillation, 127 (97.0%) active measurements and 2240 (99.2%) periodic measurements were identified as atrial fibrillation by the algorithm. For subjects with paroxysmal atrial fibrillation, 36 (17%) active measurements and 717 (19.8%) periodic measurements were identified as atrial fibrillation by the algorithm. All persistent atrial fibrillation cases could be detected as "atrial fibrillation episodes" by the photoplethysmography algorithm on the first monitoring day, while 14 (70%) patients with paroxysmal atrial fibrillation demonstrated "atrial fibrillation episodes" within the first 6 days. The average time to detect paroxysmal atrial fibrillation was 2 days (interquartile range: 1.25-5.75) by active measurement and 1 day (interquartile range: 1.00-2.00) by periodic measurement (P=.10). The first detection time of atrial fibrillation burden of <50% per 24 hours was 4 days by active measurement and 2 days by periodic measurementThe first detection time of atrial fibrillation burden of >50% per 24 hours was 1 day for both active and periodic measurements (active measurement: P=.02, periodic measurement: P=.03). CONCLUSIONS: Photoplethysmography-based smart devices demonstrated good atrial fibrillation predictive ability in both active and periodic measurements. However, atrial fibrillation type could impact detection, resulting in increased monitoring time. TRIAL REGISTRATION: Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191.

Possible Error in Reflection Pulse Oximeter Readings as a Result of Applied Pressure.

Pulse oximetry is one of the most widely used techniques in modern medicine. In pulse oximetry, photoplethysmography (PPG) signals are measured at two different wavelengths and converted into the parameter Gamma, which is used to calculate the oxygen saturation of arterial blood. Although most pulse oximetry sensors are based on transmission geometry, the reflection mode is required for different form factors such as the forehead or wrists. In reflection oximetry, local pressure is applied to the measurement surface. We investigated the relationship between applied pressure and Gamma and found that for the reflection mode, Gamma tends to increase with increasing applied pressure. To explain this, we described the PPG signal in terms of two alternative models: a volumetric model and a Scattering-Driven Model (SDM). We assumed that the application of external pressure results in a decrease in local blood flow. We showed that only SDM correctly qualitatively describes Gamma as a function of the decrease in blood flow. We concluded that both described models coexist and that the relative influence of each depends on the measurement geometry and blood perfusion in the skin.

Artifacts in pulse transit time measurements using standard patient monitoring equipment.

OBJECTIVE: Pulse transit time (PTT) refers to the time it takes a pulse wave to travel between two arterial sites. PTT can be estimated, amongst others, using the electrocardiogram (ECG) and photoplethysmogram (PPG). Because we observed a sawtooth artifact in the PTT while using standard patient monitoring equipment for ECG and PPG, we explored the reasons for this artifact. METHODS: PPG and ECG were simulated at a heartrate of both 100 and 160 beats per minute while using a Masimo PPG post-processing module and a Philips patient monitor setup at the neonatal intensive care unit. Two different post-processing modules were used. PTT was defined as the difference between the R-peak in the ECG and the point of 50% increase in the PPG. RESULTS: A sawtooth artifact was seen in all simulations. Both length (59.2 to 72.4 s) and amplitude (30.8 to 36.0 ms) of the sawtooth were dependent on the post-processing module used. Furthermore, the absolute PTT value differed up to 250 ms depending on post-processing module and heart rate. The sawtooth occurred because the PPG wave continuously showed a minimal prolongation during the length of the sawtooth, followed by a sudden shortening. Both artifacts were generated in the post-processing module containing Masimo algorithms. CONCLUSION: Post-processing of the PPG signal in the Masimo module of the Philips patient monitor introduces a sawtooth in PPG and derived PTT. This sawtooth, together with a large module-dependent absolute difference in PTT, renders the thus-derived PTT insufficient for clinical purposes.

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study.

BACKGROUND: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients with paroxysmal AF frequently exhibit premature atrial complexes (PACs), which result in poor unmanned AF detection, mainly because of rule-based or handcrafted machine learning techniques that are limited in terms of diagnostic accuracy and reliability. OBJECTIVE: This study aimed to develop deep learning (DL) classifiers using PPG data to detect AF from the sinus rhythm (SR) in the presence of PACs after successful cardioversion. METHODS: We examined 75 patients with AF who underwent successful elective direct-current cardioversion (DCC). Electrocardiogram and pulse oximetry data over a 15-min period were obtained before and after DCC and labeled as AF or SR. A 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) were chosen as the 2 DL architectures. The PAC indicator estimated the burden of PACs on the PPG dataset. We defined a metric called the confidence level (CL) of AF or SR diagnosis and compared the CLs of true and false diagnoses. We also compared the diagnostic performance of 1D-CNN and RNN with previously developed AF detectors (support vector machine with root-mean-square of successive difference of RR intervals and Shannon entropy, autocorrelation, and ensemble by combining 2 previous methods) using 10 5-fold cross-validation processes. RESULTS: Among the 14,298 training samples containing PPG data, 7157 samples were obtained during the post-DCC period. The PAC indicator estimated 29.79% (2132/7157) of post-DCC samples had PACs. The diagnostic accuracy of AF versus SR was 99.32% (70,925/71,410) versus 95.85% (68,602/71,570) in 1D-CNN and 98.27% (70,176/71,410) versus 96.04% (68,736/71,570) in RNN methods. The area under receiver operating characteristic curves of the 2 DL classifiers was 0.998 (95% CI 0.995-1.000) for 1D-CNN and 0.996 (95% CI 0.993-0.998) for RNN, which were significantly higher than other AF detectors (P<.001). If we assumed that the dataset could emulate a sufficient number of patients in training, both DL classifiers improved their diagnostic performances even further especially for the samples with a high burden of PACs. The average CLs for true versus false classification were 98.56% versus 78.75% for 1D-CNN and 98.37% versus 82.57% for RNN (P<.001 for all cases). CONCLUSIONS: New DL classifiers could detect AF using PPG monitoring signals with high diagnostic accuracy even with frequent PACs and could outperform previously developed AF detectors. Although diagnostic performance decreased as the burden of PACs increased, performance improved when samples from more patients were trained. Moreover, the reliability of the diagnosis could be indicated by the CL. Wearable devices sensing PPG signals with DL classifiers should be validated as tools to screen for AF.

Validation of Polar OH1 optical heart rate sensor for moderate and high intensity physical activities.

BACKGROUND: Optical measurement techniques and recent advances in wearable technology have made heart rate (HR) sensing simpler and more affordable. OBJECTIVES: The Polar OH1 is an arm worn optical heart rate monitor. The objectives of this study are two-fold; 1) to validate the OH1 optical HR sensor with the gold standard of HR measurement, electrocardiography (ECG), over a range of moderate to high intensity physical activities, 2) to validate wearing the OH1 at the temple as an alternative location to its recommended wearing location around the forearm and upper arm. METHODS: Twenty-four individuals participated in a physical exercise protocol, by walking on a treadmill and riding a stationary spin bike at different speeds while the criterion measure, ECG and Polar OH1 HR were recorded simultaneously at three different body locations; forearm, upper arm and the temple. Time synchronised HR data points were compared using Bland-Altman analyses and intraclass correlation. RESULTS: The intraclass correlation between the ECG and Polar OH1, for the aggregated data, was 0.99 and the estimated mean bias ranged 0.27-0.33 bpm for the sensor locations. The three sensors exhibited a 95% limit of agreement (LoA: forearm 5.22, -4.68 bpm; upper arm 5.15, -4.49; temple 5.22, -4.66). The mean of the ECG HR for the aggregated data was 112.15 ± 24.52 bpm. The intraclass correlation of HR values below and above this mean were 0.98 and 0.99 respectively. The reported mean bias ranged 0.38-0.47 bpm (95% LoA: forearm 6.14, -5.38 bpm; upper arm 6.07, -5.13 bpm; temple 6.09, -5.31 bpm), and 0.15-0.16 bpm (95% LoA: forearm 3.99, -3.69 bpm; upper arm 3.90, -3.58 bpm; temple 4.06, -3.76 bpm) respectively. During different exercise intensities, the intraclass correlation ranged 0.95-0.99 for the three sensor locations. During the entire protocol, the estimated mean bias was in the range -0.15-0.55 bpm, 0.01-0.53 bpm and -0.37-0.48 bpm, for the forearm, upper arm and temple locations respectively. The corresponding upper limits of 95% LoA were 3.22-7.03 bpm, 3.25-6.82 bpm and 3.18-7.04 bpm while the lower limits of 95% LoA were -6.36-(-2.35) bpm, -6.46-(-2.30) bpm and -7.42-(-2.41) bpm. CONCLUSION: Polar OH1 demonstrates high level of agreement with the criterion measure ECG HR, thus can be used as a valid measure of HR in lab and field settings during moderate and high intensity physical activities.

Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry).

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG). OBJECTIVE: We investigated the feasibility and accuracy of a mobile phone and smart band for AF detection using pulse data measured by PPG. METHODS: A total of 112 consecutive inpatients were recruited from the Chinese PLA General Hospital from March 15 to April 1, 2018. Participants were simultaneously tested with mobile phones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes. RESULTS: In all, 108 patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding four patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% CI 92.00%-97.40%) and 99.70% (95% CI 98.08%-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61%-99.98%), the negative predictive value was 96.24% (95% CI 93.50%-97.90%), and the accuracy was 97.72% (95% CI 96.11%-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of mobile phones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P>.99). CONCLUSIONS: The algorithm based on mobile phones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR-OOC-17014138; http://www.chictr.org.cn/showproj.aspx?proj=24191 (Archived by WebCite at http://www.webcitation/76WXknvE6).

Mobile Phone-Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App.

BACKGROUND: Mobile phone apps using photoplethysmography (PPG) technology through their built-in camera are becoming an attractive alternative for atrial fibrillation (AF) screening because of their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy remain to be answered. OBJECTIVE: This study tested the diagnostic accuracy of the FibriCheck AF algorithm for the detection of AF on the basis of mobile phone PPG and single-lead electrocardiography (ECG) signals. METHODS: A convenience sample of patients aged 65 years and above, with or without a known history of AF, was recruited from 17 primary care facilities. Patients with an active pacemaker rhythm were excluded. A PPG signal was obtained with the rear camera of an iPhone 5S. Simultaneously, a single­lead ECG was registered using a dermal patch with a wireless connection to the same mobile phone. PPG and single-lead ECG signals were analyzed using the FibriCheck AF algorithm. At the same time, a 12­lead ECG was obtained and interpreted offline by independent cardiologists to determine the presence of AF. RESULTS: A total of 45.7% (102/223) subjects were having AF. PPG signal quality was sufficient for analysis in 93% and single­lead ECG quality was sufficient in 94% of the participants. After removing insufficient quality measurements, the sensitivity and specificity were 96% (95% CI 89%-99%) and 97% (95% CI 91%-99%) for the PPG signal versus 95% (95% CI 88%-98%) and 97% (95% CI 91%-99%) for the single­lead ECG, respectively. False-positive results were mainly because of premature ectopic beats. PPG and single­lead ECG techniques yielded adequate signal quality in 196 subjects and a similar diagnosis in 98.0% (192/196) subjects. CONCLUSIONS: The FibriCheck AF algorithm can accurately detect AF on the basis of mobile phone PPG and single-lead ECG signals in a primary care convenience sample.

Assessment of Subtle Changes in Diabetes-Associated Arteriosclerosis using Photoplethysmographic Pulse Wave from Index Finger.

Distortions in macro- and microcirculation are principal contributors to diabetes-associated complications. This study aimed at investigating the validity of applying non-invasive photoplethysmographic (PPG) waveform parameters in detecting diabetes-induced subtitle changes in arterial stiffness. Between July 2009 and October 2010, totally 94 middle-aged and elderly subjects were recruited including 48 without diabetes (Group 1) and 46 with the disease (Group 2). Demographic (i.e., age, gender), anthropometric (body-mass index), biochemical (i.e., glycated hemoglobin concentration), and hemodynamic (i.e., systolic blood pressure, heart rate) parameters were obtained. Crest time (CT) and crest time ratio (CTR) computed from PPG signals acquired from left index finger were compared with left index finger pulse wave velocity (PWVfinger) obtained from six-channel ECG-PWV system to investigate the differences between the two groups and the associations of these indices with the parameters of testing subjects. Significant difference was only noted in CTR between the two groups (P < 0.005). Despite correlation of both CT and CTR with age, only CTR demonstrated significant associations with hemodynamic parameters. CTR could differentiate diabetic patients from healthy individuals despite absence of difference in arterial stiffness assessed by conventional PWV, highlighting its superior sensitivity to subtle changes in diabetes-associated arteriosclerosis.

A Universal Noninvasive Continuous Blood Pressure Measurement System for Remote Healthcare Monitoring.

BACKGROUND: The effectiveness of any remote healthcare monitoring system depends on how much accurate, patient-friendly, versatile, and cost-effective measurement it is delivering. There has always been a huge demand for such a long-term noninvasive remote blood pressure (BP) measurement system, which could be used worldwide in the remote healthcare industry. Thus, noninvasive continuous BP measurement and remote monitoring have become an emerging area in the remote healthcare industry. INTRODUCTION: Photoplethysmography-based (PPG) BP measurement is a continuous, unobtrusive, patient-friendly, and cost-effective solution. However, BP measurements through PPG sensors are not much reliable and accurate due to some major limitations like pressure disturbance, motion artifacts, and variations in human skin tone. MATERIALS AND METHODS: A novel reflective PPG sensor has been developed to eliminate the abovementioned pressure disturbance and motion artifacts during the BP measurement. Considering the variations of the human skin tone across demography, a novel algorithm has been developed to make the BP measurement accurate and reliable. The training dataset captured 186 subjects' data and the trial dataset captured another new 102 subjects' data. RESULTS AND DISCUSSION: The overall accuracy achieved by using the proposed method is nearly 98%. Thus, demonstrating the efficacy of the proposed method. CONCLUSIONS: The developed BP monitoring system is quite accurate, reliable, cost-effective, handy, and user friendly. It is also expected that this system would be quite useful to monitor the BP of infants, elderly people, patients having wounds, burn injury, or in the intensive care unit environment.

A Literature Review on Current and Proposed Technologies of Noninvasive Blood Pressure Measurement.

BACKGROUND: Noninvasive continuous blood pressure (BP) measurement has become an evolving topic in the field of remote healthcare. The classical noninvasive BP measurement techniques provide spontaneous values of systolic and diastolic BP. On the other hand, intrusive type BP measurement techniques provide continuous values of systolic and diastolic BP. However, these techniques are very painful, cannot be used for long-term monitoring, and are obtainable only in an intensive care unit environment. With the advancement of the remote healthcare industry, there is a growing demand for noninvasive continuous BP monitoring. OBJECTIVE: The objective of this research was to present a compact literature review on the various prospective approaches of noninvasive continuous BP measurement techniques. MATERIALS & METHODS: The most contemporary and advanced technologies on noninvasive continuous BP measurement are Tactile Sensing, Vascular Unloading Technique, Pulse Transit Time, Photoplethysmography, Ultrasound-based BP measurement, BP measurement from image processing, etc. The literature search based on these technologies was conducted in EMBASE, Web of Science, IEEE, PubMed, and Ovid MEDLINE databases. In this study, each selected approach was evaluated and characterized using the following criteria: (1) accuracy; (2) cost; (3) portability; (4) comfort and convenience of use; (5) clinical health and safety; and (6) ability to integrate with the remote healthcare system. RESULTS: A detailed technical analysis was done to determine the advantages and limitations of each technique in the context of the abovementioned parameters. It was observed that BP measurement, using photoplethysmography (using camera or sensor or both), perhaps was the most promising technique among all. CONCLUSION: The study emphasized the fact that the noninvasive, continuous BP measurement technique needs to evolve further to make it reliable, accurate, and user-friendly. Lastly, a possible direction toward a more reliable and comfortable noninvasive continuous BP measurement technique has been discussed.

Reliability of the Parabola Approximation Method in Heart Rate Variability Analysis Using Low-Sampling-Rate Photoplethysmography.

Photoplethysmographic signals are useful for heart rate variability analysis in practical ambulatory applications. While reducing the sampling rate of signals is an important consideration for modern wearable devices that enable 24/7 continuous monitoring, there have not been many studies that have investigated how to compensate the low timing resolution of low-sampling-rate signals for accurate heart rate variability analysis. In this study, we utilized the parabola approximation method and measured it against the conventional cubic spline interpolation method for the time, frequency, and nonlinear domain variables of heart rate variability. For each parameter, the intra-class correlation, standard error of measurement, Bland-Altman 95% limits of agreement and root mean squared relative error were presented. Also, elapsed time taken to compute each interpolation algorithm was investigated. The results indicated that parabola approximation is a simple, fast, and accurate algorithm-based method for compensating the low timing resolution of pulse beat intervals. In addition, the method showed comparable performance with the conventional cubic spline interpolation method. Even though the absolute value of the heart rate variability variables calculated using a signal sampled at 20 Hz were not exactly matched with those calculated using a reference signal sampled at 250 Hz, the parabola approximation method remains a good interpolation method for assessing trends in HRV measurements for low-power wearable applications.

Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults.

Study Objectives: To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements measured with an accelerometer, with polysomnography (PSG) and actigraphy. Methods: Using wrist-worn PPG to analyze heart rate variability and an accelerometer to measure body movements, sleep stages and sleep statistics were automatically computed from overnight recordings. Sleep-wake, 4-class (wake/N1 + N2/N3/REM) and 3-class (wake/NREM/REM) classifiers were trained on 135 simultaneously recorded PSG and PPG recordings of 101 healthy participants and validated on 80 recordings of 51 healthy middle-aged adults. Epoch-by-epoch agreement and sleep statistics were compared with actigraphy for a subset of the validation set. Results: The sleep-wake classifier obtained an epoch-by-epoch Cohen's κ between PPG and PSG sleep stages of 0.55 ± 0.14, sensitivity to wake of 58.2 ± 17.3%, and accuracy of 91.5 ± 5.1%. κ and sensitivity were significantly higher than with actigraphy (0.40 ± 0.15 and 45.5 ± 19.3%, respectively). The 3-class classifier achieved a κ of 0.46 ± 0.15 and accuracy of 72.9 ± 8.3%, and the 4-class classifier, a κ of 0.42 ± 0.12 and accuracy of 59.3 ± 8.5%. Conclusions: The moderate epoch-by-epoch agreement and, in particular, the good agreement in terms of sleep statistics suggest that this technique is promising for long-term sleep monitoring, although more evidence is needed to understand whether it can complement PSG in clinical practice. It also offers an improvement in sleep/wake detection over actigraphy for healthy individuals, although this must be confirmed on a larger, clinical population.

Multicenter Study Validating Accuracy of a Continuous Respiratory Rate Measurement Derived From Pulse Oximetry: A Comparison With Capnography.

BACKGROUND: Intermittent measurement of respiratory rate via observation is routine in many patient care settings. This approach has several inherent limitations that diminish the clinical utility of these measurements because it is intermittent, susceptible to human error, and requires clinical resources. As an alternative, a software application that derives continuous respiratory rate measurement from a standard pulse oximeter has been developed. We sought to determine the performance characteristics of this new technology by comparison with clinician-reviewed capnography waveforms in both healthy subjects and hospitalized patients in a low-acuity care setting. METHODS: Two independent observational studies were conducted to validate the performance of the Medtronic Nellcor Respiration Rate Software application. One study enrolled 26 healthy volunteer subjects in a clinical laboratory, and a second multicenter study enrolled 53 hospitalized patients. During a 30-minute study period taking place while participants were breathing spontaneously, pulse oximeter and nasal/oral capnography waveforms were collected. Pulse oximeter waveforms were processed to determine respiratory rate via the Medtronic Nellcor Respiration Rate Software. Capnography waveforms reviewed by a clinician were used to determine the reference respiratory rate. RESULTS: A total of 23,243 paired observations between the pulse oximeter-derived respiratory rate and the capnography reference method were collected and examined. The mean reference-based respiratory rate was 15.3 ± 4.3 breaths per minute with a range of 4 to 34 breaths per minute. The Pearson correlation coefficient between the Medtronic Nellcor Respiration Rate Software values and the capnography reference respiratory rate is reported as a linear correlation, R, as 0.92 ± 0.02 (P < .001), whereas Lin's concordance correlation coefficient indicates an overall agreement of 0.85 ± 0.04 (95% confidence interval [CI] +0.76; +0.93) (healthy volunteers: 0.94 ± 0.02 [95% CI +0.91; +0.97]; hospitalized patients: 0.80 ± 0.06 [95% CI +0.68; +0.92]). The mean bias of the Medtronic Nellcor Respiration Rate Software was 0.18 breaths per minute with a precision (SD) of 1.65 breaths per minute (healthy volunteers: 0.37 ± 0.78 [95% limits of agreement: -1.16; +1.90] breaths per minute; hospitalized patients: 0.07 ± 1.99 [95% limits of agreement: -3.84; +3.97] breaths per minute). The root mean square deviation was 1.35 breaths per minute (healthy volunteers: 0.81; hospitalized patients: 1.60). CONCLUSIONS: These data demonstrate the performance of the Medtronic Nellcor Respiration Rate Software in healthy subjects and patients hospitalized in a low-acuity care setting when compared with clinician-reviewed capnography. The observed performance of this technology suggests that it may be a useful adjunct to continuous pulse oximetry monitoring by providing continuous respiratory rate measurements. The potential patient safety benefit of using combined continuous pulse oximetry and respiratory rate monitoring warrants assessment.

Calibration of Contactless Pulse Oximetry.

BACKGROUND: Contactless, camera-based photoplethysmography (PPG) interrogates shallower skin layers than conventional contact probes, either transmissive or reflective. This raises questions on the calibratability of camera-based pulse oximetry. METHODS: We made video recordings of the foreheads of 41 healthy adults at 660 and 840 nm, and remote PPG signals were extracted. Subjects were in normoxic, hypoxic, and low temperature conditions. Ratio-of-ratios were compared to reference SpO2 from 4 contact probes. RESULTS: A calibration curve based on artifact-free data was determined for a population of 26 individuals. For an SpO2 range of approximately 83% to 100% and discarding short-term errors, a root mean square error of 1.15% was found with an upper 99% one-sided confidence limit of 1.65%. Under normoxic conditions, a decrease in ambient temperature from 23 to 7°C resulted in a calibration error of 0.1% (±1.3%, 99% confidence interval) based on measurements for 3 subjects. PPG signal strengths varied strongly among individuals from about 0.9 × 10 to 4.6 × 10 for the infrared wavelength. CONCLUSIONS: For healthy adults, the results present strong evidence that camera-based contactless pulse oximetry is fundamentally feasible because long-term (eg, 10 minutes) error stemming from variation among individuals expressed as A*rms is significantly lower (<1.65%) than that required by the International Organization for Standardization standard (<4%) with the notion that short-term errors should be added. A first illustration of such errors has been provided with A**rms = 2.54% for 40 individuals, including 6 with dark skin. Low signal strength and subject motion present critical challenges that will have to be addressed to make camera-based pulse oximetry practically feasible.

Non-invasive Monitoring of Dynamic Cerebrovascular Autoregulation Using Near Infrared Spectroscopy and the Finometer Photoplethysmograph.

BACKGROUND: Near infrared spectroscopy (NIRS) enables continuous monitoring of dynamic cerebrovascular autoregulation, but this methodology relies on invasive blood pressure monitoring (iABP). We evaluated the agreement between a NIRS based autoregulation index calculated from invasive blood pressure monitoring, and an entirely non-invasively derived autoregulation index from continuous non-invasive blood pressure monitoring (nABP) using the Finometer photoplethysmograph. METHODS: Autoregulation was calculated as the moving correlation coefficient between iABP and rSO2 (iTOx) or nABP and rSO2 (nTOx). The blood pressure range where autoregulation is optimal was also determined for invasive (iABPOPT) and non-invasive blood pressure measurements (nABPOPT). RESULTS: 102 simultaneous bilateral measurements of iTOx and nTOx were performed in 19 patients (median 2 per patient, range 1-9) with different acute pathologies (sepsis, cardiac arrest, head injury, stroke). Average iTOx was 0.01 ± 0.13 and nTOx was 0.01 ± 0.11. The correlation between iTOx and nTOx was r = 0.87, p < 0.001, 95 % agreement ± 0.12, bias = 0.005. The interhemispheric asymmetry of autoregulation was similarly assessed with iTOx and nTOx (r = 0.81, p < 0.001). Correlation between iABPOPT and nABPOPT was r = 0.47, p = 0.003, 95 % agreement ± 32.1 mmHg, bias = 5.8 mmHg. Coherence in the low frequency spectrum between iABP and nABP was 0.86 ± 0.08 and gain was 1.32 ± 0.77. CONCLUSIONS: The results suggest that dynamic cerebrovascular autoregulation can be continuously assessed entirely non-invasively using nTOx. This allows for autoregulation assessment using spontaneous blood pressure fluctuations in conditions where iABP is not routinely monitored. The nABPOPT might deviate from iABPOPT, likely because of discordance between absolute nABP and iABP readings.

Detection of artifacts on photoplethysmography signals using random distortion testing.

In this work, we describe a novel method based on waveform morphology for detecting artifacts in photoplethysmography (PPG) signals and, thus, improve reliability of PPG. By considering inter-individual and measure condition variability, specific parameters are estimated for each record. We introduce a novel metric for comparing pulses, which is the derivative of the correlation coefficient. Then, we propose a detection method based on Random Distortion Testing (RDT), to perform adaptive threasholding for each record. The results show that the proposed method provides pertinent detection of pulses with artifacts. Tested on 104 PPG records, the mean of sensitivity, specificity and accuracy were 84 ± 16%, 83 ± 12% and 83 ± 8%, respectively.

Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring.

The identification of invalid data in recordings obtained using wearable sensors is of particular importance since data obtained from mobile patients is, in general, noisier than data obtained from nonmobile patients. In this paper, we present a signal quality index (SQI), which is intended to assess whether reliable heart rates (HRs) can be obtained from electrocardiogram (ECG) and photoplethysmogram (PPG) signals collected using wearable sensors. The algorithms were validated on manually labeled data. Sensitivities and specificities of 94% and 97% were achieved for the ECG and 91% and 95% for the PPG. Additionally, we propose two applications of the SQI. First, we demonstrate that, by using the SQI as a trigger for a power-saving strategy, it is possible to reduce the recording time by up to 94% for the ECG and 93% for the PPG with only minimal loss of valid vital-sign data. Second, we demonstrate how an SQI can be used to reduce the error in the estimation of respiratory rate (RR) from the PPG. The performance of the two applications was assessed on data collected from a clinical study on hospital patients who were able to walk unassisted.