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

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

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

BACKGROUND

The Apple Watch™ (AW) offers heart rate (HR) tracking by photoplethysmography (PPG) and single-lead electrocardiographic (ECG) recordings. The accuracy of AW-HR and diagnostic performance of AW-ECGs among children during both sinus rhythm and arrhythmias have not been explored.

OBJECTIVE

The purposes of this study were to assess the accuracy of AW-HR measurements compared to gold standard modalities in children during sinus rhythm and arrhythmias and to identify non-sinus rhythms using AW-ECGs.

METHODS

Subjects ≤18 years wore an AW during (1) telemetry admission, (2) electrophysiological study (EPS), or (3) exercise stress test (EST). AW-HRs were compared to gold standard modality values. Recorded AW-ECGs were reviewed by 3 blinded pediatric electrophysiologists.

RESULTS

Eighty subjects (median age 13 years; interquartile range 1.0-16.0 years; 50% female) wore AW (telemetry 41% [n = 33]; EPS 34% [n = 27]; EST 25% [n = 20]). A total of 1090 AW-HR measurements were compared to time-synchronized gold standard modality HR values. Intraclass correlation coefficient (ICC) was high 0.99 (0.98-0.99) for AW-HR during sinus rhythm compared to gold standard modalities. ICC was poor comparing AW-HR to gold standard modality HR in tachyarrhythmias (ICC 0.24-0.27) due to systematic undercounting of AW-HR values. A total of 126 AW-ECGs were reviewed. Identification of non-sinus rhythm by AW-ECG showed sensitivity of 89%-96% and specificity of 78%-87%.

CONCLUSIONS

We found high levels of agreement for AW-HR values with gold standard modalities during sinus rhythm and poor agreement during tachyarrhythmias, likely due to hemodynamic effects of tachyarrhythmias on PPG-based measurements. AW-ECGs had good sensitivity and moderate specificity in identification of non-sinus rhythm in children.

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

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

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

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

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

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

Remote Photoplethysmography for Evaluation of Cutaneous Sensory Nerve Fiber Function

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

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

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

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

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

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

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

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

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

Binary CorNET: Accelerator for HR Estimation From Wrist-PPG

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

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

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

A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography

Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created the necessity for continuous noninvasive glucose monitoring. This article presents a wearable system for glucose monitoring based on a single wavelength near-infrared (NIR) Photoplethysmography (PPG) combined with machine-learning regression (MLR). The PPG readout circuit consists of a switched capacitor Transimpedance amplifier with 1 MΩ gain and a 10-Hz switched capacitor LPF. It allows a DC bias current rejection up to 20 μA with an input-referred current noise of 7.3 pA/√Hz. The proposed digital processor eliminates motion artifacts, and baseline drifts from PPG signal, extracts six distinct features and finally predicts the blood glucose level using Support Vector Regression with Fine Gaussian kernel (FGSVR) MLR. A novel piece-wise linear (PWL) approach for the exponential function is proposed to realize the FGSVR on-chip. The overall system is implemented using a 180 nm CMOS process with a chip area of 4.0 mm² while consuming 1.62 mW. The glucose measurements are performed for 200 subjects with R² of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.

A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications

A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO₂) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a −10 dB impedance bandwidth of 36 MHz, a peak realized gain of −4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device.

Usefulness of Noninvasively Measured Pulse Amplitude Changes During the Valsalva Maneuver to Identify Hospitalized Heart Failure Patients at Risk of 30-Day Heart Failure Events (from the PRESSURE-HF Study)

The pulse amplitude ratio (PAR), the ratio of pulse pressure at the end of the Valsalva maneuver to before the onset, correlates with cardiac filling pressure. We have developed a handheld device that uses finger photoplethysmography to measure PAR and estimate left ventricular end diastolic pressure (LVEDP). Patients hospitalized with heart failure (HF) performed three 10-second trials of a standardized Valsalva maneuver (at 20 mm Hg measured via pressure transducer), while photoplethysmography waveforms were recorded, at admission and discharge. Combined primary outcome was 30-day HF hospitalization, intravenous diuresis, or death. Fifty-two subjects had discharge PAR testing; 12 met the primary outcome. Median PAR on admission was 0.55 (interquartile range: 0.40 to 0.70, n = 48) and on discharge was 0.50 (interquartile range: 0.36 to 0.69). Mean PAR-estimated LVEDP was significantly higher in subjects that had an event (20.2 vs 16.9 mm Hg, p = 0.043). Subjects with PAR-estimated LVEDP >19.5 mm Hg had an event rate hazard ratio of 4.57 (95% confidence interval 1.37, 15.19, p = 0.013) compared with patients with LVEDP 19.5 mm Hg or below, with significantly lower 30-day event-free survival (log-rank p = 0.006). In conclusion, noninvasively estimated LVEDP using the pulse amplitude response to a Valsalva maneuver in patients hospitalized for HF changes with diuresis and identifies patients at high risk for 30-day HF events. Detection of elevated filling pressures before hospital discharge may be useful in guiding HF management to reduce HF events.

Monitoring driver health status in real time

Nowadays, surveillance systems have evolved significantly; hence, in order to meet the specific needs of the health sector and to monitor the patients' health conditions, intelligent systems have been proposed. These innovations represent a primordial role in road safety, which reduce the risk of traffic accidents. This paper describes an intelligent system design for remote monitoring (tele-monitoring) of a driver's health condition in real time. The measurement using new hardware and software devices is made possible through the contact between the driver contact and an intelligent steering wheel, which is coupled either to an integrated monitor or to a bluetooth link with a local Android smartphone. The driver's heart rate is calculated through the continuous collection of the electrocardiographic signal as well as the blood oxygen saturation SpO₂ by using the photoplethysmographic technique. Consequently, it is necessary to monitor the two vital functions of the driver, cardiac and respiratory activity. This information is transmitted to a remote tele-vigilance center in the case of abnormalities in these functions under the transmission control protocol/internet protocol involving a 4G/3G connection. The application is associated with the system that triggers high and low alarms locally and remotely in the events of tachycardia, bradycardia, or cardiac arrhythmia. Furthermore, another alarm is also triggered in the event of respiratory decompensation.

In silico and in vivo investigations using an endocavitary photoplethysmography sensor for tissue viability monitoring

SIGNIFICANCE

Colorectal cancer is one of the major causes of cancer-related deaths worldwide. Surgical removal of the cancerous growth is the primary treatment for this disease. A colorectal cancer surgery, however, is often unsuccessful due to the anastomotic failure that may occur following the surgical incision. Prevention of an anastomotic failure requires continuous monitoring of intestinal tissue viability during and after colorectal surgery. To date, no clinical technology exists for the dynamic and continuous monitoring of the intestinal perfusion.

AIM

A dual-wavelength indwelling bowel photoplethysmography (PPG) sensor for the continuous monitoring of intestinal viability was proposed and characterized through a set of in silico and in vivo investigations.

APPROACH

The in silico investigation was based on a Monte Carlo model that was executed to quantify the variables such as penetration depth and detected intensity with respect to the sensor-tissue separations and tissue perfusion. Utilizing the simulated information, an indwelling reflectance PPG sensor was designed and tested on 20 healthy volunteers. Two sets of in vivo studies were performed using the driving current intensities 20 and 40 mA for a comparative analysis, using buccal tissue as a proxy tissue-site.

RESULTS

Both simulated and experimental results showed the efficacy of the sensor to acquire good signals through the "contact" to a "noncontact" separation of 5 mm. A very slow wavelength-dependent variation was shown in the detected intensity at the normal and hypoxic states of the tissue, whereas a decay in the intensity was found with the increasing submucosal-blood volume. The simulated detected-to-incident-photon-ratio and the experimental signal-to-noise ratio exhibited strong positive correlations, with the Pearson product-moment correlation coefficient R ranging between 0.65 and 0.87.

CONCLUSIONS

The detailed feasibility analysis presented will lead to clinical trials utilizing the proposed sensor.

A Pulsatile Optical Tissue Phantom for the Investigation of Light-Tissue Interaction in Reflectance Photoplethysmography

The aim of this study was to investigate the effect of emitter-detector separation distance and arterial depth in reflectance photoplethysmography (PPG), utilizing a homogenous pulsatile phantom that exhibits the broad optical absorbance and scattering properties of human tissue. The developed phantom comprised of embedded silicone arteries (outer diameter = 4 mm) that were arranged parallel to one another at nine increasing depths (3.2 mm to 24.4 mm). A pulsatile pump (Harvard Apparatus, MA, USA) circulated a blood imitating fluid through the vessels at the desired heart rate (60 bpm) and stroke volume (5 Lmin^-1). The PPG sensor's emitter and detector were isolated on a translation bridge to provide a computer-controlled separation distance between them. Recordings were taken at each vessel depth for emitter-detector separation distances from 2 mm to 8 mm in 0.1 mm steps. The optimum separation distance between the emitter and detector for vessels between depths of 3.2 mm and 10.5 mm was between 3.7 and 4.3 mm, suggesting that the maximum penetration of IR (930 nm) light in a homogenous pulsatile phantom is no greater than 10.5 mm.

Pulse decomposition analysis in camera-based photoplethysmography

Imaging photoplethysmography (iPPG) is an interesting alternative to laser speckle contrast imaging for the analysis of spatio-temporal patterns in the cutaneous microcirculation. Recent years have witnessed the development of sophisticated techniques for the non-invasive extraction of vascular-related features. These techniques, referred to as pulse decomposition algorithms (PDA), most often involve the analysis of photoplethysmographic waves. This study validated the use of a multi-Gaussian (PDA) for the automatic mapping of iPPG pulse waveforms acquired with a standard camera. We show that iPPG-based PDA can reveal differences in skin perfusion in response to cold stimuli. The study thus proves the potential for morphological analyses of the iPPG pulse waveform.

The Sensing Endotracheal Tube

Current pulse oximetry sensors are not very well suited to use in anaesthetised patients as it has been shown that during episodes of reduced peripheral circulation they do not function correctly or fail all together [1], [2]. To address this problem a new design for a photoplethysmography (PPG) endotracheal (ET) sensor to monitor pulse rate and oxygen saturation (SpO₂) internally is presented. Flexible printed circuit board (PCB) technology and miniature optoelectronic components have been implemented and integrated with a custom instrumentation system [3]. The sensor adheres and conforms to the curvature of standard french-gauge 7 and 8 ET tubes at the point just above the inflatable cuff within the laryngeal positioning markings. A 3D-modelled, optically clear, soft silicon encapsulation electronically and thermally isolates the electronic components whilst providing a smooth surface to aid the insertion on the ET tube during standard intubation procedures. A pilot study with 5 patients (3 Female, 2 Male), undergoing abdominal and limb laproscopic procedures has demonstrated the operation of the sensing ET tube, showing good quality red and infra-red PPG signals. Preliminary signal analysis reveals heart rate can be measured via PPG successfully, with saturation (SpO₂) readings in close agreement with the commercial monitors of 97.9 % (STD 0.2 %) and 98.6 % (STD 0.8 %) respectively.

BioTranslator: Inferring R-Peaks from Ambulatory Wrist-Worn PPG Signal

Advancements in wireless sensor networks (WSN) technology and miniaturization of wearable sensors have enabled long-term continuous pervasive biomedical signal monitoring. Wrist-worn photoplethysmography (PPG) sensors have gained popularity given their form factor. However the signal quality suffers due to motion artifacts when used in ambulatory settings, making vital parameter estimation a challenging task. In this paper, we present a novel deep learning framework, BioTranslator, for computing the instantaneous heart rate (IHR), using wrist-worn PPG signals collected during physical activity. Using one-dimensional Convolution-Deconvolution Network, we translate a single channel PPG signal to an electrocardiogram(ECG)-like time series signal, from which relevant R-peak information can be inferred enabling IHR measures. The proposed network configuration was evaluated on 12 subjects of the TROIKA dataset, involved in physical activity. The proposed network identifies 92.8% of R-peaks, besides achieving a mean absolute error of 51±6.3ms with respect to reference ECG-derived IHR.

PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram

Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow long-term unobtrusive monitoring of heart rate in free-living conditions. PPG based heart rate measurement is unfortunately highly susceptible to motion artifacts, particularly when measured from the wrist. Traditional machine learning and deep learning approaches rely on tri-axial accelerometer data along with PPG to perform heart rate estimation. The conventional learning based approaches have not addressed the need for device-specific modeling due to differences in hardware design among PPG devices. In this paper, we propose a novel end-to-end deep learning model to perform heart rate estimation using 8-second length input PPG signal. We evaluate the proposed model on the IEEE SPC 2015 dataset, achieving a mean absolute error of 3.36±4.1BPM for HR estimation on 12 subjects without requiring patient-specific training. We also studied the feasibility of applying transfer learning along with sparse retraining from a comprehensive in-house PPG dataset for heart rate estimation across PPG devices with different hardware design.

Nine degree of freedom motion estimation for wrist PPG heart rate measurements

In recent years there has been substantial interest in wearable devices that measure heart rate via photoplethysmography (PPG) sensors placed at the wrist. This is challenging as the wrist PPG signal is severely corrupted by artefacts during motion, and although a number of algorithms are now available commercially and academically there is still a need for improved performance, especially when examining physical activities other than running. To date, algorithms for motion artefact removal from the PPG have focused on the use of a co-located accelerometer to record the motion. In this work, we introduce co-located accelerometer, gyroscope and magnetometer sensors to allow three, six and nine degrees of freedom estimates of the motion present. Assessed during a bike riding task the results show that the heart rate estimation is improved by up 0.57 beats per minute by using the additional information from these new sensors.

Design of Multi-Wavelength Optical Sensor Module for Depth-Dependent Photoplethysmography

The multi-wavelength photoplethysmography sensors were introduced to measure depth-dependent blood volume based on that concept that the longer the light wavelength, the deeper the penetration depth near visible spectrum band. In this study, we propose an omnidirectional optical sensor module that can measure photoplethysmogram while using multiple wavelengths, and describe implementation detail. The developed sensor is manufactured by making a hole in a metal plate and mounting an LED therein, and it has four wavelength LEDs of blue (460 nm), green (530 nm), red (660 nm), and IR (940 nm), being arranged concentrically around a photodetector. Irradiation light intensity was measured by photoluminescent test, and photoplethymogram was measured with each wavelength simultaneously at a periphery of the human body such as fingertip, earlobe, toe, forehead, and wrist, in order to evaluate the developed sensor. As a result, the developed sensor module showed a linear increase of irradiating light intensity according to the number of LEDs increases, and pulsatile waveforms were observed at all four wavelengths in all measuring sites.

A 2.6 μW Monolithic CMOS Photoplethysmographic (PPG) Sensor Operating With 2 μW LED Power for Continuous Health Monitoring

Photoplethysmography (PPG) enables wearable vitals monitoring. Nevertheless, it is still limited by the few mA of the LEDs driving current. We present a PPG sensor integrating an array of dedicated pinned-photodiodes (PPD) with a full readout chain integrated in a 0.18 μm CMOS Image Sensor (CIS) process. The sensor features a total input referred noise of 0.68 e-rms per PPD, independently of the input light, and achieves a 4.6 μW total power consumption, including the 2 μW LED power, at 1.38 bpm heart rate average error.

A Smartphone-Only Pulse Transit Time Monitor Based on Cardio-Mechanical and Photoplethysmography Modalities

This paper reports a system for monitoring pulse transit time (PTT). Using an Android smartphone and a customized sensing circuit, the system collects seismo-cardiogram (SCG), gyro-cardiogram (GCG), and photoplethysmogram (PPG) recordings. There is no need for any other external stand-alone systems. The SCG and GCG signals are recorded with the inertial sensors of the smartphone, while the PPG signal is recorded using a sensing circuit connected to the audio jack of the phone. The sensing circuit is battery-less, powered by the audio output of the smartphone using an energy harvester that converts audio tones into DC power. PPG waveforms are sampled via the microphone channel. A signal processing framework is developed and the system is experimentally verified on twenty healthy subjects at rest. The PTT is measured as the time difference between the aortic valve (AO) opening points in SCG or GCG and the fiducial points in PPG. The root-mean-square errors between the results from a stand-alone sensor system and the proposed system report 3.9 ms from SCG-based results and 3.4 ms from GCG-based results. The detection rates report more than 97.92% from both SCG and GCG results. This performance is comparable with stand-alone sensor nodes at a much lower cost.

A Real-Time Vital-Sign Monitoring in the Physical Domain on a Mixed-Signal Reconfigurable Platform

This work presents a mixed-signal physical-compu-tation-electronics for monitoring three vital signs; namely heart rate, blood pressure, and blood oxygen saturation; from electrocardiography, arterial blood pressure, and photoplethysmography signals in real-time. The computational circuits are implemented on a reconfigurable and programmable signal-processing platform, namely field-programmable analog array (FPAA). The design leverages the core enabling technology of FPAA, namely floating-gate CMOS devices, and an on-chip low-power microcontroller to achieve energy-efficiency while not compromising accuracy. The custom physical-computation-electronics operating in CMOS subthreshold region, performs low-level (i.e., physiologically-relevant feature extraction) and high-level (i.e., detecting arrhythmia) signal processing in an energy-efficient manner. The on-chip microcontroller is used (1) in the programming mode for controlling the charge storage at the analog-memory elements to introduce patient-dependency into the system and (2) in the run mode to quantify the vital signs. The system has been validated against digital computation results from MATLAB using datasets collected from three healthy subjects and datasets from the MIT/BIH open source database. Based on all recordings in the MIT/BIH database, ECG R-peak detection sensitivity is 94.2%. The processor detects arrhythmia in three MIT/BIH recordings with an average sensitivity of 96.2%. The cardiac processor achieves an average percentage mean error bounded by 3.75%, 6.27%, and 7.3% for R-R duration, systolic blood pressure, and oxygen saturation level calculations; respectively. The power consumption of the ECG, blood-pressure and photo-plethysmography processing circuitry are 126 nW, 251 nW and 1.44 μW respectively in a 350 nm process. Overall, the cardiac processor consumes 1.82 μW.

Low-Noise Photoplethysmography Sensor Using Correlated Double Sampling for Heartbeat Interval Acquisition

This study designs a low-power photoplethysmography (PPG) sensor based on the error compensation method for heartbeat interval acquisition. To perform heartbeat monitoring in daily life, it is necessary to obtain long-term and accurate heartbeat interval data with low power consumption, because of the limited size and battery capacity of the PPG sensor. Effective reduction in the power consumption of the sensor requires the duty-cycled LEDs and lowering pulse repetition frequency (PRF), i.e., decreasing the sampling rate. However, these methods reduce the accuracy of the heartbeat interval measurement because of signal-to-noise ratio (SNR) degradation and sampling errors. We propose an algorithm for heartbeat interval error compensation and incorporate a low-noise readout circuit to improve SNR. The readout circuit uses current integration to achieve low duty-cycle LED driving. A correlated double sampling (CDS) is introduced to minimize the random noise arising from the switching operation of the integration circuit. An error compensation method based on the PPG waveform similarity is also introduced using the autocorrelation and linear interpolation. The measurement results obtained from nine subjects show that a total current consumption of 28.2 μA is achieved with a 20-Hz PRF and 0.3% LED duty cycle. The proposed design effectively reduces the mean absolute error (MAE) of the heartbeat interval to an average of 6.2 ms.

Validation of Wireless Sensors for Psychophysiological Studies

James One (MindProber Labs) is a wireless psychophysiological device comprising two sensors: one measuring electrodermal activity (EDA), the other photoplethysmography (PPG). This paper reports the validation of James One’s EDA sensor by comparing its signal against a research grade polygraph. Twenty participants were instructed to perform breathing exercises to elicit the modulation of EDA and heart rate, while the physiological signal was captured simultaneously on James One and a Biopac MP36. The resulting EDA and PPG records collected from both systems were comprehensively compared. Results suggest that James One captures EDA signal with a quality comparable to a research grade equipment, this constituting a reliable means of capturing data while minimizing setup time and intrusiveness.

A Dynamic Spectrum extraction method for extracting blood scattering information - Dual-position extraction method

Dynamic Spectrum (DS) can effectively suppress the influences of individual differences (such as skin, muscle, fat) and the variations of measurement conditions, which shows its potential in the clinical applications. To suppress the non-linear effect of blood scattering on DS and improve the accuracy of noninvasive blood component analysis, a dual-position extraction method was proposed. By extracting DS from the upper and lower halves in a spectrum photoplethysmography (SPPG) (photoplethysmography signal at multiple wavelengths) signal separately, the DS of pulsatile partial of arterial blood contracting in two different positions can be extracted from one sample. Then, the DS containing gross errors is screened by statistical method, and the final DS output is obtained by superimposing the remainder DS averagely. To verify the effectiveness of the new method, single-trial extraction, optimized difference extraction and dual-position extraction method were used to extract DS from 231 volunteers' clinical experimental data respectively. Partial Least Squares (PLS) and Radial Basis Function (RBF) neural network were used to establish hemoglobin concentration prediction model. The results show that the modeling indicators of the dual-position extraction are lower than the other two extraction methods. However, combining the spectral data extracted by the dual-position extraction with the spectral data extracted by the single-trial extraction, the obtained modeling indicators are superior to other methods. To make full use of the advantages of the dual-position extraction method, the RBF neural network, a non-linear modeling method is used to model. The result means that, increasing spectral information at different positions can obtain more scattering information of blood. In the modeling, especially for the non-linear modeling method, dual-position extraction can better suppress the non-linear effects of blood scattering and improve the prediction accuracy of non-invasive blood component analysis.

A Novel Photoplethysmography Sensor for Vital Signs Monitoring from the Human Trachea

Current pulse oximeter sensors can be challenged in working accurately and continuously in situations of reduced periphery perfusion, especially among anaesthetised patients. A novel tracheal photoplethysmography (PPG) sensor has been developed in an effort to address the limitations of current pulse oximeters. The sensor has been designed to estimate oxygen saturation (SpO2) and pulse rate, and has been manufactured on a flexible printed circuit board (PCB) that can adhere to a standard endotracheal (ET) tube. A pilot clinical trial was carried out as a feasibility study on 10 anaesthetised patients. Good quality PPGs from the trachea were acquired at red and infrared wavelengths in all patients. The mean SpO2 reading for the ET tube was 97.1% (SD 1.0%) vs. the clinical monitor at 98.7% (SD 0.7%). The mean pulse rate for the ET sensor was 65.4 bpm (SD 10.0 bpm) vs. the clinical monitor at 64.7 bpm (SD 9.9 bpm). This study supports the hypothesis that the human trachea could be a suitable monitoring site of SpO2 and other physiological parameters, at times where the periphery circulation might be compromised.

An Injectable System for Subcutaneous Photoplethysmography, Accelerometry, and Thermometry in Animals

Obtaining physiological data from animals in a non-obtrusive and continuous manner is important to veterinary science. This paper demonstrates the design and deployment of a miniaturized capsule-based system for subdermal injection to provide real-time and continuous heart-rate, movement, and core-body-temperature measurements. The presented device incorporates sensors for photoplethysmography, motion detection, and temperature measurements. A bluetooth-low-energy enabled microcontroller configures the sensors, digitizes the sensor information, and wirelessly connects with external devices. The device is powered by a CR425 battery for this paper, and various other battery solutions are available based upon the use case. The design uses only commercially available integrated circuits in order to reduce the development cost and be modular. The encapsulation is a combination of medical epoxy and poly(methyl methacrylate) that fits within a 6-gauge hypodermic needle. The preliminary evaluation of the device included an in vitro assessment of its thermal response and measurement accuracy, the impact of one-month implantation on surrounding tissue, the power consumption with duty cycling of various sensors, and a measurement of physiological signals in a rat and a chicken. Having a form factor and implantation method similar to existing devices for animals, this novel system is a useful platform for both scientists and veterinarians to better study a diverse range of animals.

Reflectance-Based Organic Pulse Meter Sensor for Wireless Monitoring of Photoplethysmogram Signal

This paper compares the structural design of two organic biosensors that minimize power consumption in wireless photoplethysmogram (PPG) waveform monitoring. Both devices were fabricated on the same substrate with a red organic light-emitting diode (OLED) and an organic photodiode (OPD). Both were designed with a circular OLED at the center of the device surrounded by OPD. One device had an OLED area of 0.06 cm2, while the other device had half the area. The gap distance between the OLED and OPD was 1.65 mm for the first device and 2 mm for the second. Both devices had an OPD area of 0.16 cm2. We compared the power consumption and signal-to-noise ratio (SNR) of both devices and evaluated the PPG signal, which was successfully collected from a fingertip. The reflectance-based organic pulse meter operated successfully and at a low power consumption of 8 µW at 18 dB SNR. The device sent the PPG waveforms, via Bluetooth low energy (BLE), to a PC host at a maximum rate of 256 kbps data throughput. In the end, the proposed reflectance-based organic pulse meter reduced power consumption and improved long-term PPG wireless monitoring.

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.

Cardiovascular assessment by imaging photoplethysmography - a review

Over the last few years, the contactless acquisition of cardiovascular parameters using cameras has gained immense attention. The technique provides an optical means to acquire cardiovascular information in a very convenient way. This review provides an overview on the technique's background and current realizations. Besides giving detailed information on the most widespread application of the technique, namely the contactless acquisition of heart rate, we outline further concepts and we critically discuss the current state.

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.

Can pulse check by the photoplethysmography sensor on a smart watch replace carotid artery palpation during cardiopulmonary resuscitation in cardiac arrest patients? a prospective observational diagnostic accuracy study

OBJECTIVE

The purpose of this study was to assess whether a photoplethysmography (PPG) sensor in a smart watch can accurately recognise the return of spontaneous circulation (ROSC) in cardiac arrest patients compared with carotid artery palpation.

METHODS

This prospective observational study was conducted on 50 out-of-hospital cardiac arrest patients who visited the emergency department (ED) of one tertiary hospital. As soon as the patient arrived at the ED, advanced cardiac life support was carried out immediately. At this time, three smart watches were attached to the carotid artery, forehead and wrist and were checked for pulse measurements every 2 min. In the case of ROSC, blood pressure, heart rate and heart rate regularity were confirmed, and pulse was simultaneously measured at three sites with smart watches. In the case of no ROSC, only the pulse was measured at three sites with the smart watches.

RESULTS

There were 33 males (66%) and the mean age was 68±11.57 years. In 14 patients (28%), spontaneous circulation was recovered through cardiopulmonary resuscitation, and all survived. The sensitivity and specificity of manual palpation were 78.6% and 90.4%, respectively. False-positive and false-negative rates were 9.6% and 21.4%, respectively. Smart watches at all three sites had the same or higher sensitivity than manual palpation. The sensitivity of the smart watch was the highest, at 100%, in the carotid region and the lowest, at 78.6%, in the wrist region. The specificity of the smart watch was the highest, at 100%, in the wrist region and the lowest, at 78.7%, in the carotid region.

CONCLUSION

Compared with manual pulse check, the PPG sensor embedded in the smart watch showed the same sensitivity and a higher specificity for recognising ROSC when measured at the wrist.

Multi-Mode Particle Filtering Methods for Heart Rate Estimation From Wearable Photoplethysmography

OBJECTIVE

Obtaining accurate estimates of instantaneous heart rates (HRs) using reflectance-type photoplethysmography (PPG) sensors is challenging because the dominant frequency observed in the PPG signal can be corrupted by motion artifacts (MAs), especially during exercise. To address this problem, we propose multi-mode particle filtering (MPF) methods.

METHODS

We propose four MPF methods based on different approaches to particle weighting and HR determination. We compare the MPF performances with single-mode particle filtering and other state-of-the-art methods.

RESULTS

When applied to 47 PPG recordings obtained during intensive physical exercise from two different databases, the proposed MPF methods exhibit an average absolute error of less than two beats per minute, which is less than the errors of the SPF and other state-of-the-art methods. Furthermore, the MPF methods require only 6.4-6.5 ms in an 8 s window.

CONCLUSION

The MPF methods significantly reduce the HR estimation error and can be implemented in real-time in practical applications.

SIGNIFICANCE

Our proposed MPF methods accurately estimate HRs even during intensive physical exercise, with robustness evidenced by their accuracy even when PPG signals are severely corrupted by MAs in several consecutive windows. The proposed methods can also be applied to other time-varying physiological feature-monitoring problems.

Frame Registration for Motion Compensation in Imaging Photoplethysmography

Imaging photoplethysmography (iPPG) is an emerging technology used to assess microcirculation and cardiovascular signs by collecting backscattered light from illuminated tissue using optical imaging sensors. An engineering approach is used to evaluate whether a silicone cast of a human palm might be effectively utilized to predict the results of image registration schemes for motion compensation prior to their application on live human tissue. This allows us to establish a performance baseline for each of the algorithms and to isolate performance and noise fluctuations due to the induced motion from the temporally changing physiological signs. A multi-stage evaluation model is developed to qualitatively assess the influence of the region of interest (ROI), system resolution and distance, reference frame selection, and signal normalization on extracted iPPG waveforms from live tissue. We conclude that the application of image registration is able to deliver up to 75% signal-to-noise (SNR) improvement (4.75 to 8.34) over an uncompensated iPPG signal by employing an intensity-based algorithm with a moving reference frame.

A Low-Power Photoplethysmogram-Based Heart Rate Sensor Using Heartbeat Locked Loop

In this paper, we present an ultralow power heart rate (HR) monitoring photoplethysmography (PPG) sensor using a heartbeat locked loop (HBLL). The HBLL generates a narrow window that turns on the LED and analog-front-end only when a peak is expected in the PPG signal. The prototype PPG sensor implemented in 0.18  μm CMOS has an effective duty-cycle of 0.01% and consumes only 43.4  μW at a HR of 60 b/m, which is the lowest power consumption compared with previous state-of-the-art PPG sensors. The HR error of the proposed sensor is less than 2.1 b/m for HR below 180 b/m.

Low-Cost Mobile Device for Screening of Atherosclerosis and Coronary Arterial Disease

In the context of global health, telemedicine, and low-resource settings, we present a non-invasive smart-phone based device that can be used to screen for atherosclerosis,which is the leading factor for ischemic heart attacks and strokes. Using acustom Android mobile application, our device computes Pulse Wave Velocity(PWV) using the pulse signals from photo-plethysmographic (PPG) probes, which are simultaneously clipped onto the ear, index finger, and big toe of a human subject. Unlike other designs which require the use of an ECG reference, our mobile device uses only PPG signals and is entirely powered by the mobile phone via the USB port. Using the ear signal as a reference, we derived PWV values from two locations: the right index finger, and the right big toe.We present data from a recent clinical study with 78 participants (age 26 to 74) who were divided into three groups: Coronary Arterial Disease ("CAD"), hypertensive group ("PreCAD"), and Healthy controls. The CAD group was clinically diagnosed and confirmed with a CT-scan and calcium scoring. PWV values derived from the finger was found to have too much variance to be clinically useful. However, PWV values derived from the toe location showed significant differences between the groups, even after accounting for age. Measured PWV values were: 10.07 (8.51-12.01) for the older CAD group, 9.39 (7.44-9.75) for the younger CAD group, 8.26 (7.26-9.22) for the older Pre-CAD group, 10.57 m/s (8.5-11.2) for the younger Pre-CAD group, 7.13 m/s (5.97-7.69) for older healthy controls, and 6.71 m/s (4.86-7.26) for the younger healthy control subjects. These results demonstrate good potential value of this mobile PWV device as a simple low-cost screening tool for atherosclerosis and coronary arterial disease.

Improved Heart Rate Tracking Using Multiple Wrist-type Photoplethysmography during Physical Activities

Photoplethysmography (PPG) signals collected from wearable sensing devices during physical exercise are easily corrupted by motion artifact (MA), which poses great challenge on heart rate (HR) estimation. This paper proposes a new framework to accurately estimate HR using two leads of PPG signals in combination with accelerometer (ACC) data in the presence of MA. A moving time window is first used to segment PPG signals and ACC signals. Then, MA is attenuated by joint sparse spectrum reconstruction in each time window, where maximum spectrum frequencies of ACC are subtracted from the spectrum frequency of PPG signals. Further, HR for each cleansed PPG is estimated from the frequency with maximum amplitude in the sparse spectrum. The actual HR is determined using spectral band powers calculated from each reconstructed PPG signals. The proposed method was validated using the 2015 IEEE Signal Processing Cup dataset. The average absolute error is 1.15 beats per minutes (BPM) (standard deviation: 2.00 BPM), and the average absolute error percentage is 0.95% (standard deviation: 1.86%). The proposed method outperforms the previously reported work in terms of accuracy.

Fusing non-contact vital sign sensing modalities - first results

A wearable multi-sensor system allowing synchronized unobtrusive measurements of 4 vital signs at a dedicated location of interest is presented. The 4×U sensor is capable of synchronously measuring magnetic impedance, reflective photoplethysmography, capacitive electrocardiogram and seismocardiography (ballistocardiography). The hardware of all modalities is described and some preliminary results are reported.

InPhysible: Camouflage Against Video-Based Physiological Measurement

Imaging photoplethysmography (iPPG) is a powerful set of methods for measuring physiological signals from video. Recent advances have shown that a low-cost webcam can be used to measure heart rate, blood flow, respiration, blood oxygen levels and stress. While these methods have many beneficial applications, the unobtrusive and ubiquitous nature of the sensors risk exposing people to unwanted measurement. We present InPhysible the first camouflage system against video- based physiological measurement. The infra-red system can be embedded into any pair of glasses, or other headwear, and disrupts the measurement of the iPPG signal while being imperceptible by the human eye. Our system is flexible and can simulate realistic pulse signals to hinder heart rate measurement. In this paper we present the design of our prototype and a user study validating its efficacy. Finally, we discuss the limitations and implications for data privacy and security.

A Portable, Wireless Photoplethysomography Sensor for Assessing Health of Arteriovenous Fistula Using Class-Weighted Support Vector Machine

A portable, wireless photoplethysomography (PPG) sensor for assessing arteriovenous fistula (AVF) by using class-weighted support vector machines (SVM) was presented in this study. Nowadays, in hospital, AVF are assessed by ultrasound Doppler machines, which are bulky, expensive, complicated-to-operate, and time-consuming. In this study, new PPG sensors were proposed and developed successfully to provide portable and inexpensive solutions for AVF assessments. To develop the sensor, at first, by combining the dimensionless number analysis and the optical Beer Lambert’s law, five input features were derived for the SVM classifier. In the next step, to increase the signal-noise ratio (SNR) of PPG signals, the front-end readout circuitries were designed to fully use the dynamic range of analog-digital converter (ADC) by controlling the circuitries gain and the light intensity of light emitted diode (LED). Digital signal processing algorithms were proposed next to check and fix signal anomalies. Finally, the class-weighted SVM classifiers employed five different kernel functions to assess AVF quality. The assessment results were provided to doctors for diagonosis and detemining ensuing proper treatments. The experimental results showed that the proposed PPG sensors successfully achieved an accuracy of 89.11% in assessing health of AVF and with a type II error of only 9.59%.

Noninvasive Monitoring of Blood Glucose Using Color-Coded Photoplethysmographic Images of the Illuminated Fingertip Within the Visible and Near-Infrared Range: Opportunities and Questions

Noninvasive blood glucose assays have been promised for many years and various molecular spectroscopy-based methods of skin are candidates for achieving this goal. Due to the small spectral signatures of the glucose used for direct physical detection, moreover hidden among a largely variable background, broad spectral intervals are usually required to provide the mandatory analytical selectivity, but no such device has so far reached the accuracy that is required for self-monitoring of blood glucose (SMBG). A recently presented device as described in this journal, based on photoplethysmographic fingertip images for measuring glucose in a nonspecific indirect manner, is especially evaluated for providing reliable blood glucose concentration predictions.

Multi-Wavelength Photoplethysmography Enabling Continuous Blood Pressure Measurement With Compact Wearable Electronics

OBJECTIVE

To fight the "silent killer" hypertension, continuous blood pressure (BP) monitoring has been one of the most desired functions in wearable electronics. However, current BP measuring principles and protocols either involve a vessel occlusion process with a cuff or require multiple sensing nodes on the body, which makes it difficult to implement them in compact wearable electronics like smartwatches and wristbands with long-term wearability.

METHODS

In this work, we proposed a highly compact multi-wavelength photoplethysmography (MWPPG) module and a depth-resolved MWPPG approach for continuous monitoring of BP and systemic vascular resistance (SVR). By associating the wavelength-dependent light penetration depth in the skin with skin vasculatures, our method exploited the pulse transit time (PTT) on skin arterioles for tracking SVR (n = 20). Then, we developed an arteriolar PTT-based method for beat-to-beat BP measurement. The BP estimation accuracy of the proposed arteriolar PTT method was validated against Finometer (n = 20) and the arterial line (n = 4).

RESULTS

The correlation between arteriolar PTT and SVR was theoretically deduced and experimentally validated on 20 human subjects performing various maneuvers. The proposed arteriolar PTT-based method outperformed the traditional arterial PTT-based method with better BP estimation accuracy and simpler measurement setup, i.e., with a single sensing node.

CONCLUSION

The proposed depth-resolved MWPPG method can provide accurate measurements of SVR and BP, which are traditionally difficult to measure in a noninvasive or continuous fashion.

SIGNIFICANCE

This MWPPG work provides the wearable healthcare electronics of compact size with a low-cost and physiology-based solution for continuous measurement of BP and SVR.

Hemodynamic Sensing of 3-D Fingertip Force by Using Nonpulsatile and Pulsatile Signals in the Proximal Part

This study proposed a novel sensing method of 3-D contact force at a fingertip by using a photoplethysmogram (PPG) device on the proximal part of a finger. The proposed system detects nonpulsatile and pulsatile components of PPG signals from both sides of the proximal part, extracts 16 feature values related to the contact force, and estimates the 3-D force by using a multiple linear regression model. In the validation experiments, the participants wore a PPG device at the proximal parts of their index fingers and applied a contact force at the fingertips for the 11 types of touch actions. The results indicated that satisfactory agreements are observed between the system outputs and the reference forces by the calibrated force sensor. Moreover, the results revealed that the most effective number of feature values corresponded to six for the higher reproducible sensing. Although the development of the effective calibration method is expected to increase robustness, we realized that the proposed method can potentially be used for a 3-D input user interface.

Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis (p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

In vivo performance of a visible wavelength optical sensor for monitoring intestinal perfusion and oxygenation

Traumatic injury resulting in hemorrhage is a prevalent cause of death worldwide. The current standard of care for trauma patients is to restore hemostasis by controlling bleeding and administering intravenous volume resuscitation. Adequate resuscitation to restore tissue blood flow and oxygenation is critical within the first hours following admission to assess severity and avoid complications. However, current clinical methods for guiding resuscitation are not sensitive or specific enough to adequately understand the patient condition. To better address the shortcomings of the current methods, an approach to monitor intestinal perfusion and oxygenation using a multiwavelength (470, 560, and 630 nm) optical sensor has been developed based on photoplethysmography and reflectance spectroscopy. Specifically, two sensors were developed using three wavelengths to measure relative changes in the small intestine. Using vessel occlusion, systemic changes in oxygenation input, and induction of hemorrhagic shock, the capabilities and sensitivity of the sensor were explored in vivo. Pulsatile and nonpulsatile components of the red, blue, and green wavelength signals were analyzed for all three protocols (occlusion, systemic oxygenation changes, and shock) and were shown to differentiate perfusion and oxygenation changes in the jejunum. The blue and green signals produced better correlation to perfusion changes during occlusion and shock, while the red and blue signals, using a new correlation algorithm, produced better data for assessing changes in oxygenation induced both systemically and locally during shock. The conventional modulation ratio method was found to be an ineffective measure of oxygenation in the intestine due to noise and an algorithm was developed based on the Pearson correlation coefficient. The method utilized the difference in phase between two different wavelength signals to assess oxygen content. A combination of measures from the three wavelengths provided verification of oxygenation and perfusion states, and showed promise for the development of a clinical monitor.