Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method

Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors

Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years.

Two-Stage Motion Artifact Reduction Algorithm for rPPG Signals Obtained from Facial Video Recordings

Recent years have witnessed the publication of many research articles regarding the contactless measurement and monitoring of heart rate signals deduced from facial video recordings. The techniques presented in these articles, such as examining the changes in the heart rate of an infant, provide a noninvasive assessment in many cases where the direct placement of any hardware equipment is undesirable. However, performing accurate measurements in cases that include noise motion artifacts still presents an obstacle to overcome. In this research article, a two-stage method for noise reduction in facial video recording is proposed. The first stage of the system consists of dividing each (30) seconds of the acquired signal into (60) partitions and then shifting each partition to the mean level before recombining them to form the estimated heart rate signal. The second stage utilizes the wavelet transform for denoising the signal obtained from the first stage. The denoised signal is compared to a reference signal acquired from a pulse oximeter, resulting in the mean bias error (0.13), root mean square error (3.41) and correlation coefficient (0.97). The proposed algorithm is applied to (33) individuals being subjected to a normal webcam for acquiring their video recording, which can easily be performed at homes, hospitals, or any other environment. Finally, it is worth noting that this noninvasive remote technique is useful for acquiring the heart signal while preserving social distancing, which is a desirable feature in the current period of COVID-19.

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

A new approach to HR monitoring using photoplethysmographic signals during intensive physical exercise

The use of photoplethysmography (PPG) on the wrist to measure physiological indicators has attracted wide attention because of the portability and real-time characteristic of this technology. However, accurate estimation of the heart rate (HR) is difficult to realize using PPG because of the interference of motion artifacts. To address this problem, a method combining multichannel PPG signals is proposed. By using a peak selection method that combines several factors based on scores, the appropriate frequency is selected from the spectrum of the PPG signals. The chosen frequency is then considered as the HR. The approach exhibits high accuracy and speed. Experimental results for 12 training sets showed that with the proposed method, an average absolute error of 1.16 beats per minute (BPM) (standard deviation: 1.56 BPM) was obtained. Therefore, the proposed approach is reliable for HR monitoring from PPG during high-intensity physical activities. It can be applied to smart wearable devices for fitness tracking and health information tracking.

Robust Beat-to-Beat Interval from Wearable PPG using RLS and SSA

Ambulatory Photoplethysmogram (PPG) is a more user-friendly choice for continuous cardiac monitoring as compared to Electrocardiogram (ECG). However, wearable PPG is often prone to motion artefacts. In this paper, we propose a novel pipeline for motion-resistant beat-to-beat interval extraction from noisy PPG. Firstly, the effects of motion artefacts are minimized by using Adaptive Recursive-Least-Square (RLS) Filtering and Singular Spectrum Analysis (SSA). Next, the signal peaks are identified and their locations are corrected by weighted local interpolation. Finally, outlier peak-to-peak intervals are marked as incorrigible. Experimental validation on the training dataset of IEEE Signal Processing Cup 2015 reveals that the proposed method achieves 1.68% mean peak detection error rate and 11.32 milliseconds mean absolute error of detected beat-to-beat intervals. The metric values outperform those obtained by the state-of-the-art techniques by at least 12.58 and 5.74 times respectively.

Groundtruth: A Matlab GUI for Artifact and Feature Identification in Physiological Signals

Groundtruth is a Matlab Graphical User Interface (GUI) developed for the identification of key features and artifacts within physiological signals. The ultimate aim of this GUI is to provide a simple means of assessing the performance of new sensors. Secondary, to this is providing a means of providing marked data, enabling assessment of automated artifact rejection and feature identification algorithms. With the emergence of new wearable sensor technologies, there is an unmet need for convenient assessment of device performance, and a faster means of assessing new algorithms. The proposed GUI allows interactive marking of artifact regions as well as simultaneous interactive identification of key features, e.g., respiration peaks in respiration signals, R-peaks in Electrocardiography signals, etc. In this paper, we present the base structure of the system, together with an example of its use for two simultaneously worn respiration sensors. The respiration rates are computed for both original as well as artifact removed data and validated using Bland–Altman plots. The respiration rates computed based on the proposed GUI (after artifact removal process) demonstrated consistent results for two respiration sensors after artifact removal process. Groundtruth is customizable, and alternative processing modules are easy to add/remove. Groundtruth is intended for open-source use.

Current progress of photoplethysmography and SPO2 for health monitoring

A photoplethysmograph (PPG) is a simple medical device for monitoring blood flow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and reflected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiffness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.

Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals

The photoplethysmography (PPG) is inevitably corrupted by many kinds of noise no matter whether its acquisition mode is transmittance or reflectance. To enhance the quality of PPG signals, many studies have made great progress in PPG denoising by adding extra sensors and developing complex algorithms. Considering the reasonable cost, compact size, and real-time and easy implementation, this study proposed a simple real-time denoising method based on double median filters which can be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. First, we used the boundary extension to preserve the signal boundary distortion and designed a first median filter with the time window at approximately 78 ms to eliminate the high-frequency components of the signal. Then, through the second median filter with a time window which was about 780 ms, we estimated the low-frequency components. Finally, we removed the estimated low-frequency components from the signal to obtain the denoised signal. Through comparing the multiple sets of signals under calmly sitting and slightly moving postures, the PPG signals contained noises no matter whether collected by the transmittance-mode or the reflectance-mode. To evaluate the proposed method, we conducted measured, simulated experiments and a strong noisy environment experiment. Through comparing the morphology distortions, frequency spectra, and the signal-to-noise ratios (SNRs), the results showed that the proposed method can suppress noise effectively and preserve the essential morphological features from PPG signals. As a result, the proposed method can enhance the quality of PPG signals and, thus, can contribute to the improvement of the calculation accuracy of the subsequent physiological parameters. In addition, the proposed method could be a good choice to address the real-time noise reduction of portable PPG measuring instruments.

PPG pulse direction determination algorithm for PPG waveform inversion by wrist rotation

This paper describes photoplethysmography (PPG)-based pulse direction determination algorithm on a site of the radial artery using a wrist band. It has been well known that PPG is susceptible to noise and motion artifacts in the mobile environment and many research efforts have been made to focus on rejection of the noise and motion artifacts. However, no research has been performed to find PPG pulses when PPG is inverted by wrist movement. We present an algorithm, which accurately yields which direction PPG pulses face regardless of wrist movement. The algorithm is one step closer to robust real-time PPG pulse direction determination for continuous PPG monitoring regardless of body movements.

Wrist-worn optical and chest strap heart rate comparison in a heterogeneous sample of healthy individuals and in coronary artery disease patients

Background

The need for unobtrusive HR (heart rate) monitoring has led to the development of a new generation of strapless HR monitors. The aim of this study was to determine whether such an unobtrusive, wrist-worn optical HR monitor (OHRM) could be equivalent and therefore a valid alternative to a traditional chest strap during a broad range of activities in a heterogeneous healthy population and coronary artery disease (CAD) patients.

Methods

One hundred ninety-nine healthy volunteers, 84 males and 115 females, including 35 overweight-obese subjects, 53 pregnant women, and 20 CAD patients were tested in the present study. Second-by-second HR measured by the OHRM was concurrently evaluated against an ECG-based chest strap monitor during a broad range of activities (i.e., walking, running, cycling, gym, household, and sedentary activities).

Results

Data coverage, percentage of time the OHRM provides a HR not larger than 10 bpm from the reference, went from a minimum of 92% of the time in the least periodic activity (i.e., gym), to 95% during the most intense activity (i.e., running), and to a maximum of 98% for sedentary activities. The limits of agreement of the difference between the OHRM and the chest strap HR were within the range of ±15 bpm. The OHRM showed a concordance correlation coefficient of 0.98. Overall, the mean absolute error was not larger than 3 bpm, which can be considered clinically acceptable for a number of applications. A similar performance was found for CAD (94.2% coverage, 2.4 bpm error), but the small sample size does not allow any quantitative comparison.

Conclusion

Heart rate measured by OHRM at the wrist and ECG-based HR measured via a traditional chest strap are acceptably close in a broad range of activities in a heterogeneous, healthy population, and showed initial promising results also in CAD patients.

Electronic supplementary material

The online version of this article (10.1186/s13102-018-0098-0) contains supplementary material, which is available to authorized users.

Detection of essential hypertension with physiological signals from wearable devices

Early detection of essential hypertension can support the prevention of cardiovascular disease, a leading cause of death. The traditional method of identification of hypertension involves periodic blood pressure measurement using brachial cuff-based measurement devices. While these devices are non-invasive, they require manual setup for each measurement and they are not suitable for continuous monitoring. Research has shown that physiological signals such as Heart Rate Variability, which is a measure of the cardiac autonomic activity, is correlated with blood pressure. Wearable devices capable of measuring physiological signals such as Heart Rate, Galvanic Skin Response, Skin Temperature have recently become ubiquitous. However, these signals are not accurate and are prone to noise due to different artifacts. In this paper a) we present a data collection protocol for continuous non-invasive monitoring of physiological signals from wearable devices; b) we implement signal processing techniques for signal estimation; c) we explore how the continuous monitoring of these physiological signals can be used to identify hypertensive patients; d) We conduct a pilot study with a group of normotensive and hypertensive patients to test our techniques. We show that physiological signals extracted from wearable devices can distinguish between these two groups with high accuracy.

A Wearable System for Real-Time Continuous Monitoring of Physical Activity

Over the last decades, wearable systems have gained interest for monitoring of physiological variables, promoting health, and improving exercise adherence in different populations ranging from elite athletes to patients. In this paper, we present a wearable system for the continuous real-time monitoring of respiratory frequency (f_R), heart rate (HR), and movement cadence during physical activity. The system has been experimentally tested in the laboratory (by simulating the breathing pattern with a mechanical ventilator) and by collecting data from one healthy volunteer. Results show the feasibility of the proposed device for real-time continuous monitoring of f_R, HR, and movement cadence both in resting condition and during activity. Finally, different synchronization techniques have been investigated to enable simultaneous data collection from different wearable modules.

Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device

Wearable physiological monitors are becoming increasingly commonplace in the consumer domain, but in literature there exists no substantive studies of their performance when measuring the physiology of ambulatory patients. In this Letter, the authors investigate the reliability of the heart-rate (HR) sensor in an exemplar ‘wearable’ wrist-worn monitoring system (the Microsoft Band 2); their experiments quantify the propagation of error from (i) the photoplethysmogram (PPG) acquired by pulse oximetry, to (ii) estimation of HR, and (iii) subsequent calculation of HR variability (HRV) features. Their experiments confirm that motion artefacts account for the majority of this error, and show that the unreliable portions of HR data can be removed, using the accelerometer sensor from the wearable device. The experiments further show that acquired signals contain noise with substantial energy in the high-frequency band, and that this contributes to subsequent variability in standard HRV features often used in clinical practice. The authors finally show that the conventional use of long-duration windows of data is not needed to perform accurate estimation of time-domain HRV features.

A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals

The noninvasive peripheral oxygen saturation (SpO₂) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects' PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO₂ and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.

Characterization of Quadratic Nonlinearity between Motion Artifact and Acceleration Data and its Application to Heartbeat Rate Estimation

Accelerometers are applied to various applications to collect information about movements of other sensors deployed at diverse fields ranging from underwater area to human body. In this study, we try to characterize the nonlinear relationship between motion artifact and acceleration data. The cross bicoherence test and the Volterra filter are used as the approaches to detection and modeling. We use the cross bicoherence test to directly detect in the frequency domain and we indirectly identify the nonlinear relationship by improving the performance of eliminating motion artifact in heartbeat rate estimation using a nonlinear filter, the second-order Volterra filter. In the experiments, significant bicoherence values are observed through the cross bicoherence test between the photoplethysmogram (PPG) signal contaminated with motion artifact and the acceleration sensor data. It is observed that for each dataset, the heartbeat rate estimation based on the Volterra filter is superior to that of the linear filter in terms of average absolute error. Furthermore, the leave one out cross-validation (LOOCV) is employed to develop an optimal structure of the Volterra filter for the total datasets. Due to lack of data, the developed Volterra filter does not demonstrate significant difference from the optimal linear filter in terms of t-test. Through this study, it can be concluded that motion artifact may have a quadaratical relationship with acceleration data in terms of bicoherence and more experimental data are required for developing a robust and efficient model for the relationship.

Investigations on Multisensor-Based Noninvasive Blood Glucose Measurement System

Noninvasive blood glucose (NIBG) measurement technique has been explored for the last three decades to facilitate diabetes management. Photoplethysmogram (PPG) signal may be used to measure the variations in blood glucose concentration. However, the literature reveals that physiological perturbations such as temperature, skin moisture, and sweat lead to less accurate NIBG measurements. The task of minimizing the effect of these perturbations for accurate measurements is an important research area. Therefore, in the present work, galvanic skin response (GSR) and temperature measurements along with PPG were used to measure blood glucose noninvasively. The data extracted from the sensors were used to estimate blood glucose concentration with the help of two machine learning (ML) techniques, i.e., multiple linear regression (MLR) and artificial neural network (ANN). The accuracy of proposed multisensor system was evaluated by pairing and comparing noninvasive measurements with invasively measured readings. The study was performed on 50 nondiabetic subjects with body mass index (BMI) 27.3 ± 3 kg/m2. The results revealed that multisensor NIBG measurement system significantly improves mean absolute prediction error and correlation coefficient in comparison to the techniques reported in the literature.

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts

The estimation of heart rate (HR) based on wearable devices is of interest in fitness. Photoplethysmography (PPG) is a promising approach to estimate HR due to low cost; however, it is easily corrupted by motion artifacts (MA). In this work, a robust approach based on random forest is proposed for accurately estimating HR from the photoplethysmography signal contaminated by intense motion artifacts, consisting of two stages. Stage 1 proposes a hybrid method to effectively remove MA with a low computation complexity, where two MA removal algorithms are combined by an accurate binary decision algorithm whose aim is to decide whether or not to adopt the second MA removal algorithm. Stage 2 proposes a random forest-based spectral peak-tracking algorithm, whose aim is to locate the spectral peak corresponding to HR, formulating the problem of spectral peak tracking into a pattern classification problem. Experiments on the PPG datasets including 22 subjects used in the 2015 IEEE Signal Processing Cup showed that the proposed approach achieved the average absolute error of 1.65 beats per minute (BPM) on the 22 PPG datasets. Compared to state-of-the-art approaches, the proposed approach has better accuracy and robustness to intense motion artifacts, indicating its potential use in wearable sensors for health monitoring and fitness tracking.

Custom FPGA processing for real-time fetal ECG extraction and identification

Monitoring the fetal cardiac activity during pregnancy is of crucial importance for evaluating fetus health. However, there is a lack of automatic and reliable methods for Fetal ECG (FECG) monitoring that can perform this elaboration in real-time. In this paper, we present a hardware architecture, implemented on the Altera Stratix V FPGA, capable of separating the FECG from the maternal ECG and to correctly identify it. We evaluated our system using both synthetic and real tracks acquired from patients beyond the 20th pregnancy week. This work is part of a project aiming at developing a portable system for FECG continuous real-time monitoring. Its characteristics of reduced power consumption, real-time processing capability and reduced size make it suitable to be embedded in the overall system, that is the first proposed exploiting Blind Source Separation with this technology, to the best of our knowledge.

MICROST: A mixed approach for heart rate monitoring during intensive physical exercise using wrist-type PPG Signals

The performance of heart rate (HR) monitoring using wrist-type photoplethysmographic (PPG) signals is strongly influenced by motion artifacts (MAs), since the intensive physical exercises are common in real world. Few works focus on this study so far because of unsatisfying quality of corrupted PPG signals. In this paper, we propose an accurate and efficient strategy, named MICROST, which estimates heart rate based on a mixed approach. The MICROST framework is designed as a MIxed algorithm which consists of acceleration Classification (AC), fiRst-frame prOcessing and heuriStic Tracking. Experimental results using recordings from 12 subjects during fast running and intensive movement showed the average absolute error of heart rate estimation was 2.58 beat per minute (BPM), and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.988. We discuss our approach in real time to face the applications of wearable devices such as smart-watches in reality.

Comparison of Pulse Rate Variability and Heart Rate Variability for Hypoglycemia Syndrome

BACKGROUND

Heart rate variability (HRV) is a signal obtained from RR intervals of electrocardiography (ECG) signals to evaluate the balance between the sympathetic nervous system and the parasympathetic nervous system; not only HRV but also pulse rate variability (PRV) extracted from finger pulse plethysmography (PPG) can reflect irregularities that may occur in heart rate and control procedures.

OBJECTIVES

The purpose of this study is to compare the HRV and PRV during hypoglycemia in order to evaluate the features that computed from PRV that can be used in detection of hypoglycemia.

METHODS

To this end, PRV and HRV of 10 patients who required testing with insulin-induced hypoglycemia (IIHT) in Clinics of Endocrinology and Metabolism Diseases of Bezm-i Alem University (Istanbul, Turkey), were obtained. The recordings were done at three stages: prior to IIHT, during the IIHT, and after the IIHT. We used Bland-Altman analysis for comparing the parameters and to evaluate the correlation between HRV and PRV if exists.

RESULTS

Significant correlation (r > 0.90, p < 0.05) and close agreement were found between HRV and PRV for mean intervals, the root-mean square of the difference of successive intervals, standard deviation of successive intervals and the ratio of the low-to-high frequency power.

CONCLUSIONS

In conclusion, all the features computed from PRV and HRV have close agreement and correlation according to Bland-Altman analyses' results and features computed from PRV can be used in detection of hypoglycemia.

Reduction of Periodic Motion Artifacts in Photoplethysmography

Periodic motion artifacts affect photoplethysmography (PPG) signals in activities of daily living (ADL), cardiopulmonary exercise testing (CPX), and cardiopulmonary resuscitation (CPR). This hampers measurement of interbeat intervals (IBIs) and oxygen saturation (SpO ₂ ). Our objective was to develop a generic algorithm to remove periodic motion artifacts, recovering artifact-reduced PPG signals for beat-to-beat analysis.

METHODS

The algorithm was retrospectively evaluated on forehead PPG signals measured while walking on a treadmill. The step rate was tracked in a motion reference signal via a second-order generalized integrator with a frequency-locked loop. Two reference signals were compared: sensor motion relative to the skin ( ∆x[n]) measured via self-mixing interferometry and head motion ( a_v[n] ) measured via accelerometry. The step rate was used in a quadrature harmonic model to estimate the artifacts. Quadrature components need only two coefficients per frequency leading to a short filter and prevent undesired frequency-shifted components in the artifact estimate. Subtracting the estimate from the measured signal reduced the artifacts.

RESULTS

Compared to ∆x[n] , a_v[n] had a better signal-to-noise ratio and more consistently contained a component at the step rate. Artifact reduction was effective for distinct step rate and pulse rate, since the artifact-reduced signals provided more stable IBI and SpO ₂ measurements.

CONCLUSION

Accelerometry provided a more reliable motion reference signal. The proposed algorithm can be of significance for monitoring in ADL, CPX, or CPR, by providing artifact-reduced PPG signals for improved IBI and SpO ₂ measurements during periodic motion.

Study of Artifact-Resistive Technology Based on a Novel Dual Photoplethysmography Method for Wearable Pulse Rate Monitors

Pulse rate is one of the major physiological parameters for monitoring of cardiovascular conditions or excise states during daily life. However it is difficult to precisely measure the exact pulse rates as photoplethysmography (PPG) is easy to be affected by motion artifacts. Instead of using accelerometers followed by algorithms such as least mean square (LMS), recursive least square (RLS) and independent component analysis (ICA) or other equipment such as complex laser systems to measure displacement directly, a novel motion artifact estimation method which had lower computational complexity and higher signal dynamic range was studied and implemented, where a differential channel following green and red light PPG channels was applied to reduce the motion artifact caused by displacement of light emitting diode (LED), photo diode (PD) and tissue deformation before the analog signal was converted to digital form. A miniaturized, battery powered wrist worn artifact-resistive pulse rates monitoring system (PRMS) was presented to verify the proposed method. Four kinds of motions were performed and the results showed that the differential channel improved the morphology of the PPG signal and appeared to be artifact resistive during motions through light intensity control and high gain-phase consistency circuit design here.

Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion

Reflection photoplethysmography (PPG) using 530 nm (green) wavelength light has the potential to be a superior method for monitoring heart rate (HR) during normal daily life due to its relative freedom from artifacts. However, little is known about the accuracy of pulse rate (PR) measured by 530 nm light PPG during motion. Therefore, we compared the HR measured by electrocadiography (ECG) as a reference with PR measured by 530, 645 (red), and 470 nm (blue) wavelength light PPG during baseline and while performing hand waving in 12 participants. In addition, we examined the change of signal-to-noise ratio (SNR) by motion for each of the three wavelengths used for the PPG. The results showed that the limit of agreement in Bland-Altman plots between the HR measured by ECG and PR measured by 530 nm light PPG (±0.61 bpm) was smaller than that achieved when using 645 and 470 nm light PPG (±3.20 bpm and ±2.23 bpm, respectively). The ΔSNR (the difference between baseline and task values) of 530 and 470nm light PPG was significantly smaller than ΔSNR for red light PPG. In conclusion, 530 nm light PPG could be a more suitable method than 645 and 470nm light PPG for monitoring HR in normal daily life.

Reflective oxygen saturation monitoring at hypothenar and its validation by human hypoxia experiment

BACKGROUND

Pulse oxygen saturation (SpO2) is an important parameter for healthcare, and wearable sensors and systems for SpO2 monitoring have become increasingly popular. The aim of this paper is to develop a novel SpO2 monitoring system, which detects photoplethysmographic (PPG) signals at hypothenar with a reflection-mode sensor embedded into a glove.

METHODS

A special photo-detector section was designed with two photodiodes arranged symmetrically to the red and infrared light-emitting diodes (LED) to enhance the signal quality. The reflective sensor was placed in a soft silicon substrate sewn in a glove to fit the surface of the hypothenar. To lower the power consumption, the LED driving current was reduced and energy-efficient electronic components were applied. The performance for PPG signal detection and SpO2 monitoring was evaluated by human hypoxia experiments. Accelerometer-based adaptive noise cancellation (ANC) methods applying the least mean squares (LMS) and recursive least squares (RLS) algorithms were studied to suppress motion artifact.

RESULTS

A total of 20 subjects participated in the hypoxia experiment. The degree of comfort for wearing this system was accepted by them. The PPG signals were detected effectively at SpO2 levels from about 100-70%. The experiment validated the accuracy of the system was 2.34%, compared to the invasive measurements. Both the LMS and RLS algorithms improved the performance during motion. The total current consumed by the system was only 8 mA.

CONCLUSIONS

It is feasible to detect PPG signal and monitor SpO2 at the location of hypothenar. This novel system can achieve reliable SpO2 measurements at different SpO2 levels and on different individuals. The system is light-weighted, easy to wear and power-saving. It has the potential to be a solution for wearable monitoring, although more work should be conducted to improve the motion-resistant performance significantly.

Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

Wearable healthcare monitoring systems (WHMSs) have received significant interest from both academia and industry with the advantage of non-intrusive and ambulatory monitoring. The aim of this paper is to investigate the use of an adaptive filter to reduce motion artefact (MA) in physiological signals acquired by WHMSs. In our study, a WHMS is used to acquire ECG, respiration and triaxial accelerometer (ACC) signals during incremental treadmill and cycle ergometry exercises. With these signals, performances of adaptive MA cancellation are evaluated in both respiration and ECG signals. To achieve effective and robust MA cancellation, three axial outputs of the ACC are employed to estimate the MA by a bank of gradient adaptive Laguerre lattice (GALL) filter, and the outputs of the GALL filters are further combined with time-varying weights determined by a Kalman filter. The results show that for the respiratory signals, MA component can be reduced and signal quality can be improved effectively (the power ratio between the MA-corrupted respiratory signal and the adaptive filtered signal was 1.31 in running condition, and the corresponding signal quality was improved from 0.77 to 0.96). Combination of the GALL and Kalman filters can achieve robust MA cancellation without supervised selection of the reference axis from the ACC. For ECG, the MA component can also be reduced by adaptive filtering. The signal quality, however, could not be improved substantially just by the adaptive filter with the ACC outputs as the reference signals.

Remote detection of mental workload changes using cardiac parameters assessed with a low-cost webcam

We introduce a new framework for detecting mental workload changes using video frames obtained from a low-cost webcam. Image processing in addition to a continuous wavelet transform filtering method were developed and applied to remove major artifacts and trends on raw webcam photoplethysmographic signals. The measurements are performed on human faces. To induce stress, we have employed a computerized and interactive Stroop color word test on a set composed by twelve participants. The electrodermal activity of the participants was recorded and compared to the mental workload curve assessed by merging two parameters derived from the pulse rate variability and photoplethysmographic amplitude fluctuations, which reflect peripheral vasoconstriction changes. The results exhibit strong correlation between the two measurement techniques. This study offers further support for the applicability of mental workload detection by remote and low-cost means, providing an alternative to conventional contact techniques.

Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter

Background

The calculation of arterial oxygen saturation (SpO₂) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring.

Methods

A new method combining temporally constrained independent component analysis (cICA) and adaptive filters is presented here to extract the clean PPG signals from the MA corrupted PPG signals with the amplitude information reserved. The underlying PPG signal could be extracted from the MA contaminated PPG signals automatically by using cICA algorithm. Then the amplitude information of the PPG signals could be recovered by using adaptive filters.

Results

Compared with conventional ICA algorithms, the proposed approach is permutation and scale ambiguity-free. Numerical examples with both synthetic datasets and real-world MA corrupted PPG signals demonstrate that the proposed method could remove the MA from MA contaminated PPG signals more effectively than the two existing FFT-LMS and moving average filter (MAF) methods.

Conclusions

This paper presents a new method which combines the cICA algorithm and adaptive filter to extract the underlying PPG signals from the MA contaminated PPG signals with the amplitude information reserved. The new method could be used in the situations where one wants to extract the interested source automatically from the mixed observed signals with the amplitude information reserved. The results of study demonstrated the efficacy of this proposed method.

A PC-aided optical foetal heart rate detection system

Safe monitoring of foetal heart rate is a valuable tool for the healthy evolution and wellbeing of both foetus and mother. This paper presents a non-invasive optical technique that allows for foetal heart rate detection using a photovoltaic infrared (IR) detector placed on the mother's abdomen. The system presented here consists of a photoplethysmography (PPG) circuit, abdomen circuit and a personal computer equipped with MATLAB. A near IR beam having a wavelength of 880 nm is transmitted through the mother's abdomen and foetal tissue. The received abdominal signal that conveys information pertaining to the mother and foetal heart rate is sensed by a low noise photodetector. The PC receives the signal through the National Instrumentation Data Acquisition Card (NIDAQ). After synchronous detection of the abdominal and finger PPG signals, the designed MATLAB-based software saves, analyses and extracts information related to the foetal heart rate. Extraction is carried out using recursive least squares adaptive filtration. Measurements on eight pregnant women with gestational periods ranging from 35-39 weeks were performed using the proposed system and CTG. Results show a correlation coefficient of 0.978 and a correlation confidence interval between 88-99.6%. The t test results in a p value of 0.034, which is less than 0.05. Low power, low cost, high signal-to-noise ratio, reduction of ambient light effect and ease of use are the main characteristics of the proposed system.

Multi-Gaussian fitting for pulse waveform using Weighted Least Squares and multi-criteria decision making method

Analysis of pulse waveform is a low cost, non-invasive method for obtaining vital information related to the conditions of the cardiovascular system. In recent years, different Pulse Decomposition Analysis (PDA) methods have been applied to disclose the pathological mechanisms of the pulse waveform. All these methods decompose single-period pulse waveform into a constant number (such as 3, 4 or 5) of individual waves. Furthermore, those methods do not pay much attention to the estimation error of the key points in the pulse waveform. The estimation of human vascular conditions depends on the key points' positions of pulse wave. In this paper, we propose a Multi-Gaussian (MG) model to fit real pulse waveforms using an adaptive number (4 or 5 in our study) of Gaussian waves. The unknown parameters in the MG model are estimated by the Weighted Least Squares (WLS) method and the optimized weight values corresponding to different sampling points are selected by using the Multi-Criteria Decision Making (MCDM) method. Performance of the MG model and the WLS method has been evaluated by fitting 150 real pulse waveforms of five different types. The resulting Normalized Root Mean Square Error (NRMSE) was less than 2.0% and the estimation accuracy for the key points was satisfactory, demonstrating that our proposed method is effective in compressing, synthesizing and analyzing pulse waveforms.