Photoplethysmograph signal reconstruction based on a novel motion artifact detection-reduction approach. Part II: Motion and noise artifact removal

Reconsider photoplethysmogram signal quality assessment in the free living environment

Objective.Assessing signal quality is crucial for biomedical signal processing, yet a precise mathematical model for defining signal quality is often lacking, posing challenges for experts in labeling signal qualities. The situation is even worse in the free living environment.Approach.We propose to model a PPG signal by the adaptive non-harmonic model (ANHM) and apply a decomposition algorithm to explore its structure, based on which we advocate a reconsideration of the concept of signal quality.Main results.We demonstrate the necessity of this reconsideration and highlight the relationship between signal quality and signal decomposition with examples recorded from the free living environment. We also demonstrate that relying on mean and instantaneous heart rates derived from PPG signals labeled as high quality by experts without proper reconsideration might be problematic.Significance.A new method, distinct from visually inspecting the raw PPG signal to assess its quality, is needed. Our proposed ANHM model, combined with advanced signal processing tools, shows potential for establishing a systematic signal decomposition based signal quality assessment model.

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2-120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38-100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26-1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.

Artifacts classification and apnea events detection in neck photoplethysmography signals

The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites. However, this PPG signal is susceptible to artifacts that critically impair the signal quality. To fully exploit neck PPG for reliable physiological parameters extraction and apneas monitoring, this paper aims to develop two classification algorithms for artifacts and apnea detection. Features from the time, correlogram, and frequency domains were extracted. Two SVM classifiers with RBF kernels were trained for different window (W) lengths and thresholds (Thd) of corruption. For artifacts classification, the maximum performance was attained for the parameters combination of [W = 6s-Thd= 20%], with an average accuracy= 85.84%(ACC), sensitivity= 85.43%(SE) and specificity= 86.26%(SP). For apnea detection, the model [W = 10s-Thd= 50%] maximized all the performance metrics significantly (ACC= 88.25%, SE= 89.03%, SP= 87.42%). The findings of this proof of concept are significant for denoising novel neck PPG signals, and demonstrate, for the first time, that it is possible to promptly detect apnea events from neck PPG signals in an instantaneous manner. This could make a big impact in crucial real-time applications, like devices to prevent sudden-unexpected-death-in-epilepsy (SUDEP).

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

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.

Photoplethysmogram Analysis and Applications: An Integrative Review

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

Time-varying Spectral Index of Electrodermal Activity to Predict Central Nervous System Oxygen Toxicity Symptoms in Divers: Preliminary results

The most effective method to mitigate decompression sickness in divers is hyperbaric oxygen (HBO₂) pre-breathing. However, divers breathing HBO₂ are at risk for developing central nervous system oxygen toxicity (CNS-OT), which can manifest as symptoms that might impair a diver's performance, or cause more serious symptoms like seizures. In this study, we have collected electrodermal activity (EDA) signals in fifteen subjects at elevated oxygen partial pressures (2.06 ATA, 35 FSW) in the "foxtrot" chamber pool at the Duke University Hyperbaric Center, while performing a cognitive stress test for up to 120 minutes. Specifically, we have computed the time-varying spectral analysis of EDA (TVSymp) as a tool for sympathetic tone assessment and evaluated its feasibility for the prediction of symptoms of CNS-OT in divers. The preliminary results show large increase in the amplitude TVSymp values derived from EDA recordings ~2 minutes prior to expert human adjudication of symptoms related to oxygen toxicity. An early detection based on TVSymp might allow the diver to take countermeasures against the dire consequences of CNS-OT which can lead to drowning.Clinical Relevance-This study provides a sensitive analysis method which indicates a significant increase in the electrodermal activity prior to human expert adjudication of symptoms related to CNS-OT.

Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study

BACKGROUND

Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, noninvasive monitoring.

OBJECTIVE

This study aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch.

METHODS

A total of 40 adults presenting to a cardiology clinic wore a smartwatch and Holter monitor and performed scripted movements to simulate activities of daily living (ADLs). Participants' clinical and sociodemographic characteristics were abstracted from medical records. Participants completed a questionnaire assessing different domains of the device's usability. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared with gold-standard Holter monitoring.

RESULTS

The average age of participants was 71 (SD 8) years; most participants had AF risk factors and 23% (9/39) were in AF. About half of the participants owned smartphones, but none owned smartwatches. Participants wore the smartwatch for 42 (SD 14) min while generating motion noise to simulate ADLs. The algorithm determined 53 of the 314 30-second noise-free pulse segments as consistent with AF. Compared with the gold standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively.

CONCLUSIONS

A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable.

State-dependent Gaussian kernel-based power spectrum modification for accurate instantaneous heart rate estimation

Accurate estimation of the instantaneous heart rate (HR) using a reflectance-type photoplethysmography (PPG) sensor is challenging because the dominant frequency observed in the PPG signal corrupted by motion artifacts (MAs) does not usually overlap the true HR, especially during high-intensity exercise. Recent studies have proposed various MA cancellation and HR estimation algorithms that use simultaneously measured acceleration signals as noise references for accurate HR estimation. These algorithms provide accurate results with a mean absolute error (MAE) of approximately 2 beats per minute (bpm). However, some of their results deviate significantly from the true HRs by more than 5 bpm. To overcome this problem, the present study modifies the power spectrum of the PPG signal by emphasizing the power of the frequency corresponding to the true HR. The modified power spectrum is obtained using a Gaussian kernel function and a previous estimate of the instantaneous HR. Because the modification is effective only when the previous estimate is accurate, a recently reported finite state machine framework is used for real-time validation of each instantaneous HR result. The power spectrum of the PPG signal is modified only when the previous estimate is validated. Finally, the proposed algorithm is verified by rigorous comparison of its results with those of existing algorithms using the ISPC dataset (n = 23). Compared to the method without MA cancellation, the proposed algorithm decreases the MAE value significantly from 6.73 bpm to 1.20 bpm (p < 0.001). Furthermore, the resultant MAE value is lower than that obtained by any other state-of-the-art method. Significant reduction (from 10.89 bpm to 2.14 bpm, p < 0.001) is also shown in a separate experiment with 24 subjects.

A Photoplethysmographic Signal Isolated From an Additive Motion Artifact by Frequency Translation

Acquiring a precise percentage of oxygen saturation (SpO2) from a finger-probe pulse oximeter is dependent on both artifact-free red and infrared photoplethysmoghaphic (PPG) signals. Nonetheless, in real-life situations, these PPG signals are corrupted by a motion artifact (MA) signal that is generated from either finger or hand movement. To resolve this MA interference, the cause of the adulteration of PPG signals by the MA signal is examined. The MA signal is found to behave like an additive noise. Additionally, the frequency responses of the MA and PPG signals show that these signals are in the same frequency band. Hence, instead of direct current, a sinusoidal wave alternating current is proposed to drive an LED source in order to shift the PPG frequency band away from the MA frequency band. Experimentally, a commercial finger-probe pulse oximeter is employed. To determine the performance of the presented scheme, the resulting PPG signals are compared with those from employing the old-fashioned LED-driving method. In addition, the accuracy is verified by computing the SpO2 value. The results reveal that the proposed approach successfully retains the fundamental morphologies of the PPG structures when motion occurs. Moreover, the calculated SpO2 values from the proposed technique provide an average error of approximately 1.4%, whereas the conventional method yields a mean error approximately 4.2%.

Evaluation of key design parameters for mitigating motion artefact in the mobile reflectance PPG signal to improve estimation of arterial oxygenation

OBJECTIVE

Pulse oximetry, a widely accepted method for non-invasive estimation of arterial oxygen saturation (SpO₂) and pulse rate (PR), is increasingly being adapted for mobile applications. Previous work in mitigating motion artefact, which corrupts the photoplethysmogram (PPG) used in pulse oximetry, has focused on reducing noise using signal processing algorithms or through sensor design that controlled only one variable at a time. In this work, we have investigated the effect of several variables such as sensor weight, relative motion, placement, and contact force against the skin that can impact motion artefact independently or by interacting with each other.

APPROACH

We have identified a unique combination of these variables that is most optimal in reducing motion artefacts using a full factorial design of experiments methodology and evaluated the effect of these factors on PPG readings with and without motion.

MAIN RESULTS

Data collected on 10 diverse subjects showed that placement (p  =  0.03), contact force (p  =  0.004), and sensor-to-skin adhesion or relative motion when combined with force (p  <  0.001) had the most significant effect on reducing the motion artefact signal. Sensor weight (p  =  0.822) by itself had no significant effect, however when combined with sensor adhesion (p  <  0.001) had a significant impact.

SIGNIFICANCE

This lays the foundation for future development of more robust sensors that can significantly reduce the effect of motion artefacts in reflectance-based pulse oximetry and could have great clinical value due to significant reduction of SpO₂ errors and false alarms associated with motion artefact, making wearable pulse oximetry more reliable in mobile applications.

Estimating SpO2 via Time-Efficient High-Resolution Harmonics Analysis and Maximum Likelihood Tracking

The accuracy of noninvasive oxygen saturation (SpO₂), which is defined by the measurements based on photoplethysmographic (PPG) signals, is intensively affected by motion artifacts (MAs) and low perfusion. This study introduces a novel approach called ESPRIT-MLT to measure SpO₂ when such interferences are present. In contrast to previous studies, the work focuses on the harmonic model of the PPG signal and the probability model of results from harmonic analysis. The optimized parametric ESPRIT method is applied to improve the accuracy of harmonic power estimation, and the maximum likelihood SpO₂ tracking (MLT) technique is proposed to track the most probable uncontaminated harmonic of heart rate frequency. We construct an evaluation platform for testing the proposed method via generated signals and subject tests. Compared with the nonparametric periodogram method, the probability of correct harmonics being found is improved by 18.7% or 19.7%, when the signal is contaminated by motion artifacts or affected by low perfusion, respectively. In comparison with the reference methods, the proposed ESPRIT-MLT method exhibits a lower average root mean square error (RMSE) (1.17%) in the simulation using an MA-contaminated PPG signal, and a lower RMSE (2.70%) in the simulation using an extremely low (0.05%) perfusion index. A comprehensive subject test that consists of 4 activities and 20 subjects shows an average RMSE of 0.84% ( 0.44%). Furthermore, the time-efficiency is optimized to be adaptable with wearable devices. Therefore, the proposed method has potential in enhancing the performance of clinical pulse oximetry and wearable SpO₂ measurement devices for daily use.

OxiMA: A Frequency-Domain Approach to Address Motion Artifacts in Photoplethysmograms for Improved Estimation of Arterial Oxygen Saturation and Pulse Rate

OBJECTIVE

The purpose of this paper is to demonstrate that a new algorithm for estimating arterial oxygen saturation can be effective even with data corrupted by motion artifacts (MAs).

METHODS

OxiMA, an algorithm based on the time-frequency components of a photoplethysmogram (PPG), was evaluated using 22-min datasets recorded from 10 subjects during voluntarily-induced hypoxia, with and without subject-induced MAs. A Nellcor OxiMax transmission sensor was used to collect an analog PPG while reference oxygen saturation and pulse rate (PR) were collected simultaneously from an FDA-approved Masimo SET Radical RDS-1 pulse oximeter.

RESULTS

The performance of our approach was determined by computing the mean relative error between the PR/oxygen saturation estimated by OxiMA and the reference Masimo oximeter. The average estimation error using OxiMA was 3 beats/min for PR and 3.24% for oxygen saturation, respectively.

CONCLUSION

The results show that OxiMA has great potential for improving the accuracy of PR and oxygen saturation estimation during MAs.

SIGNIFICANCE

This is the first study to demonstrate the feasibility of a reconstruction algorithm to improve oxygen saturation estimates on a dataset with MAs and concomitant hypoxia.

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia

Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r²), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care.

Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

So far, many approaches have been developed for motion artifact (MA) reduction from photoplethysmogram (PPG). Specifically, single-input MA reduction methods are useful to apply wearable and mobile healthcare systems because of their low hardware costs and simplicity. However, most of them are insufficiently developed to be used in real-world situations, and they suffer from a phase distortion problem. In this study, we propose a novel single-input MA reduction algorithm based on time-variant forward-backward harmonic notch filtering. To verify the proposed method, we collected real PPG data corrupted by MA and compared it with existing single-input MA reduction methods. In conclusion, the proposed zero-phase line enhancer (ZLE) was found to be superior for MA reduction and exhibited zero phase response.

A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time-Frequency Spectral Features

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach recently developed in our laboratory. The proposed TifMA algorithm consistently provided higher detection rates than the other three methods, with accuracies greater than 95% for all data. Moreover, our algorithm was able to pinpoint the start and end times of the MNA with an error of less than 1 s in duration, whereas the next-best algorithm had a detection error of more than 2.2 s. The final, most challenging, dataset was collected to verify the performance of the algorithm in discriminating between corrupted data that were usable for accurate HR estimations and data that were nonusable. It was found that on average 48% of the data segments were found to have MNA, and of these, 38% could be used to provide reliable HR estimation.

Cardiac signal estimation based on the arterial and venous pressure signals of a hemodialysis machine

Continuous cardiac monitoring is usually not performed during hemodialysis treatment, although a majority of patients with kidney failure suffer from cardiovascular disease. In the present paper, a method is proposed for estimating a cardiac pressure signal by combining the arterial and the venous pressure sensor signals of the hemodialysis machine. The estimation is complicated by the periodic pressure disturbance caused by the peristaltic blood pump, with an amplitude much larger than that of the cardiac pressure signal. Using different techniques for combining the arterial and venous pressure signals, the performance is evaluated and compared to that of an earlier method which made use of the venous pressure only. The heart rate and the heartbeat occurrence times, determined from the estimated cardiac pressure signal, are compared to the corresponding quantities determined from a photoplethysmographic reference signal. Signals from 9 complete hemodialysis treatments were analyzed. For a heartbeat amplitude of 0.5 mmHg, the median absolute deviation between estimated and reference heart rate was 1.3 bpm when using the venous pressure signal only, but dropped to 0.6 bpm when combining the pressure signals. The results show that the proposed method offers superior estimation at low heartbeat amplitudes. Consequently, more patients can be successfully monitored during treatment without the need of extra sensors. The results are preliminary, and need to be verified on a separate dataset.

Improving Pulse Rate Measurements during Random Motion Using a Wearable Multichannel Reflectance Photoplethysmograph

Photoplethysmographic (PPG) waveforms are used to acquire pulse rate (PR) measurements from pulsatile arterial blood volume. PPG waveforms are highly susceptible to motion artifacts (MA), limiting the implementation of PR measurements in mobile physiological monitoring devices. Previous studies have shown that multichannel photoplethysmograms can successfully acquire diverse signal information during simple, repetitive motion, leading to differences in motion tolerance across channels. In this paper, we investigate the performance of a custom-built multichannel forehead-mounted photoplethysmographic sensor under a variety of intense motion artifacts. We introduce an advanced multichannel template-matching algorithm that chooses the channel with the least motion artifact to calculate PR for each time instant. We show that for a wide variety of random motion, channels respond differently to motion artifacts, and the multichannel estimate outperforms single-channel estimates in terms of motion tolerance, signal quality, and PR errors. We have acquired 31 data sets consisting of PPG waveforms corrupted by random motion and show that the accuracy of PR measurements achieved was increased by up to 2.7 bpm when the multichannel-switching algorithm was compared to individual channels. The percentage of PR measurements with error ≤ 5 bpm during motion increased by 18.9% when the multichannel switching algorithm was compared to the mean PR from all channels. Moreover, our algorithm enables automatic selection of the best signal fidelity channel at each time point among the multichannel PPG data.

An Algorithm for Real-Time Pulse Waveform Segmentation and Artifact Detection in Photoplethysmograms

Photoplethysmography has been used in a wide range of medical devices for measuring oxygen saturation, cardiac output, assessing autonomic function, and detecting peripheral vascular disease. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are critically important. However, the published PPG algorithms are limited in algorithm and study design. Therefore, the authors developed a novel embedded algorithm for real-time pulse waveform (PWF) segmentation and artifact detection based on a contour analysis in the time domain. This paper provides an overview about PWF and artifact classifications, presents the developed PWF analysis, and demonstrates the implementation on a 32-bit ARM core microcontroller. The PWF analysis was validated with data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit in equal parts. The output of the algorithm was compared with harmonized experts' annotations of the PPG with a total duration of 31.5 h. The algorithm achieved a beat-to-beat comparison sensitivity of 99.6%, specificity of 90.5%, precision of 98.5%, and accuracy of 98.3%. The interrater agreement expressed as Cohen's kappa coefficient was 0.927 and as F-measure was 0.990. In conclusion, the PWF analysis seems to be a suitable method for PPG signal quality determination, real-time annotation, data compression, and calculation of additional pulse wave metrics such as amplitude, duration, and rise time.

A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson’s correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.