Wrist PPG Research Articles

A novel 1D generative adversarial network-based framework for atrial fibrillation detection using restored wrist photoplethysmography signals

Atrial fibrillation (AF) poses an increased stroke risk, necessitating effective detection methods. While electrocardiogram (ECG) is conventionally used for AF detection, the simplicity and suitability for long-term monitoring make photoplethysmography (PPG) an attractive alternative. In this study, we present a novel approach for AF detection utilizing smartwatch-based wrist PPG signals. Notably, this is the pioneering use of 1D CycleGAN (Generative Adversarial Network) for reconstructing 1D wrist PPG signals, addressing the challenges posed by poor signal quality due to motion artifacts and limitations in acquisition sites. The proposed method underwent validation on a dataset comprising 21,278 10 s long wrist PPG segments. Two experiments were conducted to evaluate 1D Self-AFNet’s robustness by training on one split and testing on the other. First, the model was trained with Test Split 1 and evaluated on Test Split 2, then vice versa. Our classification model, Self-AFNet, incorporating 1D-CycleGAN restoration, demonstrated accuracy at 96.41 % and 97.09 % for the two splits, respectively. The restored signals exhibited a significant accuracy improvement (2.94 % and 5.08 % for test splits, respectively) compared to unrestored PPG. Additionally, AF detection using ECG signals, paired with matched PPG signals, confirmed the validity of employing reconstructed PPG for classification. Self-AFNet achieved impressive accuracies of 98.07 % and 98.97 %, mirroring the performance of AF detection using reconstructed PPG segments. This study establishes that reconstructed wrist PPG signals from wearable devices offer a reliable means for AF detection, contributing significantly to stroke risk reduction.

EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms

Effective management of diabetes requires accurate monitoring of blood glucose levels. Traditional invasive methods for such monitoring can be cumbersome and uncomfortable for patients. In this study, we introduce a noninvasive approach to estimate blood glucose levels using photoplethysmography (PPG) signals. We have focused on blood glucose prediction using wrist PPG signals and explored various PPG waveform-based features, including AC to DC ratio (AC/DC) and intrinsic mode function (IMF)-based features derived from empirical mode decomposition (EMD). To the best of our knowledge, no studies have been found using EMD-based features to estimate blood glucose levels noninvasively. Additionally, feature importance-based selection has also been used to further improve the accuracy of the proposed model. Among the four machine learning algorithms considered in this study, CatBoost consistently outperformed XGBoost, LightGBM, and random forest across a wide number of features. The best performing model, CatBoost, achieved Pearson’s r of 0.96, MSE 0.08, R2 score 0.92, and MAE 8.01 when considering the top 50 features selected from both PPG waveform-based features and IMF-based features. The p-values for all models were <0.001, indicating statistically significant correlations. Overall, this study provides valuable insights into the feasibility and effectiveness of noninvasive blood glucose monitoring using advanced machine learning techniques.

Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring

Background and MotivationsPhysiological signals, such as the Photoplethysmogram (PPG) collected through wearable devices, consistently encounter significant motion artifacts. Current signal processing techniques, and even state-of-the-art machine learning algorithms, frequently struggle to effectively restore the inherent bodily signals amidst the array of randomly generated distortions. This often leads to the modification or even the degradation of the underlying physiological information. MethodsTo enhance heart rate estimation from wrist PPG (wPPG) signals, this study introduces the Translation Through Restoration GAN (TTR-GAN). TTR-GAN comprises cascaded dual-stage 1D Cycle Generative Adversarial Networks (1D-CycleGANs) constructed using Super-ONNs. In the first phase, corrupted wPPG waveforms are blindly restored using a 1D-CycleGAN-based restoration framework. Subsequently, in the second phase, the restored wPPG waveforms are translated into clean finger PPG (fPPG) signals through a 1D-CycleGAN-based signal-to-signal translation or synthesis framework. Both the restorer and translator GANs undergo independent evaluation using robust temporal, spectral, and clinical metrics. ResultsThe application of the multipass restoration scheme to the wPPG signals resulted in significantly lower entropy compared to the raw wPPGs, indicating reduced irregularity. Using the proposed PRTX metric to evaluate the translational ability of the multichannel translator CycleGAN, we achieved a substantial improvement of 35.88% in wrist-to-finger PPG translation. The correlation between the pulse rate and pulse rate variations estimated from the generated fPPG signals and the heart rate and heart rate variability readings from the ground truth ECG improved by approximately 10.4% and 14.7%, respectively, when compared to the raw wPPG signals. ConclusionThe proposed TTR-GAN can be implemented in wearable devices to obtain reliable real-time cardiovascular data during daily activities.

Machine learning framework for Inter-Beat Interval estimation using wearable Photoplethysmography sensors

Accurate estimation of Heart Rate (HR) is crucial for various health and fitness tracking applications in wearable devices. Among the existing techniques, those based on Photoplethysmography (PPG) signals have been extensively explored for continuous HR monitoring. In this paper, we introduce a new framework that utilizes machine learning methods to estimate Inter-Beat Interval (IBI) from wrist PPG signals. The primary objective of the proposed methods is to enhance the accuracy of IBI estimates while ensuring their feasibility for wearable devices, considering typical computational power, and memory constraints. This is achieved through two combined approaches. The first method involves optimizing a set of parameters associated with the interpolation of the systolic peaks. During the training stage of this method, we optimize the weights applied to the neighboring samples of the systolic peaks while minimizing the error between the fine-tuned peak positions and their corresponding ground truth. The second method is related to a supervised machine learning approach that handles the R-peak detection as a classification task. This allows us to accurately detect systolic peaks in PPG signals by classifying candidate peaks as true or false systolic peaks. To evaluate the effectiveness of our proposed methods, we conducted experiments on a dataset collected and carefully organized to this end. It consists of PPG and Electrocardiogram (ECG) signals obtained from 46 volunteers, including individuals with normal sinus rhythm, atrial fibrillation, and other non-specified cardiac arrhythmias. Comparing our framework with other state-of-the-art methods, we observed excellent performance, with lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) in IBI estimation. The proposed approach also achieved higher levels of Precision and Recall in terms of the detection of systolic peaks.

Heart Rate Estimation from Wrist PPG Signal During Intense Physical Exercise

Heart Rate Estimation from Wrist PPG Signal During Intense Physical Exercise

THE FEASIBILITY OF CUFFLESS, NON-OCCLUSIVE BLOOD PRESSURE ASSESSMENTS USING THE NOVEL LIFELEAF WRISTWATCH AND PLATFORM

Objective: Hypertension affects 1.3 billion people worldwide and is the leading cause of myocardial infarction and stroke. Successful treatment of hypertension continues to be a global challenge, likely given that the most common tool for assessment is the sphygmomanometer, which can be time-consuming and cumbersome to utilize on a consistently compliant basis. More straightforward means of blood pressure (BP) measurement may facilitate earlier detection and closer monitoring of hypertension, thereby helping to alleviate potential comorbidities. The ability to seamlessly collect longitudinal BP data could also enhance ambulatory blood pressure monitoring, which is preferred over in-office BPs for tracking hypertension and decisions regarding pharmacotherapy. BP measurements in the ambulatory setting may also be more representative of true BPs by eliminating white coat syndrome. We therefore investigated the reliability of performing BP measurements via a novel cuffless, non-occlusive photoplethysmography (PPG)-based wristwatch compared to a standard automatic BP cuff reference. Design and method: The LIFELEAF® wristwatch (LifePlus, Inc., San Jose, CA) utilizes novel signal processing and machine learning (ML) algorithms to predict real-time systolic and diastolic blood pressures (SBP and DBP) using features extracted from PPG signal at the wrist. 274 total subjects from 3 centers (Cleveland Clinic Abu Dhabi, UAE; Hospital Sebarang Jaya, Malaysia; and iKure Clinic, India) were enrolled. Subjects were outpatients 18 years of age or older; females who were gravid or actively trying to conceive were excluded. Automatic BP cuff reference measurement on the left arm was immediately followed by 1 minute of wristwatch PPG signal acquisition on that arm. An average of 10.77 paired data acquisitions per subject were collected. Datasets were randomly split for model training (60%) and prediction (40%). Results: There were no significant differences in mean SBP or mean DBP between cuff reference measurements and wristwatch predictions. The wristwatch-predicted SBPs and DBPs also demonstrated significant linear correlation with cuff reference values. Conclusions: Longitudinal SBP and DBP measurements are feasible via non-occlusive methods incorporating sophisticated signal processing and machine learning (ML) applied to wrist PPG. Further algorithmic improvements to accuracy may improve BP/hypertension tracking by facilitating assessments compared to conventional sphygmomanometers.

A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.

Wrist photoplethysmography-based assessment of ectopic burden in hemodialysis patients

Background and Objective:Frequent ectopic beats in patients under hemodialysis may be associated with the occurrence of life-threatening arrhythmias. However, data regarding the prognostic significance of ectopic beats are limited due to the lack of accessible technology for long-term monitoring. This study aims to investigate the feasibility to assess the ectopic burden from continuously acquired wrist photoplethysmogram (PPG). Methods:The performance of two PPG-based ectopic beat detectors operating on the pulse-to-pulse basis was investigated against the reference ectopic beats detected from the electrocardiogram using 41 24-h recordings obtained from hemodialysis patients under uncontrolled conditions. The estimation accuracy of an average daily ectopic burden and the detection performance of high-risk ectopic burden (≥30 beats/h) were also compared to the reference electrocardiogram. Results:The sensitivity and positive predictive value of 68.1% and 59.5%, respectively, were reached by the better-performing PPG-based ectopic beat detector. A PPG-based average daily ectopic burden was estimated with a median error of 3 beats/h. Sensitivity of 82.7% and specificity of 89.8% were achieved when identifying high-risk ectopic burden in 1-h segments with at least 75% of acceptable-quality PPG. Conclusion:The study demonstrates the potential of the wrist PPG to monitor ectopic burden in an outpatient setting and identify patients eligible for early antiarrhythmic treatment.

PPG2EMG: Estimating Upper-Arm Muscle Activities and EMG from Wrist PPG Values.

The electromyogram (EMG) is a waveform representation of the action potential generated by muscle cells using electrodes. EMG acquired using surface electrodes is called surface EMG (sEMG), and it is the acquisition of muscle action potentials transmitted by volume conduction from the skin. Surface electrodes require disposable conductive gel or adhesive tape to be attached to the skin, which is costly to run, and the tape is hard on the skin when it is removed. Muscle activity can be evaluated by acquiring muscle potentials and analyzing quantitative, temporal, and frequency factors. It is also possible to evaluate muscle fatigue because the frequency of the EMG becomes lower as the muscle becomes fatigued. Research on human activity recognition from EMG signals has been actively conducted and applied to systems that support arm and hand functions. This paper proposes a method for recognizing the muscle activity state of the arm using pulse wave data (PPG: Photoplethysmography) and a method for estimating EMG using pulse wave data. This paper assumes that the PPG sensor is worn on the user's wrist to measure the heart rate. The user also attaches an elastic band to the upper arm, and when the user exerts a force on the arm, the muscles of the upper arm contract. The arteries are then constricted, and the pulse wave measured at the wrist becomes weak. From the change in the pulse wave, the muscle activity of the arm can be recognized and the number of action potentials of the muscle can be estimated. From the evaluation experiment with five subjects, three types of muscle activity were recognized with 80+%, and EMG was estimated with approximately 20% error rate.

Human activity recognition based on wrist PPG via the ensemble method

Human activity recognition via Electrocardiography (ECG) and Photoplethysmography (PPG) is extensively researched. While ECG requires less filtering and is less prone to disturbance and artifacts, nonetheless, PPG is cheaper and widely available in smart devices, making it a desired alternative. In this study, we explore the employment of the ensemble method with several pre-trained machine learning models namely Resnet50V2, MobileNetV2, and Xception for the classification of wrist PPG data of human activity, in comparison to its ECG counterpart. The study produced promising results with a test classification accuracy of 88.91% and 94.28% for PPG and ECG, respectively.

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis.

Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.

Evaluation of a wrist-worn photoplethysmography monitor for heart rate variability estimation in patients recovering from laparoscopic colon resection

To evaluate the accuracy of heart rate variability (HRV) parameters obtained with a wrist-worn photoplethysmography (PPG) monitor in patients recovering from minimally invasive colon resection to investigate whether PPG has potential in postoperative patient monitoring. 31 patients were monitored for three days or until discharge or reoperation using a wrist-worn PPG monitor (PulseOn, Finland) with a Holter monitor (Faros 360, Bittium Biosignals, Finland) as a reference measurement device. Beat-to-beat intervals (BBI) and HRV information collected by PPG were compared with RR intervals (RRI) and HRV obtained from the ECG reference after removing artefacts and ectopic beats. The beat-to-beat mean error (ME) and mean absolute error (MAE) of good quality heartbeat intervals obtained by wrist PPG were estimated as − 1.34 ms and 10.4 ms respectively. A significant variation in the accuracy of the HRV parameters was found. In the time domain, SDNN (9.11%), TRI (11.4%) and TINN (11.1%) were estimated with low relative MAE, while RMSSD (34.3%), pNN50 (139%) and NN50 (188%) had higher errors. The logarithmic parameters in the frequency domain (VLF Log, LF Log and HF Log) exhibited the lowest relative error, and for non-linear parameters, SD2 (7.5%), DFA α1 (8.25%) and DFA α2 (4.71%) were calculated much more accurately than SD1 (34.3%). The wrist PPG shows some potential for use in a clinical setting. The accuracy of several HRV parameters analyzed post hoc was found sufficient to be used in further studies concerning postoperative recovery of patients undergoing laparoscopic colon resection, although there were large errors in many common HRV parameters such as RMSSD, pNN50 and NN50, rendering them unusable.ClinicalTrials.gov Identifier: NCT04996511, August 9, 2021, retrospectively registered

Posture-Dependent Variability in Wrist Ballistocardiogram-Photoplethysmogram Pulse Transit Time: Implication to Cuff-Less Blood Pressure Tracking.

Toward the ultimate goal of robust cuff-less blood pressure (BP) tracking with wrist wearables against postural changes, the goal of this work was to investigate posture-dependent variability in pulse transit time (PTT) measured with ballistocardiogram (BCG) and photoplethysmogram (PPG) signal pair at the wrist. BCG and PPG signals were acquired from 25 subjects under the combination of 3 body (standing, sitting, and supine) and 3 arm (vertical in head-to-foot direction, placed on the chest, and holding a shoulder) postures. PTT was computed as the time interval between the BCG J wave and the PPG foot, and the impact of the 9 postures on PTT was analyzed by invoking an array of possible physical mechanisms. Our work suggests that (i) wrist BCG-PPG PTT is consistent under standing and sitting postures with vertically held arms; and (ii) changes in wrist orientation and height as well as restrictions in body and arm movement may alter wrist BCG-PPG PTT via distortions in the wrist BCG and PPG waveforms. The results indicate that wrist BCG-PPG PTT varies with respect to postures even when BP remains constant. The potential of cuff-less BP tracking via wrist BCG-PPG PTT demonstrated under standing posture with arms vertically down in the head-to-foot direction may not generalize to other body and arm postures. Understanding the physical mechanisms responsible for posture-induced BCG-PPG PTT variability may increase the versatility of the wrist BCG for cuff-less BP tracking.

A novel approach for decomposition of biomedical signals in different applications based on data-adaptive Gaussian average filtering

The analysis of biomedical signals plays a crucial role in modern medicine and physiological research. Due to exogenous or endogenous interferences and complex interaction among physiological systems, biomedical signals usually possess nonstationary characteristics. To extract the features buried in such kind of signals, the signal decomposition algorithm that is data-adaptive and stable is highly demanded. This paper introduces a novel decomposition algorithm termed as data-adaptive Gaussian average filtering (DAGAF) that is new and potential in biomedical applications. Six biomedical scenarios, including finger photoplethysmography (PPG) signal with obscure respiratory-induced intensity variation (RIIV) component, wrist PPG signal with apparent RIIV component, seismocardiography (SCG) signal with implicit respiration component, electrocardiogram (ECG) with baseline wander (BW), ECG with power-line interference (PLI), and R-R intervals (RRI) sequence with implicit low-frequency trend wave, are adopted as examples for computer experiments. The results of computer experiments verify that the DAGAF algorithm can satisfy the specified requirement in different biomedical scenarios. DAGAF algorithm possesses the advantages of mathematical formulation and computational efficiency. It can be an alternative choice besides the commonly used empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD).

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

B-PO05-041 DETECTION OF ATRIAL FIBRILLATION USING A WRIST PHOTOPLETHYSMOGRAM A PROSPECTIVE STUDY ON PATIENTS UNDERGOING CATHETER ABLATION OR ELECTRICAL CARDIOVERSION

Atrial fibrillation (AF) is associated with an increased risk of mortality. The electrocardiogram (ECG)-based strategy of screening for AF has some limitations. Photoplethysmography (PPG) is used in AF detection algorithms. The objectives of this study were (1) to investigate whether quantitative analysis of wrist PPG waveforms can clearly distinguish AF from the sinus rhythm and (2) to determine the appropriate data length of the PPG waveforms for feature extraction to optimize the PPG analytics program for AF detection. Continuous waveforms of ECG recorded using an electrophysiology recording system and PPG signals using a wrist-worn smartwatch were simultaneously collected from patients undergoing catheter ablation or electrical cardioversion for AF. The PPG features (temporal, spectral, or morphological) were extracted from 10, 25, 40, or 80 heartbeats of split segments. Machine learning with a support vector machine (SVM) approach was used to detect AF. Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic accuracy. A total of 116 patients were evaluated. We collected and annotated more than 117 hours of PPG waveforms. A total of 6478 and 3957 segments of 25-beat pulse-to-pulse interval (PPI) were annotated as AF and sinus rhythm, respectively. A total of eight features were extracted to distinguish AF. The accuracy of all eight PPG features extracted from the 25 PPI yielded a test area under the receiver operating characteristic curve (AUC) of 0.9676, which was significantly better than the AUC for the 10 PPI (0.9453; P<.001). Quantitative analysis of PPG waveforms can clearly discriminate the signals of AF from those of sinus rhythm. The appropriate data length of the PPG to optimize the PPG analytics program was 25 heartbeats.

Coherence between Decomposed Components of Wrist and Finger PPG Signals by Imputing Missing Features and Resolving Ambiguous Features.

: Feature extraction from photoplethysmography (PPG) signals is an essential step to analyze vascular and hemodynamic information. Different morphologies of PPG waveforms from different measurement sites appear. Various phenomena of missing or ambiguous features exist, which limit subsequent signal processing. : The reasons that cause missing or ambiguous features of finger and wrist PPG pulses are analyzed based on the concept of component waves from pulse decomposition. Then, a systematic approach for missing-feature imputation and ambiguous-feature resolution is proposed. : From the experimental results, with the imputation and ambiguity resolution technique, features from 35,036 (98.7%) of 35,502 finger PPG cycles and 36307 (99.1%) of 36,652 wrist PPG cycles can be successfully identified. The extracted features became more stable and the standard deviations of their distributions were reduced. Furthermore, significant correlations up to 0.92 were shown between the finger and wrist PPG waveforms regarding the positions and widths of the third to fifth component waves. : The proposed missing-feature imputation and ambiguous-feature resolution solve the problems encountered during PPG feature extraction and expand the feature availability for further processing. More intrinsic properties of finger and wrist PPG are revealed. The coherence between the finger and wrist PPG waveforms enhances the applicability of the wrist PPG.

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.

Assessing the Quality of Heart Rate Variability Estimated from Wrist and Finger PPG: A Novel Approach Based on Cross-Mapping Method.

The non-invasiveness of photoplethysmographic (PPG) acquisition systems, together with their cost-effectiveness and easiness of connection with IoT technologies, is opening up to the possibility of their widespread use. For this reason, the study of the reliability of PPG and pulse rate variability (PRV) signal quality has become of great scientific, technological, and commercial interest. In this field, sensor location has been demonstrated to play a crucial role. The goal of this study was to investigate PPG and PRV signal quality acquired from two body locations: finger and wrist. We simultaneously acquired the PPG and electrocardiographic (ECG) signals from sixteen healthy subjects (aged 28.5 ± 3.5, seven females) who followed an experimental protocol of affective stimulation through visual stimuli. Statistical tests demonstrated that PPG signals acquired from the wrist and the finger presented different signal quality indexes (kurtosis and Shannon entropy), with higher values for the wrist-PPG. Then we propose to apply the cross-mapping (CM) approach as a new method to quantify the PRV signal quality. We found that the performance achieved using the two sites was significantly different in all the experimental sessions (p < 0.01), and the PRV dynamics acquired from the finger were the most similar to heart rate variability (HRV) dynamics.

Pulse Transit Time-Pulse Wave Analysis Fusion Based on Wearable Wrist Ballistocardiogram for Cuff-Less Blood Pressure Trend Tracking

The objective of this study was to investigate the efficacy of pulse transit time (PTT)-pulse wave analysis (PWA) fusion in cuff-less blood pressure (BP) trend tracking based on the wearable wrist ballistocardiogram (BCG). We constructed PTT and 36 candidate BCG PWA features based on the BCG and photoplethysmogram (PPG) signals acquired at the wrist. We performed a model-based analysis to select 6 candidate BCG PWA features most sensitive to BP. We aggregated the 6 BCG PWA features into novel predictors of diastolic, systolic, and pulse BP (DP, SP, and PP) orthogonal to PTT by covariance-maximizing dimensionality reduction. Then, we evaluated and compared the efficacy of BCG PTT and BCG PTT-PWA fusion in tracking the trend of DP, SP, and PP. Unique innovations of this study, generalizable to a range of physiological signals beyond the BCG, are (i) the systematic selection of PWA features based on model-based analysis and (ii) the development of PWA-based BP predictors orthogonal to PTT. Using the experimental wrist BCG and PPG signals collected from 23 human subjects, we demonstrated that (i) BCG PTT-PWA fusion may significantly outperform BCG PTT; (ii) PTT may play the primary role in tracking BP while BCG PWA features may still have an independent and complementary value (especially in tracking PP); and (iii) model-based selection of BCG PWA features can enhance the robustness of BP trend tracking based on BCG PTT-PWA fusion.