Search Results (28)

Photoplethysmography-derived pulse rate variability (PPG-derived PRV) is increasingly used in wearable and camera-based biomedical sensors as a low-burden alternative to electrocardiography-derived heart rate variability (ECG-derived HRV), but its agreement with ECG-derived measurements remains uncertain. This systematic review and meta-analysis evaluated the accuracy of PPG-derived PRV compared with ECG-derived HRV in healthy or apparently healthy non-clinical populations, focusing on the root mean square of successive differences (RMSSD) and the standard deviation of normal-to-normal intervals (SDNN). Forty-three studies were included in the qualitative synthesis; 33 were summarized narratively, and 10 unique studies provided sufficient data for quantitative synthesis. Eight studies contributed to each RMSSD and SDNN meta-analysis. The pooled absolute standardized error was 0.188 (95% CI: 0.066 to 0.309; I² = 11.69%) for RMSSD and 0.134 (95% CI: 0.014 to 0.255; I² = 0%) for SDNN. Sensitivity analyses supported the robustness of RMSSD findings, whereas SDNN estimates were directionally stable but less robust in statistical significance. Because the quantitative synthesis included only 10 studies and was based predominantly on selected resting or controlled conditions, the pooled estimates should not be generalized to sleep, exercise, stress, or free-living settings or interpreted as evidence of interchangeability. Full article

Polysomnography (PSG) is the gold standard for diagnosing sleep disorders, but its complexity and cost limit widespread use. Heart rate variability (HRV) is traditionally assessed from electrocardiography (ECG), while photoplethysmography (PPG), widely available in wearable devices, offers a more accessible alternative. However, its reliability over full-night recordings remains underexplored. This study analyzes data from 50 subjects across five groups (healthy controls, rapid eye movement sleep behavior disorder, obstructive sleep apnea, periodic limb movements, and mixed comorbidities) to assess agreement between ECG-derived HRV and PPG-derived pulse rate variability (PRV), considering time-, frequency-, and nonlinear-domain features. Correlation and equivalence analyses were performed, with and without removal of artifactual segments. Correlation coefficients exceeded 0.6 for most features and improved to above 0.7 after artifact removal. Consistent improvements were observed across all subject groups. Equivalence testing further identified a subset of features showing high agreement and low bias. The results indicate that, with appropriate pre-processing, PPG can approximate ECG-derived variability in full-night sleep recordings. The identification of robust features for screening purposes supports the use of PRV for wearable-based screening and monitoring in heterogeneous sleep disorder populations. Full article

With the continued rise in outpatient surgical procedures, modern medicine requires more advanced tools for pain and anxiety monitoring and management. The current standard of care requires patient responses on visual analog scales, which may be subjective and are difficult to assess when a subject is unresponsive. Electrodermal activity (EDA) and pulse rate variability (PRV), two non-invasive, wearable, and objective measurements of sympathetic nervous system activity, can help provide insight into a patient’s psychological or emotional state without user input, allowing for continued monitoring even when a patient is unable to respond. However, methods based on these measurements have largely been relegated to longer duration (>60 s) or post hoc analysis, which does not suit the needs of medical care environments. Here we propose new methods for handling ultra-short (<10 s) signals to allow rapid evaluation of pain and anxiety state. We show how machine learning models trained on these signals can obtain high degrees of classification performance (AUC > 0.88) between no pain or anxiety and medium or higher pain and anxiety on signals obtained during two different forms of painful stimulation. We also show how these signals can measure the degree of stimulation irrespective of perceived pain from the patient. Further development of these algorithms will allow for greater monitoring and control of patient comfort in a clinical setting. Full article

Background/Objectives: The aim of this pilot study was to evaluate the feasibility of developing individualized machine learning models using nocturnal wearable-derived autonomic nervous system (ANS) and sleep metrics to predict next-day headache risk in patients with migraine. We also examined the associations between nocturnal ANS and sleep measures and patient-reported outcome measures (PROMs) related to nociplastic pain, migraine burden, and non-restorative sleep (NRS). Methods: Adults with migraine wore the wrist-worn Empatica EmbracePlus^® wearable during sleep and completed daily headache diaries for approximately 4 weeks (N = 10). Participants also completed daily headache diaries and PROMs assessing nociplastic pain, migraine burden, and non-restorative sleep. Personalized machine learning (ML) models were developed to predict next-day headache using nocturnal ANS activity (e.g., pulse rate variability (PRV), electrodermal activity (EDA), respiratory rate (RR)) and sleep metrics (e.g., interruptions, duration, awakenings). Model performance was evaluated using area under the receiver operating characteristic and precision–recall curves (AUROC, AUPRC), sensitivity, specificity, accuracy, and precision. Spearman correlations assessed the relationship between wearable-derived metrics and patient-reported outcome measurements of sleep quality (PROMIS-Fatigue, PROMIS-Sleep Disturbance) and a surrogate marker of nociplastic pain (Fibromyalgia (FM) Score). Results: 9 out of 10 participants wore the EmbracePlus device for at least the target duration of four weeks. For the next-day headache prediction, model performance varied between individuals; area under the ROC curve (AUROC) ranged from 28.2% to 81.2%. Nocturnal measures of EDA were strongly correlated with the FM score (Spearman’s rho = 0.72–0.75, p < 0.05). Conclusions: Phasic EDA may warrant further investigation as a potential physiological indicator related to nociplastic pain mechanisms and next-day headache. However, these findings are preliminary, and larger multicenter trials are needed to confirm results of this pilot study. Full article

Background: Pulse rate variability (PRV), a critical biomarker of autonomic nervous system (ANS) function, is typically evaluated using the pulse-to-pulse interval (PPI) signal extracted from a photoplethysmogram (PPG). Although PPGs have been widely used in wearable devices, the PPI signal is easily affected by motion artifacts or respiratory sinus arrhythmias (RSAs). These disturbances affect the accuracy of PRV for evaluating ANS function. The aim of this study was to remove the respiratory signals from raw PPI signals with an autoregressive moving average (ARMA) model. Methods: An R-wave to R-wave interval (RRI) sequence was extracted from the electrocardiogram (ECG). A self-made measurement system was used to record PPG, ECG, and respiratory signals. Nineteen healthy adults were recruited and requested to breathe with a spontaneous breathing rate (SBR) and control breathing rates (CBRs) (6, 18, and 30 breathing rate per minute, BRPM). Their ECG, PPG, and breathing signals were recorded for 6 min under different CBRs. The measurement was performed twice, i.e., eight measurements were performed. The raw RRI(t) and PPI(t) signals of 4 Hz were segmented into samples of one minute and shifted by 30 s. Thus, a subject had 80 samples, and there were 10 samples for each BRPM. RSA-free RRI signals were generated by a spectral method to filter RSA from raw RRI(t) to produce the target RRI(t). We proposed the individual subject ARMA models trained by samples with the maximum mean absolute errors between the target RRI(t) and raw PPI(t) (MAE_RAWs) of each subject, and the general model trained by samples with all maximum MAE_RAWs of all 19 subjects. Results: The mean absolute errors between the target RRI(t) and $\widetilde{PPI}(t)$ predicted by the individual subject ARMA models (MAE_{Subject-Models}) and general ARMA model (MAE_{General-Model}) were used to evaluate the performance of the two models. The results for the MAE_{Subject-Models} and MAE_{General-Model} were 132.5 ± 59.1 ms and 137.8 ± 67.8 ms, respectively, with no significant difference. MAE_{Subject-Models} and MAE_{General-Model} were compared with MAE_RAWs, whose attenuations (ATTs) were 28.5 ± 13.1% and 27.8 ± 12.6%, respectively. Conclusions: The two proposed models are capable of removing the RSA energy coupled in the raw PPI signals. Full article

We introduce a novel system for automatic assessment of newborn and preterm infant behavior—including activity levels, behavioral states, and sleep–wake cycles—in clinical settings for streamlining care and minimizing healthcare professionals’ workload. While vital signs are routinely monitored, the previously mentioned assessments require labor-intensive direct observation. Research so far has already introduced non- and minimally invasive solutions. However, we developed a system that automatizes the preceding evaluations in a non-contact way using deep learning algorithms. In this work, we provide a Gated Recurrent Unit (GRU)-stack-based solution that works on a dynamic feature set generated by computer vision methods from the cameras’ video feed and patient monitor to classify the activity phases of infants adapted from the NIDCAP (Newborn Individualized Developmental Care Program) scale. We also show how pulse rate variability (PRV) data could improve the performance of the classification. The network was trained and evaluated on our own database of 108 h collected at the Neonatal Intensive Care Unit, Dept. of Neonatology of Pediatrics, Semmelweis University, Budapest, Hungary. Full article

Atrial fibrillation (AF) is a type of cardiac arrhythmia with a worldwide prevalence of more than 37 million among the adult population. This elusive disease is a major risk factor for ischemic stroke, along with increased rates of significant morbidity and eventual mortality. It is clinically diagnosed using medical-grade electrocardiogram (ECG) sensors in ambulatory settings. The recent emergence of consumer-grade wearables equipped with photoplethysmography (PPG) sensors has exhibited considerable promise for non-intrusive continuous monitoring in free-living conditions. However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). We derive and feed heart rate variability (HRV) and pulse rate variability (PRV) features as auxiliary inputs to the framework for robustness. A larger teacher model is trained using the MIT-BIH Arrhythmia ECG dataset. Through transfer learning (TL), its learned representation is adapted to a compressed student model (32x smaller) variant by using knowledge distillation (KD) for classifying AF with the UMass and MIMIC-III datasets of PPG signals. This results in the student model yielding average improvements in accuracy, sensitivity, F1 score, and Matthews correlation coefficient of 2.0%, 15.05%, 11.7%, and 9.85%, respectively, across both PPG datasets. Additionally, we employ Gradient-weighted Class Activation Mapping (Grad-CAM) to confer a notion of interpretability to the model decisions. We conclude that through a combination of techniques such as TL and KD, i.e., pre-trained initialization, we can utilize learned ECG concepts for scarcer PPG scenarios. This can reduce resource usage and enable deployment on edge devices. Full article

This study explored the relationship between cognitive function and the autonomic nervous system by categorizing participants into two groups based on their cognitive function scores in each domain of the SNSB-D: a High Cognitive Performance (HCP) group and a Low Cognitive Performance (LCP) group. We analyzed the Pulse Rate Variability (PRV) parameters for each group. Photoplethysmography (PPG) data were collected and processed to remove noise, and the PRV parameters in the time and frequency domains were extracted. To minimize the impact of age and years of education on the PRV parameters, we performed an adjusted analysis using a Generalized Linear Model (GLM). The analysis revealed that the autonomic nervous system, particularly the parasympathetic nervous system, was more activated in the LCP group compared to the HCP group. This finding suggests that in individuals with low cognitive function, the sympathetic nerves in the autonomic nervous system are less activated, so the parasympathetic nerves are relatively more activated. This study investigated the correlation between cognitive function and PRV parameters, highlighting the potential use of these parameters as indicators for the early diagnosis and classification of cognitive decline. Full article

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO₂), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals’ physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction. Full article

Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20–100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland–Altman plots and coefficient of variation (CoV). A sampling frequency of ~100–200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40–50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1–8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates. Full article

Background and Objectives: To compare autonomic and vascular responses during reactive hyperemia (RH) between healthy individuals and patients with sickle cell anemia (SCA). Materials and Methods: Eighteen healthy subjects and 24 SCA patients were subjected to arterial occlusion for 3 min at the lower right limb level. The pulse rate variability (PRV) and pulse wave amplitude were measured through photoplethysmography using the Angiodin^® PD 3000 device, which was placed on the first finger of the lower right limb 2 min before (Basal) and 2 min after the occlusion. Pulse peak intervals were analyzed using time–frequency (wavelet transform) methods for high-frequency (HF: 0.15–0.4) and low-frequency (LF: 0.04–0.15) bands, and the LF/HF ratio was calculated. Results: The pulse wave amplitude was higher in healthy subjects compared to SCA patients, at both baseline and post-occlusion (p < 0.05). Time–frequency analysis showed that the LF/HF peak in response to the post-occlusion RH test was reached earlier in healthy subjects compared to SCA patients. Conclusions: Vasodilatory function, as measured by PPG, was lower in SCA patients compared to healthy subjects. Moreover, a cardiovascular autonomic imbalance was present in SCA patients with high sympathetic and low parasympathetic activity in the basal state and a poor response of the sympathetic nervous system to RH. Early cardiovascular sympathetic activation (10 s) and vasodilatory function in response to RH were impaired in SCA patients. Full article

Stress is a direct or indirect cause of reduced work efficiency in daily life. It can damage physical and mental health, leading to cardiovascular disease and depression. With increased interest and awareness of the risks of stress in modern society, there is a growing demand for quick assessment and monitoring of stress levels. Traditional ultra-short-term stress measurement classifies stress situations using heart rate variability (HRV) or pulse rate variability (PRV) information extracted from electrocardiogram (ECG) or photoplethysmography (PPG) signals. However, it requires more than one minute, making it difficult to monitor stress status in real-time and accurately predict stress levels. In this paper, stress indices were predicted using PRV indices acquired at different lengths of time (60 s, 50 s, 40 s, 30 s, 20 s, 10 s, and 5 s) for the purpose of real-time stress monitoring. Stress was predicted with Extra Tree Regressor, Random Forest Regressor, and Gradient Boost Regressor models using a valid PRV index for each data acquisition time. The predicted stress index was evaluated using an $R^2$ score between the predicted stress index and the actual stress index calculated from one minute of the PPG signal. The average $R^2$ score of the three models by the data acquisition time was 0.2194 at 5 s, 0.7600 at 10 s, 0.8846 at 20 s, 0.9263 at 30 s, 0.9501 at 40 s, 0.9733 at 50 s, and 0.9909 at 60 s. Thus, when stress was predicted using PPG data acquired for 10 s or more, the $R^2$ score was confirmed to be over 0.7. Full article

Most COVID-19 survivors report experiencing at least one persistent symptom after recovery, including sympathovagal imbalance. Relaxation techniques based on slow-paced breathing have proven to be beneficial for cardiovascular and respiratory dynamics in healthy subjects and patients with various diseases. Therefore, the present study aimed to explore the cardiorespiratory dynamics by linear and nonlinear analysis of photoplethysmographic and respiratory time series on COVID-19 survivors under a psychophysiological assessment that includes slow-paced breathing. We analyzed photoplethysmographic and respiratory signals of 49 COVID-19 survivors to assess breathing rate variability (BRV), pulse rate variability (PRV), and pulse–respiration quotient (PRQ) during a psychophysiological assessment. Additionally, a comorbidity-based analysis was conducted to evaluate group changes. Our results indicate that all BRV indices significantly differed when performing slow-paced breathing. Nonlinear parameters of PRV were more appropriate for identifying changes in breathing patterns than linear indices. Furthermore, the mean and standard deviation of PRQ exhibited a significant increase while sample and fuzzy entropies decreased during diaphragmatic breathing. Thus, our findings suggest that slow-paced breathing may improve the cardiorespiratory dynamics of COVID-19 survivors in the short term by enhancing cardiorespiratory coupling via increased vagal activity. Full article

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer–Ray–Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time. Full article

Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a challenge. In this study, eight image-based photoplethysmography (iPPG) extraction methods (GRD, AGRD, PCA, ICA, LE, SPE, CHROM, and POS) were compared in terms of pulse rate (PR) and PRV features. The algorithms were made robust for motion and illumination artifacts by using ad hoc pre- and postprocessing steps. Then, they were systematically tested on the public dataset UBFC-RPPG, containing data from 42 subjects sitting in front of a webcam (30 fps) while playing a time-sensitive mathematical game. The performances of the algorithms were evaluated by statistically comparing iPPG-based and finger-PPG-based PR and PRV features in terms of Spearman’s correlation coefficient, normalized root mean square error (NRMSE), and Bland–Altman analysis. The study revealed POS and CHROM techniques to be the most robust for PR estimation and the assessment of overall autonomic nervous system (ANS) dynamics by using PRV features in time and frequency domains. Furthermore, we demonstrated that a reliable characterization of the vagal tone is made possible by computing the Poincaré map of PRV series derived from the POS and CHROM methods. This study supports the use of iPPG systems as promising tools to obtain clinically useful and specific information about ANS dynamics. Full article