Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions

Distress detection in VR environment using Empatica E4 wristband and Bittium Faros 360

Introduction

Distress detection in virtual reality systems offers a wealth of opportunities to improve user experiences and enhance therapeutic practices by catering to individual physiological and emotional states.

Methods

This study evaluates the performance of two wearable devices, the Empatica E4 wristband and the Faros 360, in detecting distress in a motion-controlled interactive virtual reality environment. Subjects were exposed to a baseline measurement and two VR scenes, one non-interactive and one interactive, involving problem-solving and distractors. Heart rate measurements from both devices, including mean heart rate, root mean square of successive differences, and subject-specific thresholds, were utilized to explore distress intensity and frequency.

Results

Both the Faros and E4 sensors adequately captured physiological signals, with Faros demonstrating a higher signal-to-noise ratio and consistency. While correlation coefficients were moderately positive between Faros and E4 data, indicating a linear relationship, small mean absolute error and root mean square error values suggested good agreement in measuring heart rate. Analysis of distress occurrence during the interactive scene revealed that both devices detect more high- and medium-level distress occurrences compared to the non-interactive scene.

Discussion

Device-specific factors in distress detection were emphasized due to differences in detected distress events between devices.

A two-branch framework for blood pressure estimation using photoplethysmography signals with deep learning and clinical prior physiological knowledge

Objective.This paper presents a novel dual-branch framework for estimating blood pressure (BP) using photoplethysmography (PPG) signals. The method combines deep learning with clinical prior knowledge and models different time periods (morning, afternoon, and evening) to achieve precise, cuffless BP estimation.Approach.Preprocessed single-channel PPG signals are input into two feature extraction branches. The first branch converts PPG dimensions to 2D and uses pre-trained Mobile Vision Transformer-v2 (MobileViTv2) and Visual Geometry Group19 (Vgg19) backbones to extract deep PPG features based on the different mechanisms of systolic blood pressure (SBP) and diastolic blood pressure (DBP) formation. The second branch calculates multi-dimensional feature parameters based on the relationship between PPG waveforms and factors affecting BP. We fuse the features from both branches and consider diurnal BP variations, using AutoML strategy to construct specific SBP and DBP estimation models for the different periods. The algorithm was developed on the human resting state PPG and BP dataset (HRSD) and validated on the MIMIC-IV dataset for generalization performance.Main results.The mean absolute error (MAE) for BP estimation is 6.42 mmHg SBP and 4.96 mmHg DBP in the morning, 4.84 mmHg (SBP) and 3.73 mmHg (DBP) in the afternoon, and 2.65 mmHg (SBP) and 2.56 mmHg (DBP) in the evening. Performance on the MIMIC-IV database was 4.34 mmHg (SBP) and 3.11 mmHg (DBP). The method meets the standards of the Association for the Advancement of Medical Instrumentation and achieves Grade A of the British Hypertension Society (BHS) standards.Significance. This indicates that it is an accurate and reliable non-invasive BP monitoring technology, applicable for continuous health monitoring and cardiovascular disease prevention.

In situ fabrication of self-filtered near-infrared Ti3C2Tx/n-Si Schottky-barrier photodiodes for a continuous non-invasive photoplethysmographic system

Two-dimensional (2D) MXenes have emerged as promising candidates to serve as Schottky contact electrodes for the development of high-performance photodiodes owing to their extraordinary electronic properties. However, it remains a formidable challenge to fabricate a large-area, uniform MXene layer for practical device application. Here, we develop a facile route to produce a large-area Ti3C2Tx layer by post-etching treatment of a pulsed laser-deposited Ti3AlC2 film, enabling the in situ construction of a back-illuminated Ti3C2Tx/n-Si Schottky-barrier photodiode. Significantly, the device exhibits excellent performance with a distinctive self-filtered near-infrared (NIR) photoresponse behavior in the range of 700-1100 nm. By avoiding disturbances caused by ambient light, the NIR photodiode-based transmission-type photoplethysmographic (PPG) measurement system is capable of more reliable detection of PPG waveforms than the commercial PPG sensors for continuously monitoring heart rate. This enables the accurate extraction of blood pressures using a PPG-only method. Our findings not only pave the way for fabrication of a high-quality large-area 2D MXene layer, but also provide a general design principle for developing high-performance MXene/Si photodiodes for health monitoring systems.

Multiclass arrhythmia classification using multimodal smartwatch photoplethysmography signals collected in real-life settings

In the early stages of atrial fibrillation (AF), most cases are paroxysmal (pAF), making identification only possible with continuous and prolonged monitoring. With the advent of wearables, smartwatches equipped with photoplethysmographic (PPG) sensors are an ideal approach for continuous monitoring of pAF. There have been numerous studies demonstrating successful capture of pAF events, especially using deep learning. However, deep learning requires a large amount of data and independent testing on diverse datasets, to ensure the generalizability of the model, and most prior studies did not meet these requirements. Moreover, most prior studies using wearable-based PPG sensor data collection were limited either to controlled environments, to minimize motion artifacts, or to short duration data collection. Most importantly, frequent premature atrial and ventricular contractions (PAC/PVC) can confound most AF detection algorithms. This has not been well studied, largely due to limited datasets containing these rhythms. Note that the recent deep learning models show 97% AF detection accuracy, and the sensitivity of the current state-of-the-art technique for PAC/PVC detection is only 75% on minimally motion artifact corrupted PPG data. Our study aims to address the above limitations using a recently completed NIH-funded Pulsewatch clinical trial which collected smartwatch PPG data over two weeks from 106 subjects. For our approach, we used multi-modal data which included 1D PPG, accelerometer, and heart rate data. We used a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) deep learning model to detect three classes: normal sinus rhythm, AF, and PAC/PVC. Our proposed 1D-Bi-GRU model's performance was compared with two other deep learning models that have reported some of the highest performance metrics, in prior work. For three-arrhythmia-classification, testing data for all deep learning models consisted of using independent data and subjects from the training data, and further evaluations were performed using two independent datasets that were not part of the training dataset. Our multimodal model achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. Our model was computationally more efficient (14 times more efficient and 2.7 times faster) and outperformed the best state-of-the-art model by 20.81% for PAC/PVC sensitivity and 2.55% for AF accuracy. We also tested our models on two independent PPG datasets collected with a different smartwatch and a fingertip PPG sensor. Our three-arrhythmia-classification results show high macro-averaged area under the receiver operating characteristic curve values of 96.22%, and 94.17% for two independent datasets, demonstrating better generalizability of the proposed model.

Hypothalamic deep brain stimulation augments walking after spinal cord injury

A spinal cord injury (SCI) disrupts the neuronal projections from the brain to the region of the spinal cord that produces walking, leading to various degrees of paralysis. Here, we aimed to identify brain regions that steer the recovery of walking after incomplete SCI and that could be targeted to augment this recovery. To uncover these regions, we constructed a space-time brain-wide atlas of transcriptionally active and spinal cord-projecting neurons underlying the recovery of walking after incomplete SCI. Unexpectedly, interrogation of this atlas nominated the lateral hypothalamus (LH). We demonstrate that glutamatergic neurons located in the LH (LHVglut2) contribute to the recovery of walking after incomplete SCI and that augmenting their activity improves walking. We translated this discovery into a deep brain stimulation therapy of the LH (DBSLH) that immediately augmented walking in mice and rats with SCI and durably increased recovery through the reorganization of residual lumbar-terminating projections from brainstem neurons. A pilot clinical study showed that DBSLH immediately improved walking in two participants with incomplete SCI and, in conjunction with rehabilitation, mediated functional recovery that persisted when DBSLH was turned off. There were no serious adverse events related to DBSLH. These results highlight the potential of targeting specific brain regions to maximize the engagement of spinal cord-projecting neurons in the recovery of neurological functions after SCI. Further trials must establish the safety and efficacy profile of DBSLH, including potential changes in body weight, psychological status, hormonal profiles and autonomic functions.

Assessment of Physiological Signals from Photoplethysmography Sensors Compared to an Electrocardiogram Sensor: A Validation Study in Daily Life

Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This paper systematically compares heart rate (HR) and heart rate variability (HRV) measures from PPG against an electrocardiogram (ECG) monitor in free-living settings. Two devices with PPG and one device with an ECG sensor were worn by 25 healthy volunteers for 10 days. PPG-derived HR and HRV showed reasonable accuracy and reliability, particularly during sleep, with mean absolute error < 1 beat for HR and 6-15 ms for HRV. The relative error of HRV estimated from PPG varied with activity type and was higher than during the resting state by 14-51%. The accuracy of HR/HRV was impacted by the proportion of usable data, body posture, and epoch length. The multi-scale peak and trough detection algorithm demonstrated superior performance in detecting beats from PPG signals, with an F1 score of 89% during sleep. The study demonstrates the trade-offs of utilizing PPG measurements for remote monitoring in daily life and identifies optimal use conditions by recommending enhancements.

BP-diff: a conditional diffusion model for cuffless continuous BP waveform estimation using U-Net

Objective.Continuous monitoring of blood pressure (BP) is crucial for daily healthcare. Although invasive methods provide accurate continuous BP measurements, they are not suitable for routine use. Photoplethysmography (PPG), a non-invasive technique that detects changes in blood volume within the microcirculation using light, shows promise for BP measurement. The primary goal of this study is to develop a novel cuffless method based on PPG for accurately estimating continuous BP.Approach.We introduce BP-Diff, an end-to-end method for cuffless continuous BP waveform estimation utilizing a conditional diffusion probability model combined with a U-Net architecture. This approach takes advantage of the stochastic properties of diffusion models and the strong feature representation capabilities of U-Net. It integrates the continuous BP waveform as the initial status and uses the PPG signal and its derivatives as conditions to guide the training and sampling process.Main results.BP-Diff was evaluated using both uncalibrated and calibrated schemes. The results indicate that, when uncalibrated, BP-Diff can accurately track BP dynamics, including peak and valley positions, as well as timing. After calibration, BP-Diff achieved highly accurate BP estimations. The mean absolute error of the estimated BP waveforms, along with the systolic BP, diastolic BP, and mean arterial pressure from the calibrated BP-Diff model, were 2.99 mmHg, 2.6 mmHg, 1.4 mmHg, and 1.44 mmHg, respectively. Consistency tests, including Bland-Altman analysis and Pearson correlation, confirmed its high reliability compared to reference BP. BP-Diff meets the American Association for Medical Instrumentation standards and has achieved a Grade A from the British Hypertension Society.Significance.This study utilizes PPG signals to develop a novel cuffless continuous BP measurement method, demonstrating superiority over existing approaches. The method is suitable for integration into wearable devices, providing a practical solution for continuous BP monitoring in everyday healthcare.

Effect of urban environment on cardiovascular health: a feasibility pilot study using machine learning to predict heart rate variability in patients with heart failure

Aims

Urbanization is related to non-communicable diseases such as congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system responses to environmental attributes in uncontrolled real-world settings. The goal is to validate whether this approach can ascertain and quantify the connection between environmental attributes and cardiac autonomic response in patients with CHF.

Methods and results

A total of 20 participants (10 healthy individuals and 10 patients with CHF) wore smartwatches for 3 weeks, recording activities, locations, and heart rate (HR). Environmental attributes were extracted from Google Street View images. Machine learning models were trained and tested on the data to predict HRV metrics. The models were evaluated using Spearman's correlation, root mean square error, prediction intervals, and Bland-Altman analysis. Machine learning models predicted HRV metrics related to vagal activity well (R > 0.8 for HR; 0.8 > R > 0.5 for the root mean square of successive interbeat interval differences and the Poincaré plot standard deviation perpendicular to the line of identity; 0.5 > R > 0.4 for the high frequency power and the ratio of the absolute low- and high frequency power induced by environmental attributes. However, they struggled with metrics related to overall autonomic activity, due to the complex balance between sympathetic and parasympathetic modulation.

Conclusion

This study highlights the potential of ML-based models to discern vagal dynamics influenced by living environments in healthy individuals and patients diagnosed with CHF. Ultimately, this strategy could offer rehabilitation and tailored lifestyle advice, leading to improved prognosis and enhanced overall patient well-being in CHF.

Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, passive, and continuous monitoring of moods and NPS, overcoming limitations and inconveniences of current assessment methods. In this longitudinal study, we examined the predictive ability of digital biomarkers based on sensor data from a wrist-worn wearable to determine the severity of NPS and mood disorders on a daily basis in older adults with predominant MCI. In addition to conventional physiological biomarkers, such as heart rate variability and skin conductance levels, we leveraged deep-learning features derived from physiological data using a self-supervised convolutional autoencoder. Models combining common digital biomarkers and deep features predicted depression severity scores with a correlation of r = 0.73 on average, total severity of mood disorder symptoms with r = 0.67, and mild behavioral impairment scores with r = 0.69 in the study population. Our findings demonstrated the potential of physiological biomarkers collected from wearables and deep learning methods to be used for the continuous and unobtrusive assessments of mental health symptoms in older adults, including those with MCI. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov (NCT05059353) on September 28, 2021, titled "Effectiveness and Safety of a Digitally Based Multidomain Intervention for Mild Cognitive Impairment".

Widespread Autonomic Physiological Coupling Across the Brain-Body Axis

The brain is closely attuned to visceral signals from the body's internal environment, as evidenced by the numerous associations between neural, hemodynamic, and peripheral physiological signals. We show that these brain-body co-fluctuations can be captured by a single spatiotemporal pattern. Across several independent samples, as well as single-echo and multi-echo fMRI data acquisition sequences, we identify widespread co-fluctuations in the low-frequency range (0.01 - 0.1 Hz) between resting-state global fMRI signals, neural activity, and a host of autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems. The same brain-body co-fluctuations observed at rest are elicited by arousal induced by cued deep breathing and intermittent sensory stimuli, as well as spontaneous phasic EEG events during sleep. Further, we show that the spatial structure of global fMRI signals is maintained under experimental suppression of end-tidal carbon dioxide (PETCO2) variations, suggesting that respiratory-driven fluctuations in arterial CO2 accompanying arousal cannot explain the origin of these signals in the brain. These findings establish the global fMRI signal as a significant component of the arousal response governed by the autonomic nervous system.

Heart rate response to cognitive load as a marker of depression and increased anxiety

Introduction

Understanding the interplay between cardiovascular parameters, cognitive stress induced by increasing load, and mental well-being is vital for the development of integrated health strategies today. By monitoring physiological signals like electrocardiogram (ECG) and photoplethysmogram (PPG) in real time, researchers can discover how cognitive tasks influence both cardiovascular and mental health. Cardiac biomarkers resulting from cognitive strain act as indicators of autonomic nervous system function, potentially reflecting conditions related to heart and mental health, including depression and anxiety. The purpose of this study is to investigate how cognitive load affects ECG and PPG measurements and whether these can signal early cardiovascular changes during depression and anxiety disorders.

Methods

Ninety participants aged 18 to 45 years, ranging from symptom-free individuals to those with diverse psychological conditions, were assessed using psychological questionnaires and anamnesis. ECG and PPG monitoring were conducted as volunteers engaged in a cognitive 1-back task consisting of two separate blocks, each with six progressively challenging levels. The participants' responses were analyzed to correlate physiological and psychological data with cognitive stressors and outcomes.

Results

The study confirmed a notable interdependence between anxiety and depression, and cardiovascular responses. Task accuracy decreased with increased task difficulty. A strong relationship between PPG-measured heart rate and markers of depression and trait anxiety was observed. Increasing task difficulty corresponded to an increase in heart rate, linked with elevated levels of depression and trait anxiety. A strong relationship between ECG-measured heart rate and anxiety attacks was observed. Increasing task difficulty corresponded to an increase in heart rate, linked with elevated levels of anxiety attacks, although this association decreased under more challenging conditions.

Discussion

The findings underscore the predictive importance of ECG and PPG heart rate parameters in mental health assessment, particularly depression and anxiety under cognitive stress induced by increasing load. We discuss mechanisms of sympathetic activation explaining these differences. Our research outcomes have implications for clinical assessments and wearable device algorithms for more precise, personalized mental health diagnostics.

Advantages of Modeling Photoplethysmography (PPG) Signals using Variational Autoencoders

Photoplethysmography (PPG) signal analysis is an emerging field of research and has been applied to a variety of tasks like disease detection and blood pressure monitoring, and the signal waveforms, when properly deciphered, continue to expose increasing levels of detail about the physiology of a person. Variational Autoencoders (VAE) is a fundamental deep learning technique that is within the category of generative models in artificial intelligence. The transformative nature of VAEs enable a powerful approach of processing and interpreting PPG signals. In this paper, we propose a VAE model for PPG heart beats, called PPG-VAE (~1.67% MAE), and discuss its advantages and sample applications. We show how this model can help identify localized slope of a PPG Heart Beat (HB) Wave or pulse, remove localized high frequency noise, and generate new signal segments matching existing signal segment morphology. Finally, we conclude by describing how the model can be used for the compression of PPG signal data, as well as how the model's utility for signal synthesis could be extended to provide granular control over the features of the created signal.

A Guide to Measuring Heart and Respiratory Rates Based on Off-the-Shelf Photoplethysmographic Hardware and Open-Source Software

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates' determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman's coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman's coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables.

Ultrahigh Field fMRI Reveals Different Roles of the Temporal and Frontoparietal Cortices in Subjective Awareness

A central question in consciousness theories is whether one is dealing with a dichotomous ("all-or-none") or a gradual phenomenon. In this 7T fMRI study, we investigated whether dichotomy or gradualness in fact depends on the brain region associated with perceptual awareness reports. Both male and female human subjects performed an emotion discrimination task (fear vs neutral bodies) presented under continuous flash suppression with trial-based perceptual awareness measures. Behaviorally, recognition sensitivity increased linearly with increased stimuli awareness and was at chance level during perceptual unawareness. Physiologically, threat stimuli triggered a slower heart rate than neutral ones during "almost clear" stimulus experience, indicating freezing behavior. Brain results showed that activity in the occipitotemporal, parietal, and frontal regions as well as in the amygdala increased with increased stimulus awareness while early visual areas showed the opposite pattern. The relationship between temporal area activity and perceptual awareness best fitted a gradual model while the activity in frontoparietal areas fitted a dichotomous model. Furthermore, our findings illustrate that specific experimental decisions, such as stimulus type or the approach used to evaluate awareness, play pivotal roles in consciousness studies and warrant careful consideration.

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

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

Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network

ECG helps in diagnosing heart disease by recording heart activity. During long-term measurements, data loss occurs due to sensor detachment. Therefore, research into the reconstruction of missing ECG data is essential. However, ECG requires user participation and cannot be used for continuous heart monitoring. Continuous monitoring of PPG signals is conversely low-cost and easy to carry out. In this study, a deep neural network model is proposed for the reconstruction of missing ECG signals using PPG data. This model is an end-to-end deep learning neural network utilizing WNet architecture as a basis, on which a bidirectional long short-term memory network is added in establishing a second model. The performance of both models is verified using 146 records from the MIMIC III matched subset. Compared with the reference, the ECG reconstructed using the proposed model has a Pearson's correlation coefficient of 0.851, root mean square error (RMSE) of 0.075, percentage root mean square difference (PRD) of 5.452, and a Fréchet distance (FD) of 0.302. The experimental results demonstrate that it is feasible to reconstruct missing ECG signals from PPG.

The influence of cardiac arrhythmias on the detection of heartbeats in the photoplethysmogram: benchmarking open-source algorithms

Objective.Cardiac arrhythmias are a leading cause of mortality worldwide. Wearable devices based on photoplethysmography give the opportunity to screen large populations, hence allowing for an earlier detection of pathological rhythms that might reduce the risks of complications and medical costs. While most of beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, the performance of these algorithms in patients with other cardiac arrhythmias, such as ventricular tachycardia or bigeminy, remain unknown to date.Approach. ThePPG-beatsopen-source framework, developed by Charlton and colleagues, evaluates the performance of the beat detectors namedQPPG,MSPTDandABDamong others. We applied thePPG-beatsframework on two newly acquired datasets, one containing seven different types of cardiac arrhythmia in hospital settings, and another dataset including two cardiac arrhythmias in ambulatory settings.Main Results. In a clinical setting, theQPPGbeat detector performed best on atrial fibrillation (with a medianF1score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked 2nd for bigeminy (75.7%) behindABDdetector (76.1%). In an ambulatory setting, theMSPTDbeat detector performed best on normal sinus rhythm (94.6%), and theQPPGdetector on atrial fibrillation (91.6%) and bigeminy (80.0%).Significance. Overall, the PPG beat detectorsQPPG,MSPTDandABDconsistently achieved higher performances than other detectors. However, the detection of beats from wrist-PPG signals is compromised in presence of bigeminy or ventricular tachycardia.

Blood Pressure Estimation Based on PPG and ECG Signals Using Knowledge Distillation

OBJECTIVE

Easy access bio-signals are useful for alleviating the shortcomings and difficulties associated with cuff-based and invasive blood pressure (BP) measurement techniques. This study proposes a deep learning model, trained using knowledge distillation, based on photoplethysmographic (PPG) and electrocardiogram (ECG) signals to estimate systolic and diastolic blood pressures.

METHODS

The estimation model comprises convolutional layers followed by one bidirectional recurrent layer and attention layers. The training approach involves knowledge distillation, where a smaller model (student model) is trained by leveraging information from a larger model (teacher model).

RESULTS

The proposed multistage model was evaluated on 1205 subjects from Medical Information Mart for Intensive Care (MIMIC) III database using the Association for the Advancement of Medical Instrumentation (AAMI) and the standards of the British Hypertension Society (BHS). The results revealed that our model performance achieved grade A in estimating both systolic blood pressure (SBP) and diastolic blood pressure (DBP) and met the requirements of the AAMI standard. After training with knowledge distillation (KD), the model achieved a mean absolute error and standard deviation of 2.94 ± 5.61 mmHg for SBP and 2.02 ± 3.60 mmHg for DBP.

CONCLUSION

Our results demonstrate the benefits of the knowledge distillation training method in reducing the number of parameters and improving the predictive accuracy of the blood pressure regression model.

Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment

BACKGROUND

Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI.

METHODS

We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation.

RESULTS

The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61.

CONCLUSIONS

Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.

Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography

Photoplethysmography (PPG) is used for heart-rate monitoring in a variety of contexts and applications due to its versatility and simplicity. These applications, namely studies involving PPG data acquisition during day-to-day activities, require reliable and continuous measurements, which are often performed at the index finger or wrist. However, some PPG sensors are susceptible to saturation, motion artifacts, and discomfort upon their use. In this paper, an off-the-shelf PPG sensor was benchmarked and modified to improve signal saturation. Moreover, this paper explores the feasibility of using an optimized sensor in the lower limb as an alternative measurement site. Data were collected from 28 subjects with ages ranging from 18 to 59 years. To validate the sensors' performance, signal saturation and quality, wave morphology, performance of automatic systolic peak detection, and heart-rate estimation, were compared. For the upper and lower limb locations, the index finger and the first toe were used as reference locations, respectively. Lowering the amplification stage of the PPG sensor resulted in a significant reduction in signal saturation, from 18% to 0.5%. Systolic peak detection at rest using an automatic algorithm showed a sensitivity and precision of 0.99 each. The posterior wrist and upper arm showed pulse wave morphology correlations of 0.93 and 0.92, respectively. For these locations, peak detection sensitivity and precision were 0.95, 0.94 and 0.89, 0.89, respectively. Overall, the adjusted PPG sensors are a good alternative for obtaining high-quality signals at the fingertips, and for new measurement sites, the posterior pulse and the upper arm allow for high-quality signal extraction.

Photoplethysmography-based cuffless blood pressure estimation: an image encoding and fusion approach

Objective.Photoplethysmography (PPG) is a promising wearable technology that detects volumetric changes in microcirculation using a light source and a sensor on the skin's surface. PPG has been shown to be useful for non-invasive blood pressure (BP) measurement. Deep learning-based BP measurements are now gaining popularity. However, almost all methods focus on 1D PPG. We aimed to design an end-to-end approach for estimating BP using image encodings from a 2D perspective.Approach.In this paper, we present a BP estimation approach based on an image encoding and fusion (BP-IEF) technique. We convert the PPG into five image encodings and use them as input. The proposed BP-IEF consists of two parts: an encoder and a decoder. In addition, three kinds of well-known neural networks are taken as the fundamental architecture of the encoder. The decoder is a hybrid architecture that consists of convolutional and fully connected layers, which are used to fuse features from the encoder.Main results.The performance of the proposed BP-IEF is evaluated on the UCI database in both non-mixed and mixed manners. On the non-mixed dataset, the root mean square error and mean absolute error for systolic BP (SBP) are 13.031 mmHg and 9.187 mmHg respectively, while for diastolic BP (DBP) they are 5.049 mmHg and 3.810 mmHg. On the mixed dataset, the corresponding values for SBP are 4.623 mmHg and 3.058 mmHg, while for DBP the values are 2.350 mmHg and 1.608 mmHg. In addition, both SBP and DBP estimation on the mixed dataset achieved grade A compared to the British Hypertension Society standard. The DBP estimation on the non-mixed dataset also achieved grade A.Significance.The results indicate that the proposed approach has the potential to improve on the current mobile healthcare for cuffless BP measurement.

The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.

Understanding Privacy Risks versus Predictive Benefits in Wearable Sensor-Based Digital Phenotyping: A Quantitative Cost-Benefit Analysis

Wearable devices with embedded sensors can provide personalized healthcare and wellness benefits in digital phenotyping and adaptive interventions. However, the collection, storage, and transmission of biometric data (including processed features rather than raw signals) from these devices pose significant privacy concerns. This quantitative, data-driven study examines the privacy risks associated with wearable-based digital phenotyping practices, with a focus on user reidentification (ReID), which is the process of identifying participants' IDs from deidentified digital phenotyping datasets. We propose a machine-learning-based computational pipeline to evaluate and quantify model outcomes under various configurations, such as modality inclusion, window length, and feature type and format, to investigate the factors influencing ReID risks and their predictive trade-offs. This pipeline leverages features extracted from three wearable sensors, resulting in up to 68.43% accuracy in ReID risk for a sample size of N=45 socially anxious participants based on only descriptive features of 10-second observations. Additionally, we explore the trade-offs between privacy risks and predictive benefits by adjusting various settings (e.g., the ways to process extracted features). Our findings highlight the importance of privacy in digital phenotyping and suggest potential future directions.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks. We posit that passively detecting social contexts can be particularly useful for social anxiety research, as it may ultimately help identify changes in social anxiety status and patterns of social avoidance and withdrawal. To this end, we recruited a sample of highly socially anxious undergraduate students (N=46) to examine whether we could detect the presence of experimentally manipulated virtual social contexts via wristband sensors. Using a multitask machine learning pipeline, we leveraged passively sensed biobehavioral streams to detect contexts relevant to social anxiety, including (1) whether people were in a social situation, (2) size of the social group, (3) degree of social evaluation, and (4) phase of social situation (anticipating, actively experiencing, or had just participated in an experience). Results demonstrated the feasibility of detecting most virtual social contexts, with stronger predictive accuracy when detecting whether individuals were in a social situation or not and the phase of the situation, and weaker predictive accuracy when detecting the level of social evaluation. They also indicated that sensing streams are differentially important to prediction based on the context being predicted. Our findings also provide useful information regarding design elements relevant to passive context detection, including optimal sensing duration, the utility of different sensing modalities, and the need for personalization. We discuss implications of these findings for future work on context detection (e.g., just-in-time adaptive intervention development).

Physiological Signals and Affect as Predictors of Advertising Engagement

This study investigated the use of affect and physiological signals of heart rate, electrodermal activity, pupil dilation, and skin temperature to classify advertising engagement. The ground truth for the affective and behavioral aspects of ad engagement was collected from 53 young adults using the User Engagement Scale. Three gradient-boosting classifiers, LightGBM (LGBM), HistGradientBoostingClassifier (HGBC), and XGBoost (XGB), were used along with signal fusion to evaluate the performance of different signal combinations as predictors of engagement. The classifiers trained on the fusion of skin temperature, valence, and tiredness (features n = 5) performed better than those trained on all signals (features n = 30). The average AUC ROC scores for the fusion set were XGB = 0.68 (0.10), LGBM = 0.69 (0.07), and HGBC = 0.70 (0.11), compared to the lower scores for the set of all signals (XGB = 0.65 (0.11), LGBM = 0.66 (0.11), HGBC = 0.64 (0.10)). The results also show that the signal fusion set based on skin temperature outperforms the fusion sets of the other three signals. The main finding of this study is the role of specific physiological signals and how their fusion aids in more effective modeling of ad engagement while reducing the number of features.

Non-invasive arterial blood pressure measurement and SpO2 estimation using PPG signal: a deep learning framework

BACKGROUND

Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.

METHOD

Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation.

RESULTS

The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard.

CONCLUSIONS

The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

The Effect of Lifting-and-Thrusting Laser Acupuncture on Electrodermal Activity of Acupoints, Pulse Characteristics, and Brainwave

Acupuncture has been shown as an effective traditional Chinese medicine treatment method, especially for pain relief. Recently, laser acupuncture is becoming increasingly popular, thanks to its noninvasive and painless nature and effectiveness in treating diseases, proven by many studies (for example, some previous studies showed that low-power laser stimulation is able to increase the power of alpha rhythms and theta waves). In our prior work, we developed a novel laser acupuncture model that emulates lifting-and-thrusting operation commonly used in traditional needle acupuncture and showed its benefit in improving cardiac output and peripheral circulation. By extending our previous studies, in this work, we perform extensive experiments to understand the effect of such a system on electrodermal activity (EDA) of acupoints, pulse characteristics, and brainwave, to further verify its efficacy. In particular, we found that laser stimulation could cause significant changes in EDA of acupoints, pulse amplitude, pulse-rate-variability (PRV), and acupoint conductance, as a function of laser power and stimulation time. In addition, laser acupuncture with the lifting-and-thrusting operation has more significant effect on increasing the power of alpha and theta frequency bands as compared to laser acupuncture without the lifting-and-thrusting operation. Finally, given sufficient stimulation time (e.g., > 20 min), the performance of a low-powered laser acupuncture with the lifting-and-thrusting operation could be comparable to that of traditional needle acupuncture.

A Multi-Parameter Fusion Method for Cuffless Continuous Blood Pressure Estimation Based on Electrocardiogram and Photoplethysmogram

Blood pressure (BP) is an essential physiological indicator to identify and determine health status. Compared with the isolated BP measurement conducted by traditional cuff approaches, cuffless BP monitoring can reflect the dynamic changes in BP values and is more helpful to evaluate the effectiveness of BP control. In this paper, we designed a wearable device for continuous physiological signal acquisition. Based on the collected electrocardiogram (ECG) and photoplethysmogram (PPG), we proposed a multi-parameter fusion method for noninvasive BP estimation. An amount of 25 features were extracted from processed waveforms and Gaussian copula mutual information (MI) was introduced to reduce feature redundancy. After feature selection, random forest (RF) was trained to realize systolic BP (SBP) and diastolic BP (DBP) estimation. Moreover, we used the records in public MIMIC-III as the training set and private data as the testing set to avoid data leakage. The mean absolute error (MAE) and standard deviation (STD) for SBP and DBP were reduced from 9.12 ± 9.83 mmHg and 8.31 ± 9.23 mmHg to 7.93 ± 9.12 mmHg and 7.63 ± 8.61 mmHg by feature selection. After calibration, the MAE was further reduced to 5.21 mmHg and 4.15 mmHg. The result showed that MI has great potential in feature selection during BP prediction and the proposed multi-parameter fusion method can be used for long-term BP monitoring.

Gut-Brain Coupling and Multilevel Physiological Response to Biofeedback Relaxation After a Stressful Task Under Virtual Reality Immersion: A Pilot Study

Human physiological reactions to the environment are coordinated by the interactions between brain and viscera. In particular, the brain, heart, and gastrointestinal tract coordinate with each other to provide physiological equilibrium by involving the central, autonomic, and enteric nervous systems. Recent studies have demonstrated an electrophysiological coupling between the gastrointestinal tract and the brain (gut-brain axis) under resting-state conditions. As the gut-brain axis plays a key role in individual stress regulation, we aimed to examine modulation of gut-brain coupling through the use of an overwhelming and a relaxing module as a first step toward modeling of the underlying mechanisms. This study was performed in 12 participants who, under a virtual reality environment, performed a 9-min cognitive stressful task followed by a 9-min period of relaxation. Brain activity was captured by electroencephalography, autonomic activities by photoplethysmography, and electrodermal and gastric activities by electrogastrography. Results showed that compared with the stressful task, relaxation induced a significant decrease in both tonic and phasic sympathetic activity, with an increase in brain alpha power and a decrease in delta power. The intensity of gut-brain coupling, as assessed by the modulation index of the phase-amplitude coupling between the normogastric slow waves and the brain alpha waves, decreased under the relaxation relative to the stress condition. These results highlight the modulatory effect of biofeedback relaxation on gut-brain coupling and suggest noninvasive multilevel electrophysiology as a promising way to investigate the mechanisms underlying gut-brain coupling in physiological and pathological situations.

PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods

There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents "PulseDB," the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality 10 -s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects' identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals, making PulseDB directly usable for training deep learning models with minimal data pre-processing. Additionally, using this dataset, we conduct the first study to provide insights about the performance gap between calibration-based and calibration-free testing approaches for evaluating generalizability of the BP estimation models. We expect PulseDB, as a user-friendly, large, comprehensive and multi-functional dataset, to be used as a reliable source for the evaluation of cuff-less BP estimation methods.

Contactless Cardiovascular Assessment by Imaging Photoplethysmography: A Comparison with Wearable Monitoring

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.

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor

Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to 0.95 were obtained for the SCL component, and a mean consensus of 69% for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of 99.46% and 87.85% were obtained, respectively. Moreover, an HR estimation error of 17.40±14.80 Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring.

A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability

BACKGROUND

Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions.

OBJECTIVE

We evaluate the accuracy of PPG signals-collected by the Samsung Gear Sport smartwatch in free-living conditions-in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor.

METHODS

We conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods.

RESULTS

We found a significantly high positive correlation between the smartwatch's and Shimmer ECG's HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch's and shimmer ECG's LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances.

CONCLUSION

The Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.

Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets

Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.

vital_sqi: A Python package for physiological signal quality control

Electrocardiogram (ECG) and photoplethysmogram (PPG) are commonly used to determine the vital signs of heart rate, respiratory rate, and oxygen saturation in patient monitoring. In addition to simple observation of those summarized indexes, waveform signals can be analyzed to provide deeper insights into disease pathophysiology and support clinical decisions. Such data, generated from continuous patient monitoring from both conventional bedside and low-cost wearable monitors, are increasingly accessible. However, the recorded waveforms suffer from considerable noise and artifacts and, hence, are not necessarily used prior to certain quality control (QC) measures, especially by those with limited programming experience. Various signal quality indices (SQIs) have been proposed to indicate signal quality. To facilitate and harmonize a wider usage of SQIs in practice, we present a Python package, named vital_sqi, which provides a unified interface to the state-of-the-art SQIs for ECG and PPG signals. The vital_sqi package provides with seven different peak detectors and access to more than 70 SQIs by using different settings. The vital_sqi package is designed with pipelines and graphical user interfaces to enable users of various programming fluency to use the package. Multiple SQI extraction pipelines can take the PPG and ECG waveforms and generate a bespoke SQI table. As these SQI scores represent the signal features, they can be input in any quality classifier. The package provides functions to build simple rule-based decision systems for signal segment quality classification using user-defined SQI thresholds. An experiment with a carefully annotated PPG dataset suggests thresholds for relevant PPG SQIs.

Classifying sepsis from photoplethysmography

Sepsis is a life-threatening organ dysfunction. It is caused by a dysregulated immune response to an infection and is one of the leading causes of death in the intensive care unit (ICU). Early detection and treatment of sepsis can increase the survival rate of patients. The use of devices such as the photoplethysmograph could allow the early evaluation in addition to continuous monitoring of septic patients. The aim of this study was to verify the possibility of detecting sepsis in patients from whom the photoplethysmographic signal was acquired via a pulse oximeter. In this work, we developed a deep learning-based model for sepsis identification. The model takes a single input, the photoplethysmographic signal acquired by pulse oximeter, and performs a binary classification between septic and nonseptic samples. To develop the method, we used MIMIC-III database, which contains data from ICU patients. Specifically, the selected dataset includes 85 septic subjects and 101 control subjects. The PPG signals acquired from these patients were segmented, processed and used as input for the developed model with the aim of identifying sepsis. The proposed method achieved an accuracy of 76.37% with a sensitivity of 70.95% and a specificity of 81.04% on the test set. As regards the ROC curve, the Area Under Curve reached a value of 0.842. The results of this study indicate how the plethysmographic signal can be used as a warning sign for the early detection of sepsis with the aim of reducing the time for diagnosis and therapeutic intervention. Furthermore, the proposed method is suitable for integration in continuous patient monitoring.

Convolutional neural network-based respiration analysis of electrical activities of the diaphragm

The electrical activity of the diaphragm (Edi) is considered a new respiratory vital sign for monitoring breathing patterns and efforts during ventilator care. However, the Edi signal contains irregular noise from complex causes, which makes reliable breathing analysis difficult. Deep learning was implemented to accurately detect the Edi signal peaks and analyze actual neural breathing in premature infants. Edi signals were collected from 17 premature infants born before gestational age less than 32 weeks, who received ventilatory support with a non-invasive neurally adjusted ventilatory assist. First, a local maximal detection method that over-detects candidate Edi peaks was used. Subsequently, a convolutional neural network-based deep learning was implemented to classify candidates into final Edi peaks. Our approach showed superior performance in all aspects of respiratory Edi peak detection and neural breathing analysis compared with the currently used recording technique in the ventilator. The method obtained a f1-score of 0.956 for the Edi peak detection performance and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}^{2}$$\end{document}R2 value of 0.823 for respiratory rates based on the number of Edi peaks. The proposed technique can achieve a more reliable analysis of Edi signals, including evaluation of the respiration rate in premature infants.

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.

Detecting beats in the photoplethysmogram: benchmarking open-source algorithms

The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best.Objective:This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology.Approach:Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using theF1score, which combines sensitivity and positive predictive value.Main results:Eight beat detectors performed well in the absence of movement withF1scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise withF1scores of 55%-91%; poorer in neonates than adults withF1scores of 84%-96% in neonates compared to 98%-99% in adults; and poorer in atrial fibrillation (AF) withF1scores of 92%-97% in AF compared to 99%-100% in normal sinus rhythm.Significance:Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.

Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth

Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.

Photoplethysmography-Based Pulse Rate Variability and Haemodynamic Changes in the Absence of Heart Rate Variability: An In-Vitro Study

Pulse rate variability (PRV), measured from pulsatile signals such as the photoplethysmogram (PPG), has been largely used in recent years as a surrogate of heart rate variability (HRV), which is measured from electrocardiograms (ECG). However, different studies have shown that PRV does not always replicate HRV as there are multiple factors that could affect their relationship, such as respiration and pulse transit time. In this study, an in-vitro model was developed for the simulation of the upper-circulatory system, and PPG signals were acquired from it when haemodynamic changes were induced. PRV was obtained from these signals and time-domain, frequency-domain and non-linear indices were extracted. Factorial analyses were performed to understand the effects of changing blood pressure and flow on PRV indices in the absence of HRV. Results showed that PRV indices are affected by these haemodynamic changes and that these may explain some of the differences between HRV and PRV. Future studies should aim to replicate these results in healthy volunteers and patients, as well as to include the HRV information in the in-vitro model for a more profound understanding of these differences.

Detecting sepsis from photoplethysmography: strategies for dataset preparation

Sepsis is one of the most frequent causes of death in Intensive Care Units, and its prognosis greatly depend on timeliness of diagnosis. MIMIC-III database is a frequent source of data for developing method for automatic sepsis detection. However, the heterogeneity of data jeopardize the feasibility of the task. In this work we propose a selection strategy for generating high quality data suitable for training a sepsis detection system based on the utilization of only plethysmographic data. Clinical relevance A system for detecting sepsis based only on PPG may be potentially at virtually no cost in any case clinicians suspect the possibility of developing sepsis.

The Effects of Filtering PPG Signal on Pulse Arrival Time-Systolic Blood Pressure Correlation

Pulse arrival time (PAT), evaluated from electro-cardiogram (ECG) and photoplethysmogram (PPG) signals, has been widely used for cuff-less blood pressure (BP) estimation due to its high correlation with BP. However, the question of whether filtering the PPG signal impacts the extracted PAT values and consequently, the correlation between PAT and BP, has not been investigated before. In this paper, using data from 18 subjects, changes in the PAT values, and in the subject-specific PAT-systolic BP (SBP) correlation caused by filtering the PPG signal with variable cutoff frequencies in the range of 2 to 15 Hz are studied. For PAT extraction, three PPG characteristic points (foot, maximum slope and systolic peak) are considered. Results show that differences in the cutoff frequency can shift the PAT values and introduce a worst-case error of over 8.2 mmHg for SBP estimation, indicating that PPG signal filter settings can impact PAT-based BP estimations. Our study suggests that extracting the PAT from the maximum slope point of PPG signal filtered at 10 Hz provides the most stable correlation with SBP.

Outlier Management for Pulse Rate Variability Analysis from Photoplethysmographic Signals

Pulse rate variability (PRV) has been proposed as a surrogate for the estimation of Heart Rate Variability (HRV), which is a non-invasive technique used to assess the cardiac autonomic activity. However, both physiological and technical factors may affect the relationship between HRV and PRV, and there are no standards for the analysis of PRV from photoplethysmographic (PPG) signals. The aim of this study was to determine the best outlier management strategies for PRV analysis. 117 PPG signals with randomly generated PRV information were simulated using Gaussian signals. From these, interbeat intervals were detected and different outlier detection and correction techniques were applied. Time and frequency-domain and non-linear PRV indices were extracted and compared with respect to the gold standard values obtained from the simulated PRV information. The results show that, in good quality PPG signals, there is no need to apply any outlier management technique for the extraction of PRV information. Clinical relevance- Establishing guidelines for PRV mea-surement can lead to more reliable and comparable results, as well as to the increase in the use of this variable for the diagnosis and monitoring of cardiovascular and autonomic conditions.