An open source benchmarked toolbox for cardiovascular waveform and interval analysis

Prediction of intradialytic hypotension based on heart rate variability and skin sympathetic nerve activity using LASSO-enabled feature selection: a two-center study

BACKGROUND

Intradialytic hypotension (IDH) is a prevalent complication during hemodialysis (HD). However, conventional predictive models are imperfect due to multifaceted etiologies underlying IDH.

METHODS

This study enrolled 201 patients undergoing maintenance HD across two centers. Seventy percent of the patient cohort was randomly allocated to the training cohort (n = 136), while the remaining 30% formed the validation cohort (n = 65). IDH was defined as a reduction in systolic blood pressure (SBP) ≥20 mmHg or mean arterial pressure (MAP) ≥10 mmHg. Clinical data and autonomic nervous parameters, including skin sympathetic nerve activity (SKNA) and heart rate variability (HRV) during the initial 30 min of HD, were employed to construct the model. The least absolute shrinkage and selection operator (LASSO) regression facilitated variable selection associated with IDH. Subsequently, a multivariable logistic regression model was formulated to predict the risk of IDH and establish the nomogram.

RESULTS

Sixty-six baseline features were included in the LASSO-regression model. In the final multivariable logistic regression model, 5 variables (SBP0, aSKNA0, △aSKNA0-30, SDNN0, △SDNN0-30) were incorporated into the nomogram. The AUC was 0.920 (95% CI, 0.878-0.962) in the training cohort and 0.855 (95% CI, 0.763-0.947) in the validation cohort, indicating concordance between the nomogram prediction and actual observation of IDH.

CONCLUSION

The LASSO-enabled model, based on clinical characteristics and autonomic nervous system parameters from the first 30 min of HD, shows promise in accurately predicting IDH.

Reduced Complexity of Pulse Rate Is Associated With Faster Cognitive Decline in Older Adults

BACKGROUND

Cardiovascular diseases are closely linked to cognitive health. Subclinical cardiovascular functional changes, such as cardiac autonomic dysfunction, precede cardiovascular diseases and improve risk stratification. Continuous monitoring of heart rate or pulse rate is a commonly used approach to evaluate cardiac cycle and autonomic regulation. We investigated whether the complexity of pulse rate is associated with longitudinal cognitive decline in older adults.

METHODS

Overnight pulse oximetry data were collected from 503 participants (mean age=82±7 [SD] years, 76% female). We used a previously established distribution entropy algorithm to extract the complexity of pulse rate as a proxy for subclinical cardiovascular function. Participants completed a standardized cognitive test battery during the same visit of pulse oximetry and at least 1 follow-up visit. Linear mixed-effects models were conducted to test whether distribution entropy is associated with longitudinal changes in global cognition and separately, in 5 cognitive domains.

RESULTS

Greater distribution entropy (ie, better complexity) was associated with a slower decline in global cognition; the effect of 1-SD increase in distribution entropy was equivalent to being approximately 3 years younger. No associations were observed between conventional time- or frequency-domain pulse rate variability measures and cognitive changes.

CONCLUSIONS

Higher complexity of pulse rate is linked with slower cognitive decline in older adults. Future studies should test whether complexity is also associated with future risks of neurodegenerative disorders, such as dementia, and further elucidate the causal directions.

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and treatment, yet conventional genetic testing remains costly and time-consuming. This study aims to improve the distinction between LQTS genotypes, particularly LQT3, through a novel electrocardiogram (ECG)-based approach. Patients with LQT3 are at elevated risk due to arrhythmia triggers associated with rest and sleep. Employing a database of genotyped long QT syndrome E-HOL-03-0480-013 ECG signals, we introduced two innovative parameterization techniques-area under the ECG curve and wave transformation into the unit circle-to classify LQT3 against LQT1 and LQT2 genotypes. Our methodology utilized single-lead ECG data with a 200 Hz sampling frequency. The support vector machine (SVM) model demonstrated the ability to discriminate LQT3 with a recall of 90% and a precision of 81%, achieving an F1-score of 0.85. This parameterization offers a potential substitute for genetic testing and is practical for low frequencies. These single-lead ECG data could enhance smartwatches' functionality and similar cardiovascular monitoring applications. The results underscore the viability of ECG morphology-based genotype classification, promising a significant step towards streamlined diagnosis and improved patient care in LQTS.

Feature Extraction Tool Using Temporal Landmarks in Arterial Blood Pressure and Photoplethysmography Waveforms

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool's ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tool's real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tool's detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 %), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.

The MSPTDfast photoplethysmography beat detection algorithm: design, benchmarking, and open-source distribution

Objective:photoplethysmography is widely used for physiological monitoring, whether in clinical devices such as pulse oximeters, or consumer devices such as smartwatches. A key step in the analysis of photoplethysmogram (PPG) signals is detecting heartbeats. The multi-scale peak & trough detection (MSPTD) algorithm has been found to be one of the most accurate PPG beat detection algorithms, but is less computationally efficient than other algorithms. Therefore, the aim of this study was to develop a more efficient, open-source implementation of theMSPTDalgorithm for PPG beat detection, namedMSPTDfast (v.2).Approach.five potential improvements toMSPTDwere identified and evaluated on four datasets.MSPTDfast (v.2)was designed by incorporating each improvement which on its own reduced execution time whilst maintaining a highF1-score. After internal validation,MSPTDfast (v.2)was benchmarked against state-of-the-art beat detection algorithms on four additional datasets.Main results.MSPTDfast (v.2)incorporated two key improvements: pre-processing PPG signals to reduce the sampling frequency to 20 Hz; and only calculating scalogram scales corresponding to heart rates >30 bpm. During internal validationMSPTDfast (v.2)was found to have an execution time of between approximately one-third and one-twentieth ofMSPTD, and a comparableF1-score. During benchmarkingMSPTDfast (v.2)was found to have the highestF1-score alongsideMSPTD, and amongst one of the lowest execution times with onlyMSPTDfast (v.1),qppgfastandMMPD (v.2)achieving shorter execution times.Significance.MSPTDfast (v.2)is an accurate and efficient PPG beat detection algorithm, available in an open-source Matlab toolbox.

Establishing normative values for short-term heart rate variability indices in healthy infants in the emergency department

Background

Heart rate variability (HRV) has been used as a marker of cardiovascular health and a risk factor for mortality in the adult and paediatric populations, and as an indicator of neonatal sepsis. There has been an increasing interest in using short-term (5 minutes) HRV to identify infants ≤90 days of life with serious bacterial infections. However, there has not been any normative data range reported for short-term HRV indices in this infant population. The aim of this study was to evaluate short-term HRV indices in awake, healthy young infants >48 hours and ≤90 days of life and to establish a reference range. We also aimed to produce a clinical calculator that can be used in this population for evaluation of short-term HRV variables in young infants in the emergency department (ED) setting that can be potentially used in future clinical validation and research.

Methods

We conducted a prospective observational study of short-term HRV analysis of awake, well infants ≤90 days of life in the ED setting.

Results

One hundred and eight infants with complete data [51.9% male, median age 9 days (interquartile range, 4-35 days)] were included. We found that heart rate (HR) is correlated with HRV. Thus, normalisation of HRV parameters was done to remove their dependence on HR. We then provided normative reference range of widely used short-term HRV time-domain, frequency-domain, and non-linear HRV metrics in our cohort.

Conclusions

We established normative values and HRV calculator for evaluation of these short-term HRV variables in young infants in ED settings that can be used for further clinical validation and clinical research.

One-week test-retest stability of heart rate variability during rest and deep breathing

Objective. Heart rate variability (HRV) is an important indicator of cardiac autonomic function. Given its clinical significance, reliable HRV assessment is crucial. Here, we assessed test-retest stability, as a key aspect of reliability, quantifying the consistency of a measure when repeated under the same conditions.Approach. This observational study includes healthy individuals. A 20 min electrocardiogram was recorded at rest in a supine position and during deep breathing in two lab sessions within one week, at the same time of day. HRV indices from time domain, frequency domain, nonlinear dynamics, and information-theoretic complexity were assessed using a validated toolbox. Additionally, heart rate variations per respiratory cycle were evaluated during deep breathing. Lifestyle factors such as perceived stress, mood, physical activity, sleep quality were assessed prior to both sessions. Intra-class correlation (ICC) and coefficients of variation (CVs) were used to assess the concordance between the two measurements and the relative deviation, respectively.Main results. From 62 screened individuals, 51 participants were recruited from the local community. One participant opted out for personal reasons, and another with frequent premature beats was excluded, leaving a final sample of 49 individuals. Most self-rated psychological and lifestyle indicators showed substantial agreement, though participants reported less stress and better mood in the second session. At rest, ICC of HRV ranged from 0.50 to 0.83, with CV from 5% to 41%. Spectral HRV measures were less reliable than time domain parameters. Nonlinear and time domain features had substantial to nearly perfect agreement. Complexity measures had low CVs but limited test-retest correlation. The stability indices of HRV during deep breathing were not significantly different from those during rest. Test-retest differences in root mean square of the successive beat-to-beat interval difference were not sufficiently explained by lifestyle factors.Significance.Test-retest stability of HRV depends considerably on chosen measures. Our data suggest that HRV can be assessed reliably using time-domain indices at rest.

One-week test-retest recordings of resting cardiorespiratory data for reliability analysis

Heart rate variability (HRV) is a key indicator of cardiac autonomic function, making reliable assessment crucial. To examine the test-retest stability of resting HRV in healthy individuals, fifty participants attended two lab sessions within a week, at the same time of day. After a 5-minute acclimatization period, electrocardiogram and respiration were recorded at rest. For validation, average heart rate and RMSSD were assessed over 15 minutes using a validated open-source toolbox. Test-retest agreement was evaluated using intra-class correlation (ICC), and coefficients of variation (CV). Mean heart rate showed high stability (ICC = 0.81, CV = 6%), while RMSSD had lower concordance (ICC = 0.75) and greater variation (CV = 30%). These findings indicate good test-retest agreement for standard HRV features. However, a wide range of methodologies exists for assessing various properties of heart rate dynamics. This database is intended to support other researchers in testing additional HRV metrics to evaluate their reliability in healthy individuals.

The Effect of Yoga Nidra Intervention on Blood Pressure and Heart Rate Variability Among Hypertensive Adults: A Single-arm Intervention Trial

Background Hypertension [blood pressure (BP) >130/80 mmHg] contributes significantly to cardiovascular morbidity and mortality. Lifestyle modifications, including mindfulness-based practices like Yoga, meditation, and relaxation techniques, have emerged as promising adjuncts to pharmacotherapy. This study aimed to explore the acute effects of Yoga Nidra (YN) on BP in essential hypertension and the potential mechanisms of the effect of YN on BP, in the form of changes in heart rate variability (HRV) components. Methods A total of 32 hypertensive individuals (mean age: 43 ±0.54 years; 22 males, 10 females) were enrolled at the Lifestyle Disease Clinic. Patients were provided regular consultation and pharmacotherapy. BP and HRV were assessed before and after a 16-minute YN session. HRV parameters included time and frequency domain measures. Statistical analysis included linear regression to study the relationship of components of HRV with those of the changes in BP. Results Following YN intervention, there was a significant reduction in both systolic BP (SBP) (7 mmHg) and diastolic BP (DBP) (6 mmHg). HRV analysis revealed significant increases. Regression analysis showed changes in SBP having significant coefficients. Conclusions A single session of YN reduced the systolic and diastolic BP and increased HRV parameters. Regression analyses showed that the reduction in BP can be explained by an increase in HRV parameters. Thus, this study demonstrates the positive effect of YN as an intervention for essential hypertension and also the potential mechanisms behind it, which can be explained by the Neurovisceral Integration Model.

Dissecting depression symptoms: Multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation

In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small marker panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals. The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms.

LieRHRV system for remote lie detection using heart rate variability parameters

The standard polygraph, or lie detector, is limited by its reliance on average heart rate, subjective examiner interpretation, and the need for direct subject contact. Remote photoplethysmography (rPPG) offers a promising contactless alternative, by using facial videos to extract heart rate variability (HRV). We introduce "LieRHRV," a remote lie detection algorithm based solely on extracted HRV parameters. To test the HRV parameter quality, we compared these parameters to HRV parameters extracted from ECG and photoplethysmography (PPG) records archived in five gold-standard ECG/PPG datasets. A prospective study of 39 healthy volunteers was also performed to evaluate the accuracy of lie detection based on PPG- or rPPG-derived HRV parameters. Effective HRV parameter extraction from both PPG and ECG sources was demonstrated, with comparable outcomes among 60% of the parameters on average with the publicly available datasets, and prospective study with 80% of the parameters. LieRHRV performance on ECG, PPG or rPPG (with parameters selected for PPG) exhibited an accuracy of 83.3 ± 3%, 87.3 ± 4% or 91.7 ± 3.5%, respectively. In comparison, the naïve model for ECG, PPG or rPPG data achieved an accuracy of 58.3 ± 3%, 61.0 ± 3% or 67.0 ± 5%, respectively. This study demonstrated the feasibility and effectiveness of LieRHRV, and offers a promising avenue for advancing lie detection technologies beyond polygraph limitations.

Heart rate variability wrist-wearable biomarkers identify adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common after traumatic events. We examined whether wrist-wearable devices could provide heart rate variability (HRV) biomarkers for recovery after traumatic stress exposure in a large socioeconomically disadvantaged cohort. Participants were enrolled in the emergency department within 72 hours after a traumatic event as part of the AURORA (Advancing Understanding of RecOvery afteR traumA) multicenter prospective observational cohort study and followed over 6 months. HRV biomarkers were derived and validated for associations with specific APNS symptoms at a point in time and changes in symptom severity over time. Sixty-four HRV characteristics were derived and validated as cross-sectional biomarkers of APNS symptoms, including pain (26), re-experiencing (8), somatic (7), avoidance (7), concentration difficulty (6), hyperarousal (5), nightmares (1), anxiety (1), and sleep disturbance (3). Changes in 22 HRV characteristics were derived and validated as biomarkers identifying changes in APNS symptoms, including reexperiencing (11), somatic (3), avoidance (2), concentration difficulty (1), hyperarousal (1), and sleep disturbance (4). Changes in HRV variables over time predicted symptom improvement (PPV 0.68-0.87) and symptom worsening (NPV 0.71-0.90). HRV biomarkers collected from wrist-wearable devices may have utility as screening tools for APNS symptoms that occur after traumatic stress exposure in high-risk populations.

Deriving Accurate Nocturnal Heart Rate, rMSSD and Frequency HRV from the Oura Ring

Cardiovascular diseases are a major cause of mortality worldwide. Long-term monitoring of nighttime heart rate (HR) and heart rate variability (HRV) may be useful in identifying latent cardiovascular risk. The Oura Ring has shown excellent correlation only with ECG-derived HR, but not HRV. We thus assessed if stringent data quality filters can improve the accuracy of time-domain and frequency-domain HRV measures. 92 younger (<45 years) and 22 older (≥45 years) participants from two in-lab sleep studies with concurrent overnight Oura and ECG data acquisition were analyzed. For each 5 min segment during time-in-bed, the validity proportion (percentage of interbeat intervals rated as valid) was calculated. We evaluated the accuracy of Oura-derived HR and HRV measures against ECG at different validity proportion thresholds: 80%, 50%, and 30%; and aggregated over different durations: 5 min, 30 min, and Night-level. Strong correlation and agreements were obtained for both age groups across all HR and HRV metrics and window sizes. More stringent validity proportion thresholds and averaging over longer time windows (i.e., 30 min and night) improved accuracy. Higher discrepancies were found for HRV measures, with more than half of older participants exceeding 10% Median Absolute Percentage Error. Accurate HRV measures can be obtained from Oura's PPG-derived signals with a stringent validity proportion threshold of around 80% for each 5 min segment and aggregating over time windows of at least 30 min.

Resting-state functional MRI of the nose as a novel investigational window into the nervous system

Besides being responsible for olfaction and air intake, the nose contains abundant vasculature and autonomic nervous system innervations, and it is a cerebrospinal fluid clearance site. Therefore, the nose is an attractive target for functional MRI (fMRI). Yet, nose fMRI has not been possible so far due to signal losses originating from nasal air-tissue interfaces. Here, we demonstrated feasibility of nose fMRI by using novel ultrashort/zero echo time (TE) MRI. Results obtained in the resting-state from 13 healthy participants at 7T and in 5 awake mice at 9.4T revealed a highly reproducible resting-state nose functional network that likely reflects autonomic nervous system activity. Another network observed in humans involves the nose, major brain vessels and CSF spaces, presenting a temporal dynamic that correlates with heart rate and breathing rate. These resting-state nose functional signals should help elucidate peripheral and central nervous system integrations.

Nomogram model based on clinical factors and autonomic nervous system activity for predicting residual renal function decline in patients undergoing peritoneal dialysis

Background

Several heart rate variability (HRV) parameters were reported to be associated with residual renal function (RRF) in patients undergoing continuous ambulatory peritoneal dialysis (CAPD). However, it is unclear whether using HRV or other autonomic nervous system (ANS) activity indexes can predict RRF decline in CAPD patients.

Methods

Patients undergoing CAPD in 2022 from the First Affiliated Hospital of Nanjing Medical University were enrolled in this study. Their clinical characteristics, 5-min HRV parameters and average voltage of 5-min skin sympathetic nerve activity (aSKNA) were collected. According to the 12-month glomerular filtration rate (GFR) decline rate compared with the upper quartile, these patients were categorized into two groups: RRF decline (RRF-D) group and RRF stable (RRF-S) group. Clinical factors and ANS activity indexes for predicting 1-year RRF decline were analyzed using logistic regression, and a nomogram model was further established. The relationships between volume load related indexes and aSKNA were displayed by Spearman's correlation graphs.

Results

Ninety-eight patients (53 women, average age of 46.7 ± 13.0 years old) with a median dialysis vintage of 24.5 months were enrolled in this study. Seventy-three patients were categorized into the RRF-S group and 25 patients into the RRF-D group. Compared with RRF-S group, patients in the RRF-D group had higher systolic blood pressure (BP; p = 0.019), higher GFR (p = 0.016), higher serum phosphorous level (p = 0.030), lower total Kt/V (p = 0.001), and lower levels of hemoglobin (p = 0.007) and albumin (p = 0.010). The RRF-D group generally exhibited lower HRV parameters and aSKNA compared with the RRF-S group. A nomogram model included clinical factors (sex, systolic BP, hemoglobin, GFR, and total Kt/V) and aSKNA showed the largest AUC of 0.940 (95% CI: 0.890-0.990) for predicting 1-year RRF decline.

Conclusion

The nomogram model included clinical factors (sex, systolic BP, hemoglobin, GFR and total Kt/V) and ANS activity index (aSKNA) might be a promising tool for predicting 1-year RRF decline in CAPD patients.

Understanding allostasis: Early-life self-regulation involves both up- and down-regulation of arousal

Optimal performance lies at intermediate autonomic arousal, but no previous research has examined whether the emergence of endogenous control associates with changes in children's up-regulation from hypo-arousal, as well as down-regulation from hyper-arousal. We used wearables to take day-long recordings from N = 58, 12-month-olds (60% white/58% female); and, in the same infants, we measured self-regulation in the lab with a still-face paradigm. Overall, our findings suggest that infants who showed more self-regulatory behaviors in the lab were more likely to actively change their behaviors in home settings moment-by-moment "on the fly" following changes in autonomic arousal, and that these changes result in up- as well as down-regulation. Implications for the role of atypical self-regulation in later psychopathology are discussed.

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.

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study

BACKGROUND

Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations.

OBJECTIVE

We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting.

METHODS

Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA.

RESULTS

All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001).

CONCLUSIONS

Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.3390/clockssleep6010010.

Exaggerated Peripheral and Systemic Vasoconstriction During Trauma Recall in Posttraumatic Stress Disorder: A Co-Twin Control Study

BACKGROUND

Individuals with posttraumatic stress disorder (PTSD) face an increased risk of cardiovascular disease, but the mechanisms linking PTSD to cardiovascular disease remain incompletely understood. We used a co-twin control study design to test the hypothesis that individuals with PTSD exhibit augmented peripheral and systemic vasoconstriction during a personalized trauma recall task.

METHODS

In 179 older male twins from the Vietnam Era Twin Registry, lifetime history of PTSD and current (last month) PTSD symptoms were assessed. Participants listened to neutral and personalized trauma scripts while peripheral vascular tone (Peripheral Arterial Tone ratio) and systemic vascular tone (e.g., total vascular conductance) were measured. Linear mixed-effect models were used to assess the within-pair relationship between PTSD and vascular tone indices.

RESULTS

The mean age of participants was 68 years, and 19% had a history of PTSD. For the Peripheral Arterial Tone ratio analysis, 32 twins were discordant for a history of PTSD, and 46 were discordant for current PTSD symptoms. Compared with their brothers without PTSD, during trauma recall, participants with a history of PTSD had greater increases in peripheral (β = -1.01, 95% CI [-1.72, -0.30]) and systemic (total vascular conductance: β = -1.12, 95% CI [-1.97, -0.27]) vasoconstriction after adjusting for cardiovascular risk factors. Associations persisted after adjusting for antidepressant medication use and heart rate and blood pressure during the tasks. Analysis of current PTSD symptom severity showed consistent results.

CONCLUSIONS

PTSD is associated with exaggerated peripheral and systemic vasoconstrictor responses to traumatic stress reminders, which may contribute to elevated risk of cardiovascular disease.

Refined matrix completion for spectrum estimation of heart rate variability

Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessments of cardiac activities. In this study, we introduced a novel approach for estimating uncertainties in HRV spectrum based on matrix completion. The proposed method utilises the low-rank characteristic of HRV spectrum matrix to efficiently estimate data uncertainties. In addition, we developed a refined matrix completion technique to enhance the estimation accuracy and computational cost. Benchmarking on five public datasets, our model shows effectiveness and reliability in estimating uncertainties in HRV spectrum, and has superior performance against five deep learning models. The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.

Sample entropy correlates with intraventricular hemorrhage and mortality in premature infants early in life

BACKGROUND

Mortality and intraventricular hemorrhage (IVH) are common adverse outcomes in preterm infants and are challenging to predict clinically. Sample entropy (SE), a measure of heart rate variability (HRV), has shown predictive power for sepsis and other morbidities in neonates. We evaluated associations between SE and mortality and IVH in the first week of life.

METHODS

Participants were 389 infants born before 32 weeks of gestation for whom bedside monitor data were available. A total of 29 infants had IVH grade 3 or 4 and 31 infants died within 2 weeks of life. SE was calculated with the PhysioNet open-source benchmark. Logistic regressions assessed associations between SE and IVH and/or mortality with and without common clinical covariates over various hour of life (HOL) censor points.

RESULTS

Lower SE was associated with mortality by 4 HOL, but higher SE was very strongly associated with IVH and mortality at 24-96 HOL. Bootstrap testing confirmed SE significantly improved prediction using clinical variables at 96 HOL.

CONCLUSION

SE is a significant predictor of IVH and mortality in premature infants. Given IVH typically occurs in the first 24-72 HOL, affected infants may initially have low SE followed by a sustained period of high SE.

IMPACT

SE correlates with IVH and mortality in preterm infants early in life. SE combined with clinical factors yielded ROC AUCs well above 0.8 and significantly outperformed the clinical model at 96 h of life. Previous studies had not shown predictive power over clinical models. First study using the PhysioNet Cardiovascular Toolbox benchmark in young infants. Relative to the generally accepted timing of IVH in premature infants, we saw lower SE before or around the time of hemorrhage and a sustained period of higher SE after. Higher SE after acute events has not been reported previously.

Investigating Ultra-Short-Term Heart Rate Variability as an Indicator of Craving in Recently Treated Patients with Opioid Use Disorder. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2024;2024:1-4. [PMID: 40039667 PMCID: PMC11940695 DOI: 10.1109/embc53108.2024.10782795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Induced craving frequently leads to relapse in patients with a history of opioid use disorder (OUD). Quantifying the physiological manifestations of craving can enable caregivers of patients with OUD to continuously monitor craving and thus help inform treatment plans. Heart rate variability (HRV) is a promising candidate to capture the trends in such manifestations due to the ease of measuring changes in heartbeat intervals using the electrocardiogram. However, the relationship between craving and HRV measured over periods shorter than 5 minutes, i.e., ultra-short-term HRV, has not yet been investigated in detail. In this work, we present the first analysis on the relationship between 12 HRV features computed over ~2-minute periods and subjective craving reported on the visual analog scale (VAS-craving). Patients with OUD stable on medication (N = 12) went through an approximately 2-hour protocol involving audio/visual opioid cues to induce craving, alongside active transcutaneous vagus nerve stimulation or sham stimulation. Regardless of stimulation type, changes in VAS-craving scores over the course of the protocol were negatively correlated with changes in two ultra-short-term HRV features: the number of adjacent normal heartbeat intervals differing by more than 50 milliseconds (r = -0.63, p = 0.03) and the total power across all frequency bands (r = -0.69, p = 0.03).Clinical relevance-This preliminary study suggests that certain ultra-short-term HRV features can potentially serve as indicators of craving in patients with a history of OUD. Using such metrics computed over small data intervals can enable efficient clinical decision making and may help prevent opioid relapse.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

An open-access simultaneous electrocardiogram and phonocardiogram database

Objective.The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels.Approach.We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics.Main results.The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis.Significance.The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.

Heart Rate Variability, Deceleration Capacity of Heart Rate, and Death: A Veteran Twins Study

BACKGROUND

Autonomic function can be measured noninvasively using heart rate variability (HRV), which indexes overall sympathovagal balance. Deceleration capacity (DC) of heart rate is a more specific metric of vagal modulation. Higher values of these measures have been associated with reduced mortality risk primarily in patients with cardiovascular disease, but their significance in community samples is less clear.

METHODS AND RESULTS

This prospective twin study followed 501 members from the VET (Vietnam Era Twin) registry. At baseline, frequency domain HRV and DC were measured from 24-hour Holter ECGs. During an average 12-year follow-up, all-cause death was assessed via the National Death Index. Multivariable Cox frailty models with random effect for twin pair were used to examine the hazard ratios of death per 1-SD increase in log-transformed autonomic metrics. Both in the overall sample and comparing twins within pairs, higher values of low-frequency HRV and DC were significantly associated with lower hazards of all-cause death. In within-pair analysis, after adjusting for baseline factors, there was a 22% and 27% lower hazard of death per 1-SD increment in low-frequency HRV and DC, respectively. Higher low-frequency HRV and DC, measured during both daytime and nighttime, were associated with decreased hazard of death, but daytime measures showed numerically stronger associations. Results did not substantially vary by zygosity.

CONCLUSIONS

Autonomic inflexibility, and especially vagal withdrawal, are important mechanistic pathways of general mortality risk, independent of familial and genetic factors.

Disentangling Visual Exploration Differences in Cognitive Impairment

OBJECTIVE

Individuals with cognitive impairment (CI) exhibit different oculomotor functions and viewing behaviors. In this work we aimed to quantify the differences in these functions with CI severity, and assess general CI and specific cognitive functions related to visual exploration behaviors.

METHODS

A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals. Spatiotemporal properties of the scanpath, the semantic category of the viewed regions, and other composite features were extracted from the estimated eyegaze locations on the corresponding pictures displayed during the test. These features were then used to characterize viewing patterns, classify cognitive impairment, and estimate scores in various neuropsychological tests using machine learning.

RESULTS

Statistically significant differences in spatial, spatiotemporal, and semantic features were found between healthy controls and individuals with CI. The CI group spent more time gazing at the center of the image, looked at more regions of interest (ROI), transitioned less often between ROI yet in a more unpredictable manner, and exhibited different semantic preferences. A combination of these features achieved an area under the receiver-operator curve of 0.78 in differentiating CI individuals from controls. Statistically significant correlations were identified between actual and estimated CI scores and other neuropsychological tests.

CONCLUSION

Evaluating visual exploration behaviors provided quantitative and systematic evidence of differences in CI individuals, leading to an improved approach for passive cognitive impairment screening.

SIGNIFICANCE

The proposed passive, accessible, and scalable approach could help with earlier detection and a better understanding of cognitive impairment.

Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns

OBJECTIVE

Psychiatric evaluation suffers from subjectivity and bias, and is hard to scale due to intensive professional training requirements. In this work, we investigated whether behavioral and physiological signals, extracted from tele-video interviews, differ in individuals with psychiatric disorders.

METHODS

Temporal variations in facial expression, vocal expression, linguistic expression, and cardiovascular modulation were extracted from simultaneously recorded audio and video of remote interviews. Averages, standard deviations, and Markovian process-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination.

RESULTS

Statistically significant feature differences were found between psychiatric and control subjects. Correlations were found between features and self-rated depression and anxiety scores. Heart rate dynamics provided the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities provided AUROCs of 0.72-0.82.

CONCLUSION

Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status.

SIGNIFICANCE

The proposed multimodal approach has the potential to facilitate scalable, remote, and low-cost assessment for low-burden automated mental health services.

Combining Cardiovascular and Pupil Features Using k-Nearest Neighbor Classifiers to Assess Task Demand, Social Context, and Sentence Accuracy During Listening

In daily life, both acoustic factors and social context can affect listening effort investment. In laboratory settings, information about listening effort has been deduced from pupil and cardiovascular responses independently. The extent to which these measures can jointly predict listening-related factors is unknown. Here we combined pupil and cardiovascular features to predict acoustic and contextual aspects of speech perception. Data were collected from 29 adults (mean  =  64.6 years, SD  =  9.2) with hearing loss. Participants performed a speech perception task at two individualized signal-to-noise ratios (corresponding to 50% and 80% of sentences correct) and in two social contexts (the presence and absence of two observers). Seven features were extracted per trial: baseline pupil size, peak pupil dilation, mean pupil dilation, interbeat interval, blood volume pulse amplitude, pre-ejection period and pulse arrival time. These features were used to train k-nearest neighbor classifiers to predict task demand, social context and sentence accuracy. The k-fold cross validation on the group-level data revealed above-chance classification accuracies: task demand, 64.4%; social context, 78.3%; and sentence accuracy, 55.1%. However, classification accuracies diminished when the classifiers were trained and tested on data from different participants. Individually trained classifiers (one per participant) performed better than group-level classifiers: 71.7% (SD  =  10.2) for task demand, 88.0% (SD  =  7.5) for social context, and 60.0% (SD  =  13.1) for sentence accuracy. We demonstrated that classifiers trained on group-level physiological data to predict aspects of speech perception generalized poorly to novel participants. Individually calibrated classifiers hold more promise for future applications.

NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data

BACKGROUND AND OBJECTIVE

We present NLDyn, an open-source MATLAB toolbox tailored for in-depth analysis of nonlinear dynamics in biomedical signals. Our objective is to offer a user-friendly yet comprehensive platform for researchers to explore the intricacies of time series data.

METHODS

NLDyn integrates approximately 80 distinct methods, encompassing both univariate and multivariate nonlinear dynamics, setting it apart from existing solutions. This toolbox combines state-of-the-art nonlinear dynamical techniques with advanced multivariate entropy methods, providing users with powerful analytical capabilities. NLDyn enables analyses with or without a sliding window, and users can easily access and customize default parameters.

RESULTS

NLDyn generates results that are both exportable and visually informative, facilitating seamless integration into research and presentations. Its ongoing development ensures it remains at the forefront of nonlinear dynamics analysis.

CONCLUSIONS

NLDyn is a valuable resource for researchers in the biomedical field, offering an intuitive interface and a wide array of nonlinear analysis tools. Its integration of advanced techniques empowers users to gain deeper insights from their data. As we continually refine and expand NLDyn's capabilities, we envision it becoming an indispensable tool for the exploration of complex dynamics in biomedical signals.

Psychophysiological reactions during the trauma-film paradigm and their predictive value for intrusions

Background: Adequate adaptation of the autonomic nervous system (ANS) is crucial in potentially life-threatening situations. The defence cascade provides a descriptive model of progressing dominant physiological reactions in such situations, including cardiovascular parameters and body mobility. The empirical evidence for this model is scarce, and the influence of physiological reactions in this model for predicting trauma-induced intrusions is unresolved.Objectives: Using a trauma-film paradigm, we aimed to test physiological reactions to a highly stressful film as an analogue to a traumatic event along the defence cascade model. We also aimed to examine the predictive power of physiological activity for subsequent intrusive symptoms.Method: Forty-seven healthy female participants watched a stressful and a neutral film in randomized order. Heart rate (HR), heart rate variability (HRV), and body sway were measured. Participants tracked frequency, distress, and quality of subsequent intrusions in a diary for 7 consecutive days.Results: For the stressful film, we observed an initial decrease in HR, followed by an increase, before the HR stabilized at a high level, which was not found during the neutral film. No differences in HRV were observed between the two films. Body sway and trembling frequency were heightened during the stressful film. Neither HR nor HRV predicted subsequent intrusions, whereas perceived distress during the stressful film did.Conclusions: Our results suggest that the physiological trauma-analogue response is characterized by an orientation response and subsequent hyperarousal, reaching a high physiological plateau. In contrast to the assumptions of the defence cascade model, the hyperarousal was not followed by downregulation. Potential explanations are discussed. For trauma-associated intrusions in the subsequent week, psychological distress during the film seems to be more important than physiological distress. Understanding the interaction between physiological and psychological responses during threat informs the study of ANS imbalances in mental disorders such as post-traumatic stress disorder.

Physiological measurements for driving drowsiness: A comparative study of multi-modality feature fusion and selection

A large number of traffic accidents were caused by drowsiness while driving. In-vehicle alert system based on physiological signals was one of the most promising solutions to monitor driving fatigue. However, different physiological modalities can be used, and many relative studies compared different modalities without considering the implementation feasibility of portable or wearable devices. Moreover, evaluations of each modality in previous studies were based on inconsistent choices of fatigue label and signal features, making it hard to compare the results of different studies. Therefore, the modality comparison and fusion for continuous drowsiness estimation while driving was still unclear. This work sought to comprehensively compare widely-used physiological modalities, including forehead electroencephalogram (EEG), electrooculogram (EOG), R-R intervals (RRI) and breath, in a hardware setting feasible for portable or wearable devices to monitor driving fatigue. Moreover, a more general conclusion on modality comparison and fusion was reached based on the regression of features or their combinations and the awake-to-drowsy transition. Finally, the feature subset of fused modalities was produced by feature selection method, to select the optimal feature combination and reduce computation consumption. Considering practical feasibility, the most effective combination with the highest correlation coefficient was using forehead EEG or EOG, along with RRI and RRI-derived breath. If more comfort and convenience was required, the combination of RRI and RRI-derived breath was also promising.

Heart rhythm complexity analysis in patients with inferior ST-elevation myocardial infarction

Heart rhythm complexity (HRC), a subtype of heart rate variability (HRV), is an important tool to investigate cardiovascular disease. In this study, we aimed to analyze serial changes in HRV and HRC metrics in patients with inferior ST-elevation myocardial infarction (STEMI) within 1 year postinfarct and explore the association between HRC and postinfarct left ventricular (LV) systolic impairment. We prospectively enrolled 33 inferior STEMI patients and 74 control subjects and analyzed traditional linear HRV and HRC metrics in both groups, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We also analyzed follow-up postinfarct echocardiography for 1 year. The STEMI group had significantly lower standard deviation of RR interval (SDNN), and DFAα2 within 7 days postinfarct (acute stage) comparing to control subjects. LF power was consistently higher in STEMI group during follow up. The MSE scale 5 was higher at acute stage comparing to control subjects and had a trend of decrease during 1-year postinfarct. The MSE area under scale 1-5 showed persistently lower than control subjects and progressively decreased during 1-year postinfarct. To predict long-term postinfarct LV systolic impairment, the slope between MSE scale 1 to 5 (slope 1-5) had the best predictive value. MSE slope 1-5 also increased the predictive ability of the linear HRV metrics in both the net reclassification index and integrated discrimination index models. In conclusion, HRC and LV contractility decreased 1 year postinfarct in inferior STEMI patients, and MSE slope 1-5 was a good predictor of postinfarct LV systolic impairment.

Assessment of the human response to acute mental stress-An overview and a multimodal study

Numerous vital signs are reported in association with stress response assessment, but their application varies widely. This work provides an overview over methods for stress induction and strain assessment, and presents a multimodal experimental study to identify the most important vital signs for effective assessment of the response to acute mental stress. We induced acute mental stress in 65 healthy participants with the Mannheim Multicomponent Stress Test and acquired self-assessment measures (Likert scale, Self-Assessment Manikin), salivary α-amylase and cortisol concentrations as well as 60 vital signs from biosignals, such as heart rate variability parameters, QT variability parameters, skin conductance level, and breath rate. By means of statistical testing and a self-optimizing logistic regression, we identified the most important biosignal vital signs. Fifteen biosignal vital signs related to ventricular repolarization variability, blood pressure, skin conductance, and respiration showed significant results. The logistic regression converged with QT variability index, left ventricular work index, earlobe pulse arrival time, skin conductance level, rise time and number of skin conductance responses, breath rate, and breath rate variability (F1 = 0.82). Self-assessment measures indicated successful stress induction. α-amylase and cortisol showed effect sizes of -0.78 and 0.55, respectively. In summary, the hypothalamic-pituitary-adrenocortical axis and sympathetic nervous system were successfully activated. Our findings facilitate a coherent and integrative understanding of the assessment of the stress response and help to align applications and future research concerning acute mental stress.

HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF)

Objective. To examine whether heart rate interval based rapid alert (HIRA) score derived from a combination model of heart rate variability (HRV) and modified early warning score (MEWS) is a surrogate for the detection of acute respiratory failure (ARF) in critically ill sepsis patients.Approach. Retrospective HRV analysis of sepsis patients admitted to Emory healthcare intensive care unit (ICU) was performed between sepsis-related ARF and sepsis controls without ARF. HRV measures such as time domain, frequency domain, and nonlinear measures were analyzed up to 24 h after patient admission, 1 h before the onset of ARF, and a random event time in the sepsis controls. Statistical significance was computed by the Wilcoxon Rank Sum test. Machine learning algorithms such as eXtreme Gradient Boosting and logistic regression were developed to validate the HIRA score model. The performance of HIRA and early warning score models were evaluated using the area under the receiver operating characteristic (AUROC).Main Results. A total of 89 (ICU) patients with sepsis were included in this retrospective cohort study, of whom 31 (34%) developed sepsis-related ARF and 58 (65%) were sepsis controls without ARF. Time-domain HRV for Electrocardiogram (ECG) Beat-to-Beat RR intervals strongly distinguished ARF patients from controls. HRV measures for nonlinear and frequency domains were significantly altered (p< 0.05) among ARF compared to controls. The HIRA score AUC: 0.93; 95% confidence interval (CI): 0.88-0.98) showed a higher predictive ability to detect ARF when compared to MEWS (AUC: 0.71; 95% CI: 0.50-0.90).Significance. HRV was significantly impaired across patients who developed ARF when compared to controls. The HIRA score uses non-invasively derived HRV and may be used to inform diagnostic and therapeutic decisions regarding the severity of sepsis and earlier identification of the need for mechanical ventilation.

Insights into vascular physiology from sleep photoplethysmography

STUDY OBJECTIVES

Photoplethysmography (PPG) in consumer sleep trackers is now widely available and used to assess heart rate variability (HRV) for sleep staging. However, PPG waveform changes during sleep can also inform about vascular elasticity in healthy persons who constitute a majority of users. To assess its potential value, we traced the evolution of PPG pulse waveform during sleep alongside measurements of HRV and blood pressure (BP).

METHODS

Seventy-eight healthy adults (50% male, median [IQR range] age: 29.5 [23.0, 43.8]) underwent overnight polysomnography (PSG) with fingertip PPG, ambulatory blood pressure monitoring, and electrocardiography (ECG). Selected PPG features that reflect arterial stiffness: systolic to diastolic distance (∆T_norm), normalized rising slope (Rslope) and normalized reflection index (RI) were derived using a custom-built algorithm. Pulse arrival time (PAT) was calculated using ECG and PPG signals. The effect of sleep stage on these measures of arterial elasticity and how this pattern of sleep stage evolution differed with participant age were investigated.

RESULTS

BP, heart rate (HR) and PAT were reduced with deeper non-REM sleep but these changes were unaffected by the age range tested. After adjusting for lowered HR, ∆T_norm, Rslope, and RI showed significant effects of sleep stage, whereby deeper sleep was associated with lower arterial stiffness. Age was significantly correlated with the amount of sleep-related change in ∆T_norm, Rslope, and RI, and remained a significant predictor of RI after adjustment for sex, body mass index, office BP, and sleep efficiency.

CONCLUSIONS

The current findings indicate that the magnitude of sleep-related change in PPG waveform can provide useful information about vascular elasticity and age effects on this in healthy adults.

Stress-induced brain responses are associated with BMI in women

Overweight and obesity are associated with altered stress reactivity and increased inflammation. However, it is not known whether stress-induced changes in brain function scale with BMI and if such associations are driven by peripheral cytokines. Here, we investigate multimodal stress responses in a large transdiagnostic sample using predictive modeling based on spatio-temporal profiles of stress-induced changes in activation and functional connectivity. BMI is associated with increased brain responses as well as greater negative affect after stress and individual response profiles are associated with BMI in females (pperm < 0.001), but not males. Although stress-induced changes reflecting BMI are associated with baseline cortisol, there is no robust association with peripheral cytokines. To conclude, alterations in body weight and energy metabolism might scale acute brain responses to stress more strongly in females compared to males, echoing observational studies. Our findings highlight sex-dependent associations of stress with differences in endocrine markers, largely independent of peripheral inflammation.

Dimensional emotion recognition from camera-based PRV features

Heart rate variability (HRV) is an important indicator of autonomic nervous system activity and can be used for the identification of affective states. The development of remote Photoplethysmography (rPPG) technology has made it possible to measure pulse rate variability (PRV) using a camera without any sensor-skin contact, which is highly correlated to HRV, thus, enabling contactless assessment of emotional states. In this study, we employed ten machine learning techniques to identify emotions using camera-based PRV features. Our experimental results show that the best classification model achieved a coordination correlation coefficient of 0.34 for value recognition and 0.36 for arousal recognition. The rPPG-based measurement has demonstrated promising results in detecting HAHV (high-arousal high-valence) emotions with high accuracy. Furthermore, for emotions with less noticeable variations, such as sadness, the rPPG-based measure outperformed the baseline deep network for facial expression analysis.

Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning

BACKGROUND

Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults.

METHODS

Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP).

RESULTS

Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness.

CONCLUSION

Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns

Objective

The current clinical practice of psychiatric evaluation suffers from subjectivity and bias, and requires highly skilled professionals that are often unavailable or unaffordable. Objective digital biomarkers have shown the potential to address these issues. In this work, we investigated whether behavioral and physiological signals, extracted from remote interviews, provided complimentary information for assessing psychiatric disorders.

Methods

Time series of multimodal features were derived from four conceptual modes: facial expression, vocal expression, linguistic expression, and cardiovascular modulation. The features were extracted from simultaneously recorded audio and video of remote interviews using task-specific and foundation models. Averages, standard deviations, and hidden Markov model-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination.

Results

Statistically significant feature differences were found between controls and subjects with mental health conditions. Correlations were found between features and self-rated depression and anxiety scores. Visual heart rate dynamics achieved the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities achieved AUROCs of 0.72-0.82. Features from task-specific models outperformed features from foundation models.

Conclusion

Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status.

Significance

The proposed multimodal approach has the potential to facilitate objective, remote, and low-cost assessment for low-burden automated mental health services.

Transcutaneous auricular Vagus Nerve Stimulation and Median Nerve Stimulation reduce acute stress in young healthy adults: a single-blind sham-controlled crossover study

Stress is a major determinant of health and wellbeing. Conventional stress management approaches do not account for the daily-living acute changes in stress that affect quality of life. The combination of physiological monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify stress-induced physiological manifestations and reduce stress during everyday life. This study aimed to evaluate the effectiveness of three well-established transcutaneous PNS modalities in reducing physiological manifestations of stress compared to a sham: auricular and cervical Vagus Nerve Stimulation (taVNS and tcVNS), and Median Nerve Stimulation (tMNS). Using a single-blind sham-controlled crossover study with four visits, we compared the stress mitigation effectiveness of taVNS, tcVNS, and tMNS, quantified through physiological markers derived from five physiological signals peripherally measured on 19 young healthy volunteers. Participants underwent three acute mental and physiological stressors while receiving stimulation. Blinding effectiveness was assessed via subjective survey. taVNS and tMNS relative to sham resulted in significant changes that suggest a reduction in sympathetic outflow following the acute stressors: Left Ventricular Ejection Time Index (LVETI) shortening (tMNS: p = 0.007, taVNS: p = 0.015) and Pre-Ejection Period (PEP)-to-LVET ratio (PEP/LVET) increase (tMNS: p = 0.044, taVNS: p = 0.029). tMNS relative to sham also reduced Pulse Pressure (PP; p = 0.032) and tonic EDA activity (tonicMean; p = 0.025). The nonsignificant blinding survey results suggest these effects were not influenced by placebo. taVNS and tMNS effectively reduced stress-induced sympathetic arousal in wearable-compatible physiological signals, motivating their future use in novel personalized stress therapies to improve quality of life.

Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance- This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

Economic Choice and Heart Rate Fractal Scaling Indicate That Cognitive Effort Is Reduced by Depression and Boosted by Sad Mood

BACKGROUND

People with depression typically exhibit diminished cognitive control. Control is subjectively costly, prompting speculation that control deficits reflect reduced cognitive effort. Evidence that people with depression exert less cognitive effort is mixed, however, and motivation may depend on state affect.

METHODS

We used a cognitive effort discounting task to measure propensity to expend cognitive effort and fractal structure in the temporal dynamics of interbeat intervals to assess on-task effort exertion for 49 healthy control subjects, 36 people with current depression, and 67 people with remitted depression.

RESULTS

People with depression discounted more steeply, indicating that they were less willing to exert cognitive effort than people with remitted depression and never-depressed control subjects. Also, steeper discounting predicted worse functioning in daily life. Surprisingly, a sad mood induction selectively boosted motivation among participants with depression, erasing differences between them and control subjects. During task performance, depressed participants with the lowest cognitive motivation showed blunted autonomic reactivity as a function of load.

CONCLUSIONS

Discounting patterns supported the hypothesis that people with current depression would be less willing to exert cognitive effort, and steeper discounting predicted lower global functioning in daily life. Heart rate fractal scaling proved to be a highly sensitive index of cognitive load, and data implied that people with lower motivation for cognitive effort had a diminished physiological capacity to respond to rising cognitive demands. State affect appeared to influence motivation among people with current depression given that they were more willing to exert cognitive effort following a sad mood induction.

Analysing heart rate variability in preterm infants: the effect of temporal adjustment of NN peaks and missing data

The measurement of heart rate variability (HRV) in preterm infants provides important information on function to clinicians. Measuring the underlying electrocardiogram (ECG) in the neonatal intensive care unit is a challenge and there is a trade off between extracting accurate measurements of the HRV and the amount of ECG processed due to contamination. Knowledge on the effects of 1) quantization in the time domain and 2) missing data on the calculation of HRV features will inform clinical implementation. In this paper, we studied multiple 5 minute epochs from 148 ECG recordings on 56 extremely preterm infants. We found that temporal adjustment of NN peaks improves the estimate of the NN interval resulting in HRV features (m = 9) that are better correlated with age (median percentage increase in correlation of individual features: 0.2%, IQR: 0.0 to 5.6%; correlation with age predictor and age from 0.721 to 0.787). Improved (sub-sample) quantization of the NN intervals (via interpolation) reduced the overall value of HRV features (median percentage reduction in feature value: -1.3%, IQR: -18.8 to 0.0; m = 9), primarily through a reduction in the energy of high-frequency oscillations. HRV features were also robust to missing data, with measures such as mean NN, fractal dimension and the smoothed nonlinear energy operator (SNEO) less susceptible to missing data than features such as VLF, LF, and HF. Furthermore, age predictions derived from a combination of HRV measures were more robust to missing data than individual HRV measures.Clinical Relevance-Poor quantization in time when estimating the NN peak and the presence of missing data confound HRV measures, particularly spectral measures.

Generalization Error of a Regression Model for Non-Invasive Blood Pressure Monitoring using a Single Photoplethysmography (PPG) Signal

Photoplethysmography (PPG) sensors integrated in wearable devices offer the potential to monitor arterial blood pressure (ABP) in patients. Such cuffless, non-invasive, and continuous solution is suitable for remote and ambulatory monitoring. A machine learning model based on PPG signal can be used to detect hypertension, estimate beat-by-beat ABP values, and even reconstruct the shape of the ABP. Overall, models presented in literature have shown good performance, but there is a gap between research and potential real-world use cases. Usually, models are trained and tested on data from the same dataset and same subjects, which may lead to overestimating their accuracy. In this paper: we compare cross-validation, where the test data are from the same dataset as training data, and external validation, where the model is tested on samples from a new dataset, on a regression model which predicts diastolic blood pressure from PPG features. The results show that, in the cross-validation, the predicted and the real values are linearly dependent, while in the external validation, the predicted values are not related to the real ones, but probably just through an average value.

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat's performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

Isometric handgrip exercise speeds working memory responses in younger and older adults

Physiological arousal affects attention and memory, sometimes enhancing and other times impairing what we attend to and remember. In the present study, we investigated how changes in physiological arousal-induced through short bursts of isometric handgrip exercise-affected subsequent working memory performance. A sample of 57 younger (ages 18-29) and 56 older (ages 65-85) participants performed blocks of isometric handgrip exercise in which they periodically squeezed a therapy ball, alternating with blocks of an auditory working memory task. We found that, compared with those in a control group, participants who performed isometric handgrip had faster reaction times on the working memory task. Handgrip-speeded responses were observed for both younger and older participants, across working memory loads. Analysis of multimodal physiological responses indicated that physiological arousal increased during handgrip. Our findings suggest that performing short bouts of isometric handgrip exercise can improve processing speed, and they offer testable possibilities for the mechanism underlying handgrip's effects on performance. The potential for acute isometric exercise to temporarily improve processing speed may be of particular relevance for older adults who show declines in processing speed and working memory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Disentangling visual exploration differences in cognitive impairment

Objective

Compared to individuals without cognitive impairment (CI), those with CI exhibit differences in both basic oculomotor functions and complex viewing behaviors. However, the characteristics of the differences and how those differences relate to various cognitive functions have not been widely explored. In this work we aimed to quantify those differences and assess general cognitive impairment and specific cognitive functions.

Methods

A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals. Spatial, temporal, semantic, and other composite features were extracted from the estimated eye-gaze locations on the corresponding pictures displayed during the test. These features were then used to characterize viewing patterns, classify cognitive impairment, and estimate scores in various neuropsychological tests using machine learning.

Results

Statistically significant differences in spatial, spatiotemporal, and semantic features were found between healthy controls and individuals with CI. CI group spent more time gazing at the center of the image, looked at more regions of interest (ROI), transitioned less often between ROI yet in a more unpredictable manner, and had different semantic preferences. A combination of these features achieved an area under the receiver-operator curve of 0.78 in differentiating CI individuals from controls. Statistically significant correlations were identified between actual and estimated MoCA scores and other neuropsychological tests.

Conclusion

Evaluating visual exploration behaviors provided quantitative and systematic evidence of differences in CI individuals, leading to an improved approach for passive cognitive impairment screening.

Significance

The proposed passive, accessible, and scalable approach could help with earlier detection and a better understanding of cognitive impairment.

Increasing physical activity in the vehicle with an interactive seating system in a male sample

An interactive seating system (IASS) was compared to a state-of-the-art massage seating system (MS) regarding the potential of reducing health risks from prolonged sitting in the vehicle. The study investigated if the systems (1) increase heart rate, which is associated with reduced metabolic and cardiovascular risks; (2) activate muscles with the potential to reduce musculoskeletal pain; (3) influence seating comfort and discomfort. The systems were compared in a passenger scenario in a laboratory study (30 male subjects). Only the use of the IASS significantly elevated the heart rate. Muscle activity showed tendencies to increase in the lower back only while using the MS. In comparison, the IASS activated all six captured muscles. Significantly less discomfort was found for the IASS compared to the MS. In comparison to the MS, the IASS showed a substantially higher potential for reducing health risks from static sitting in the vehicle.Practitioner summary: This laboratory study compared the effects of a novel automotive interactive seating system with those of a state-of-the-art massage seating system. Muscle activity, heart rate and discomfort indicated that the IASS has a significantly higher potential to reduce health risks associated with static seating in a vehicle.Abbreviations: AB: air bladder; AC: active condition; ADSS: active dynamic seating system; CLBP: chronic lumbar back pain; ECG: electrocardiography; EMG: electromyography; IASS: interactive seating system; MS: massage seating system; PC: passive condition; PDSS: passive dynamic seating system; RMS: rootmean-square; TI: time interval.