Physiological Measurement

BackgroundElectronic Health Records(EHR) are very crucial for Clinical Decision Support Systems and for proper care to be delivered to ICU heart failure patients, there is often missing data due to monitoring device errors thus the need for robust imputation methodologies. ObjectiveTo compare and evaluate three different methodologies for imputing missing data for heart failure patients from the MIMIC-III database: Denoising Autoencoder (DAE), Self-Attention Imputation for Time Series (SAITS), and Multiple Imputation by Chained Equations (MICE) with LightGBM. MethodsAnalysis of 14,090 ICU admissions for patients with heart failure was performed using data from the MIMIC-III database. Features were selected based off of clinical relevance, and 19 clinical features were selected through a combination of Random Forest analysis, correlation analysis, and Mutual Information. The introduction of artificial missing values of 20%, 30%, and 50% was applied to the data set, and then 3 imputation methodologies were evaluated with the DAE, SAITS, and MICE+LightGBM. The performance of each imputation methodology was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). ResultsBoth DAE and SAITS had superior performance on the imputation of missing values across all percentages of missing values. At 20% missingness, DAE had mean MAE = 0.004967, RMSE = 0.005217, and NRMSE = 3.260893 while SAITS had mean MAE = 0.005461, RMSE = 0.005797, and NRMSE = 3.244695; thus MICE+LightGBM resulted in a higher number of errors. At 50% missingness, the SAITS methodology demonstrated the best performance followed by DAE and MICE+LightGBM methods demonstrated decreased performance. The deep learning methodologies maintained a consistent level of accuracy between the clinical variables measured. ConclusionsOur analysis indicates that deep learning-based imputation methodologies significantly outperform traditional methodologies for imputing missing values in ICU heart failure data thus supporting the implementation of these methodologies into Clinical Decision Support Systems for heart failure patients.

PurposeWhile bowel sound auscultation represents a key component of abdominal examination, its utility is limited because bowel sounds (BS) are intermittent, variable, and influenced by factors such as diet and digestive state. This renders it challenging to use them for a quantitative assessment of gastrointestinal health. MethodsBS signals were recorded from 84 subjects (39 patients and 45 healthy controls) using an acoustic SonicGuard sensor and categorized into four patterns. Metadata on physiological parameters were collected to examine their influence on BS characteristics and the differences between healthy and patient BS patterns. ResultsBowel sound patterns are significantly influenced by meal timing, caffeine consumption, and medication intake. Significant differences between healthy and patient groups were also observed in sound count, duration, energy, and waveform shape. These differences were mirrored in the performance of machine learning models finetuned for BS patterns classification, with performance depending on the group used for training and evaluation. ConclusionBS patterns present a promising quantitative indicators of gas-trointestinal health when analyzed alongside relevant physiological parameters.

Reliable interpretation of electrocardiograms (ECGs) requires precise identification of P, QRS, and T (PQRST) wave boundaries. However, it remains challenging due to noise, signal quality variability, and inherent morphological diversity particularly in recordings from children. This study systematically compares the performance of leading deep neural networks (DNN) and heuristic-based delineation algorithms on ambulatory single-lead ECG signals focusing on temporal accuracy. Experiments were conducted using the publicly available LUDB dataset and a private validation dataset comprising 21,759 annotated single-lead wave segments from 611 children recorded using KardiaMobile ECG sensor. DNN were first trained on the LUDB dataset and subsequently tested on the validation dataset. The delineation performance was assessed using Sensitivity (Se) and positive-predictive-value (P+) metrics. The best-performing heuristic based and DNN models reached Se and P+ of (98.9% vs 97.9%) for P, (99.8% vs 99.2%) for QRS, and (98.7% vs 95.9%) for T wave fiducials, respectively. The lowest standard-deviation (in ms) of wave onset/offset delineation was achieved by attention based 1DU-Net model; {+/-}16.6/{+/-}16.3 for P-wave, {+/-}14.0/{+/-}16.3 for QRS, and {+/-}26.3/{+/-}18.8 for T-wave, respectively. The findings indicate that optimized heuristic models can perform comparably to complex DNN, highlighting their efficiency and suitability for real-time ECG delineation in digital health monitoring applications.

Objective: Skin sympathetic nerve activity (SKNA) has emerged as a promising non-invasive surrogate measure of sympathetic drive, but its relevant physiological characteristics remain ill-defined. This observational study aims to investigate its regulatory patterns during rest and Valsalva maneuver (VM) in healthy participants. Method: Using a two-layer strategy integrating signal analysis and physiological modelling, we analyzed data recorded from 41 subjects performing repeated VMs. The observational layer includes time-domain feature comparisons using linear mixed-effect models, and time-varying spectral coherence analysis. The mechanistic layer proposes a mathematical model to investigate whether baroreflex and respiratory modulation are sufficient to reproduce the observed HR and average SKNA (aSKNA) dynamics. Main Results: Mean integrated SKNA (iSKNA) showed more significant change than HRV for VM induced effects. We also found mean iSKNA increase during VM varies with BMI and sex. The coherence analysis indicated that iSKNA strongly synchronized with EDR under resting conditions. The proposed model successfully reproduced main characteristics of aSKNA dynamics, yielding a high median Pearson correlation coefficient of 0.80 ([Q1, Q3] = [0.60, 0.91]). In contrast, HR dynamics were only partially captured, with a median PCC of 0.37 ([Q1, Q3] = [0.16, 0.55]). These results likely suggest SKNA provides a more direct representation of sympathetic burst dynamics during VM in healthy subjects. Significance: This study provides convergent evidence that SKNA reflects known autonomic regulatory influences in healthy subjects. These findings strengthen the physiological interpretability of SKNA while clarifying its appropriate use as a practical biomarker of sympathetic function.

BackgroundStress cardiomyopathy (SCM) shares features with acute myocardial infarction (AMI) which may lead to misdiagnosis and misaligned management decisions. We hypothesize that features derived from the 12-lead ECG can identify cases of SCM and differentiate them from AMI. MethodsUsing data from a large registry of critically ill patients, we trained a deep learning algorithm to perform two classification tasks: (1) SCM vs. Non-SCM and (2) SCM vs. AMI. Model training was accomplished with 3 different sets of input features: raw ECG waveforms, clinical features from the electronic health record (EHR), and a fusion model combining ECG with clinical data. SHapley Additive exPlanations (SHAP) analysis was performed to identify the most influential predictive features. ResultsAmong 71,479 patients admitted to ICU, 349 (0.48%) were diagnosed with SCM while 4,507 (6.31%) had AMI. The clinical-ECG fusion model achieved best performance, with area under the precision-recall curve (AUPRC) values of 0.191 (0.146-0.243) for SCM vs. Non-SCM and 0.430 (0.371-0.488) for SCM vs. AMI, outperforming baseline AUPRCs of 0.0048 and 0.0625, respectively. The fusion model outperformed both the waveform model (p < 0.001) and the EHR model (p < 0.001) for both SCM vs. Non-SCM and SCM vs. AMI. The waveform model achieved AUPRC values of 0.089 (0.062-0.125) for SCM vs. Non-SCM and 0.309 (0.257-0.374) for SCM vs. AMI. There was no statistically significant difference in performance between the waveform model and the EHR model for both tasks. SHAP analysis highlighted female gender as well as congestive heart failure (CHF) and hypertension as the features most influential in predicting SCM. ConclusionFindings indicate that ECG waveforms contain latent information which supports the detection of SCM in patients admitted to the ICU. ECG-based deep learning screening could enable early identification and treatment of SCM and might be particularly valuable in resource-constrained environments.

ObjectiveWhile there are numerous reports on heart rate and its variabilities, a detailed analysis of the component intervals for healthy adults in well controlled condition is lacking. This study analyzes the effect of age, sex, and Body Mass Index (BMI) on nine resting electrocardiogram (ECG) intervals and their intra-individual variabilities in healthy adults under the same testing environment. MethodsUsing the "Autonomic Aging" dataset, ECG recordings from 1,121 healthy volunteers (ages 18-92) were processed. The study employed a specialized segmentation algorithm to identify key ECG markers. We analyze statistically how age, BMI, and sex impact the durations and variabilities of nine ECG intervals. ResultsFifty years of age serves as a critical transition age for cardiac aging for all subjects as a whole. Above this age, the active interval, which is the combined atrial and ventricular conduction time, increases three times faster than at a younger age, primarily driven by lengthening of depolarization times. Compared to the opposite sex, older low-BMI males have a longer atrial conduction time, and older low-BMI females have a larger variability in the ventricular conduction time. High BMI increases the heart rate by reducing the length of the idle interval, i.e., the isoelectric segment at the end of a cardiac cycle. The rate increase is more pronounced among older subjects than younger ones. High BMI males start to exhibit an elevated heart rate and larger variability in the atrial conduction time in their 30s. High BMI females start to show a larger variability in the ventricular repolarization time around 50 years old. ConclusionAge, BMI, and sex all have major impacts on the ECG intervals and their variability. A resting heart behaves largely like a pulse width modulation system, with a stable active interval and an adjustable idle interval to meet the varying needs for cardiac output. The durations and variabilities of the active interval, more than those of the RR interval, are indicators of a hearts health condition. A young and healthy heart tends to have a shorter duration and smaller variability in the active interval.

The electrocardiogram (ECG) is a critical tool in the diagnosis and monitoring of cardiovascular disease. Although traditional 12-lead ECGs offer comprehensive in-sights into the electrical activity of the heart, they typically require clinical settings and expert interpretation, which limits their accessibility. In contrast, smartwatch 1-lead ECGs can be recorded at home, allowing more frequent and rapid monitoring. This opens opportunities not only for early detection but also for enhancing patient autonomy. This study investigates whether 1-lead ECGs can provide information beyond heart rhythm, specifically whether they can be used to assess left ventricular function (LVF) using explainable deep learning models. Our findings show that LVF can be accurately predicted from 1-lead ECGs (AUC = 0.883), nearly matching the performance of 12-lead ECGs (AUC = 0.897). These results suggest that 1-lead ECGs, when combined with interpretable AI, could support broader clinical applications and empower patients, particularly in resource-limited or remote settings.

Background: Atrial fibrillation (AFib) is the most common sustained arrhythmia in the world, imposing a heavy clinical and economic burden on global healthcare systems. Early detection of AFib can reduce mortality and morbidity, while helping to alleviate the growing economic burden of cardiovascular diseases. With the increasing availability of digital health technologies, computational solutions have great potential to support the timely diagnosis of cardiac abnormalities. Objectives: With the increasing availability of electrocardiogram (ECG) data from clinical and wearable devices, manual interpretation has become impractical due to its time-consuming and subjective nature. Existing automated approaches often rely on single classifiers or fixed ensembles that primarily optimize predictive accuracy while neglecting model diversity, which leads to limited robustness and generalization across heterogeneous datasets. Therefore, this study aims to develop a robust and diversity-aware framework for automatic AFib detection that simultaneously improves classification performance and model generalizability. To this end, we propose MOE-ECG, a multi-objective ensemble selection and fusion framework that explicitly optimizes both predictive performance and inter-model diversity for reliable AFib detection from ECG recordings. Methods: The proposed multi-objective ensemble (MOE) framework uses ensemble selection as a bi-objective optimization problem and employs multi-objective particle swarm optimization to identify complementary classifiers from a heterogeneous model pool. Unlike conventional ensembles, it explicitly optimizes both predictive performance and diversity and integrates Dempster-Shafer theory for uncertainty-aware decision fusion. After filtering the ECG signals to remove baseline wander and noise, they were segmented into windows of 20, 60, and 120 heartbeats with 50% overlap. The proposed approach was evaluated over five independent runs to assess its stability and generalization. Fifteen statistical and nonlinear features were obtained from the RR-intervals of the pre-processed ECG signals, of which eight features were selected with correlation analysis to capture subtle information from the ECG data. We trained and evaluated the performance of the proposed model in three open source databases, namely, the MIT-BIH Atrial Fibrillation Database, Saitama Heart Database Atrial Fibrillation, and Long-Term AF Database. Results: The proposed approach achieved the best overall performance on 60-beat segments, with an average accuracy of 89.85%, precision of 91.14%, recall of 94.19%, an F1-score of 92.64%, and area under the curve (AUC) of around 0.95. Statistical analysis using Holm-adjusted Wilcoxon tests confirmed significant improvements (p<0.05) compared to both the best individual classifier and the unoptimized average ensemble of all classifiers. These findings show that the proposed selection and evaluation methodology, rather than group aggregation alone, is the key driver of performance improvements. Conclusion: The results obtained demonstrate that the MOE-ECG model offers a robust, accurate, and reliable solution for the detection of AFib from short ECG segments. The empirical findings, in general, confirm that multi-objective ensemble fusion enhances diagnostic performance and offers robust predictions that will open up possibilities for real-time AFib detection in clinical and tele-health settings.

RationaleAutonomic dysfunction is a hallmark of sepsis pathophysiology, yet its quantification remains challenging. Multiscale entropy (MSE) derived from heart rate variability (HRV) offers a dynamic measure of physiological complexity and may serve as a biomarker of early deterioration associated with subsequent organ failure, vasopressor escalation, or mortality. ObjectiveTo determine whether MSE computed across multiple temporal scales during the first 24 hours of Intensive Care Unit (ICU) admission is associated with short-term mortality and longer-term organ dysfunction in patients with sepsis, and whether these relationships vary across vasopressor exposure. Unlike prior studies that focused on short-term HRV metrics, we applied MSE across multiple temporal scales and incorporated these features into machine learning models to evaluate their prognostic utility in septic shock. MethodsThis retrospective cohort study included adult ICU sepsis patients at Emory University Hospital from January 2016 to December 2019. Of 2,076 eligible patients, 958 were propensity matched into two cohorts: fluids-only and fluids-plus-vasopressor, with norepinephrine as the primary vasopressor. High-resolution electrocardiogram (ECG) waveforms were analyzed to compute MSE across 20 temporal scales. Machine learning models using (1) MSE features alone and (2) MSE combined with demographic and vital sign data (MSE-DV) were compared against traditional HRV measures based model and severity of illness scores for predicting outcomes. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), with a primary outcome of mortality at day 7 and secondary outcome of persistent organ dysfunction at day 28. ResultsIn the fluids-plus-vasopressor cohort, MSE-based models demonstrated superior predictive performance for 7-day mortality (AUROC 0.84) compared to severity of illness scores (AUROC 0.64). MSE-DV models also predicted organ dysfunction including 28-day renal (AUROC 0.75), neurological (AUROC 0.79), and respiratory (AUROC 0.71) dysfunction. Patients receiving second-line and third-line vasopressors and corticosteroids exhibited progressively lower MSE values, particularly at mid-range and long-range scales. ConclusionMSE features in the first 24 hours of ICU stay predict mortality and organ dysfunction with higher discrimination than traditional severity of illness scores. Future work should validate these findings, assess longitudinal MSE trends, and race-specific autonomic patterns to refine predictive models.

Accurate diagnosis of cardiac abnormalities from electrocardiogram signals remains a central challenge in automated cardiovascular assessment. This study investigates the efficiency of time-frequency representations and deep learning architectures in classifying 12-lead ECGs into five diagnostic super-classes using the PTB-XL dataset. Continuous Wavelet Transform is applied to generate time- frequency representations, scalograms and phasograms, representing spectral energy and phase distributions, respectively. We experiment with both early and late information fusion strategies using several convolutional and transformer-based networks of a custom Convolutional Neural Network, Hybrid Deep Learning, transfer learning, feature fusion, and ensemble modeling, and weighted loss strategies. An ensemble fusion of models trained on time-frequency representation and time representation achieved the best overall performance of Area Under Curve of 0.9233 surpassing individual modalities. To improve the results further, weighted focal loss is used to improve the low classification rates in some labels due to imbalanced data. The results highlight the potential of multi-representation wavelet fusion for interpretable and generalizable ECG classification.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is strongly associated with increased risks of stroke, heart failure, and mortality. Traditional methods to predict AF and prognostic its associated risks often fail to capture the full complexity of AF patterns, limiting their predictive accuracy. In spite of the improvements achieved by machine learning (ML) techniques, state-of-the-art AF-focused predictors do not generally incorporate longitudinal data, reducing their capacity to model the dynamic and evolving nature of individual behaviors and physiological indicators over time. The absence of a longitudinal perspective restricts understanding of how AF risk develops and changes across prognostic windows. This study addresses these limitations by developing superior ML models tailored to predict adverse events within a longitudinal Portuguese cohort of individuals with AF. The work targets six clinical endpoints: stroke, all-cause death, cardiovascular death, heart failure hospitalizations, inpatient visits, and acute coronary syndrome. The predictors yielded an AUC of 0.65 for 1-year stroke prediction, outperforming CHA2DS_2-VASc (0.59). For all-cause mortality prediction, the models achieved an AUC of 0.78 against the 0.72 reference of GARFIELD-AF. In addition to predictive advances, the study identifies determinants of AF-related risks and introduces a prototype decision-support tool for clinical use.

BackgroundArtificial intelligence (AI) has emerged as a promising tool for interpreting 12-lead electrocardiograms (ECGs), with the potential to enhance diagnostic accuracy for arrhythmia detection. However, published studies vary widely in methodology and validation strategy, warranting a quantitative synthesis of diagnostic performance. MethodsA systematic review and meta-analysis was conducted according to the PRISMA-DTA 2018 guidelines and registered in PROSPERO (CRD420251027264). Searches were performed in MEDLINE, Embase, and Cochrane Library through September 2025 without language restrictions. Studies evaluating AI algorithms for arrhythmia detection using 12-lead ECGs were included. Data on sensitivity, specificity, and area under the curve (AUC) were extracted. Pooled estimates were generated using a bivariate random-effects model. Risk of bias was assessed with QUADAS-2, and the certainty of evidence was quantified using GRADE. Results20 studies were included in the meta-analysis, encompassing over 5.5 million ECGs. The pooled sensitivity, specificity, and AUC for AI-based arrhythmia detection were 94.0% (95% CI 90.8-96.2; I{superscript 2} = 96.9%), 98.7% (95% CI 97.3-99.3; I{superscript 2} = 98.3%), and 0.982 (95% CI 0.965-0.986), respectively. Detection of atrial fibrillation (AF) yielded a sensitivity of 92.6% (95% CI 86.4-96), a specificity of 99.1% (95% CI 98.4-99.5), and an AUC of 0.988. Convolutional neural networks (CNN) specifically demonstrated a sensitivity of 97.6%, specificity of 98.7%, and an AUC of 0.982 for overall arrhythmia detection. When limited to external validation (n=6), the sensitivity was 96.9% (95% CI 89.2-99.1), specificity was 95.6% (95% CI 77.6-99.3), and AUC was 0.983. No significant publication bias was detected, and the overall certainty of evidence was rated as high. ConclusionsAI models applied to 12-lead ECGs demonstrate excellent diagnostic performance for arrhythmia detection. Findings support potential integration into clinical workflows, particularly in settings with limited cardiology expertise. Given substantial heterogeneity, standardized datasets and multicenter prospective validation are essential to ensure effective and equitable implementation. What is KnownO_LIArtificial intelligence has been increasingly applied to 12-lead electrocardiograms for arrhythmia detection, with multiple studies reporting high diagnostic accuracy. C_LI What the Study AddsO_LIThis meta-analysis demonstrates consistently high diagnostic performance of artificial intelligence for arrhythmia detection on 12-lead ECGs, including atrial fibrillation and externally validated models. C_LIO_LIThe substantial heterogeneity observed underscores the need for standardized datasets and multicenter prospective validation before widespread clinical implementation. C_LI

While the need for continuous blood pressure (BP) monitoring in Japan is high, there are no commercially available cuffless devices for personal daily monitoring use. Fingertip-based sensors are a promising alternative as they eliminate the discomfort of repeated cuff inflation. However, their reliability during winter has been a major technical limitation due to cold-induced peripheral vasoconstriction. This study aimed to address this issue by validating a novel fingertip-based continuous BP monitor used by exercising adults during summer and winter. Eleven community-dwelling older adults (mean age, 73.1 {+/-} 8.8 years) were included in this seasonal comparative study. During exercise, we compared a personal fingertip-based continuous monitor (ArteVu) with a standard oscillometric cuff device (Omron) in summer (mean, 26.5{degrees}C) and winter (mean, 7.4{degrees}C). The study also evaluated the device's accuracy during exercise-induced BP fluctuations and seasonal environmental changes. Awareness of the participants regarding BP management was also assessed using questionnaires. There were strong correlations for systolic BP (SBP) between summer and winter (r = 0.93 in summer; r = 0.88 in winter). Although the mean difference for the SBP was higher in winter than in summer (3.1 {+/-} 11.2 mmHg vs. 0.2 {+/-} 9.4 mmHg), the values remained within a clinically acceptable range for personal monitoring. Notably, 72.7% of participants reported that the ease of using the fingertip-based device significantly increased their awareness and motivation for daily BP management. This study confirms the feasibility of cuffless fingertip-based continuous BP monitoring across different seasons, including in winter. By overcoming the seasonal limitations, this device fills a critical gap in the Japanese health-monitoring market. Our findings support the development of smaller and more portable models, representing a shift from traditional "snapshot" cuff measurements to continuous and integrated lifestyle monitoring for older adults.

Hypermobile Ehlers-Danlos Syndrome (hEDS) is a genetic connective tissue disorder characterized by hypermobile joints, chronic pain, fatigue, brain fog, orthostatic intolerance, and GI symptoms and dysmotility. Its heterogeneous presentation contributes to poor quality of life, inappropriate interventions, and prolonged diagnostic delays, often up to 10 years. This study primarily aimed to determine if physiological signals captured by a medical-grade wrist wearable could characterize autonomic patterns in hEDS and relate them to symptoms. Individuals with hEDS (n=30) and healthy controls (n=28) wore a medical grade smartwatch for 30 days, collecting continuous heart rate variability, activity, oxygen saturation, and blood pressure, alongside initial baseline symptom and quality-of-life surveys. Individuals with hEDS showed greater instability and variability in both systolic and diastolic blood pressure as well as the HRV metric LF/HF ratio, in comparison to healthy controls (p-values: 0.04, 0.02, 0.02). During sleep, metrics of parasympathetic activity (HRV measures: HF power, pNN50, RMSSD) trended lower in hEDS than healthy in comparison. As expected, survey domains assessing physiologic symptoms and quality-of-life were significantly worse in the hEDS cohort (p-values < 0.05). Notably, autonomic metrics correlated with GI symptoms in the hEDS cohort (Spearman's {rho} range: 0.38-0.60), and psychological symptoms in the healthy cohort (Spearman's {rho} range: -0.47-0.41). Principal component analysis (PCA) of physiologic and symptom features clearly separated groups, supporting distinct physiologic profiles. Combination of GI symptom index and wearable monitoring show promise as a hybrid screening approach that could substantially shorten the time to diagnosis in this population.

Background: Continuous adhesive patch electrocardiographic (ECG) wearables are increasingly prescribed. Patient experience with these devices can influence adherence, but research in this area is limited. This study aimed to explore the perceptions and experiences of patients receiving wearable cardiac monitoring technology as part of their routine care through the lens of treatment burden. Methods: This was a qualitative study with semi-structured phone interviews conducted between February and May 2024. We recruited participants from primary care and outpatient clinics using maximum variation sampling to ensure diversity in sex, ethnicity, and education levels. Interviews were audio-recorded, transcribed, and analysed using reflexive thematic analysis. Results: Sixteen participants (mean age 51 years, 63% female) were interviewed (average duration: 33 minutes). Three themes were developed: 1) ?Experience using the device: Burden vs Ease of Use?, which captured participants? perceptions of how easily they could integrate the device in their daily lives; 2) ?Individual variability in responses to ECG self-monitoring? covered participants? emotional and cognitive response to knowing their heart rhythm was monitored; and 3) ?The care process shapes patient experiences? reflected support preferences during the set-up and monitoring period and the uncertainty regarding timely clinical and device feedback. Conclusions: Patients valued cardiac wearables for facilitating diagnosis and felt reassured knowing they were clinically monitored. However, gaps in information provided to patients seemed to cause anxiety for some participants. These concerns could be mitigated through clearer clinician communication and patient education at the time of prescription.

Background Narcolepsy is a rare, lifelong neurological disorder that often begins in childhood or adolescence. Diagnosis is frequently delayed because current diagnostic testing relies on specialist in-patient sleep investigations: overnight polysomnography (PSG) followed by a multiple sleep latency test (MSLT), interpreted according to International Classification of Sleep Disorders criteria (ICSD-3-TR). These investigations are expensive, labour intensive, and available in a limited number of centres, contributing to delays and inequity of access. Automated analysis of sleep-stage probabilities (hypnodensity) using neural networks has shown promising diagnostic performance in research cohorts but still requires hospital-based PSG acquisition. The Dreem 3 headband (DH) is a comfortable, dry-montage EEG device designed for home use. Combined with its proprietary machine learning classification of sleep stages, it may offer accurate ambulatory sleep physiology assessments and support clinical decision making. Methods This was a single-centre, prospective, observational study recruiting 60 participants aged 10 to 35 years undergoing investigation for hypersomnolence within GSTT sleep services and scheduled for PSG and MSLT as part of routine care. Exclusion criteria included physician-diagnosed medical or psychiatric disorder that could independently account for excessive daytime sleepiness; and/ or regular use of prescribed or recreational medication known to affect sleep architecture. Participants first wore the DH at home for five weeknights, followed by a continuous 48-hour weekend recording using two devices in rotation. They then underwent routine in-patient PSG and MSLT. PSG and MSLT were interpreted according to ICSD-3 by an experienced sleep physician and a final diagnosis determined by a sleep physiology consultant. The primary outcome is accuracy of ambulatory DH-based assessment of sleep physiology and subsequent diagnosis of sleep disorders. We evaluate proprietary and in-house developed machine learning methods to detect SOREM epochs and predict narcolepsy diagnosis from PSG, PSG+MSLT and DH data. All algorithmic outcomes will be compared to clinical outcomes derived from current clinical standard of care. Discussion This study will provide proof-of-concept evidence for a home-based wearable EEG approach to narcolepsy diagnosis. Patient and public involvement work with young people with confirmed narcolepsy indicates high acceptability of the DH protocol: in a survey of ten young people, eight reported they would be willing to wear a sleep headband nightly at home for five days (two were unsure), and seven reported they would be willing to wear it continuously for 48 hours over a weekend (two were unsure; one said no). These findings informed the decision to restrict continuous wear to the weekend, reflecting feedback that daytime wear during school or work hours would be unacceptable. If validated, this approach could reduce delays to diagnosis, improve equity of access, and support development of a subsequent multicentre study. Trial registration IRAS Project ID: 321547. Registered October 2022. Recruitment was completed on 30 January 2026.

This study presents RED-RHD, a machine learning methodology for early detection and classification of Rheumatic Heart Disease (RHD) using heart sound recordings. By leveraging OpenL3 deep acoustic embeddings, cloud-based workflows, and an ensemble of SVM and XGBoost classifiers, RED-RHD achieves an average precision of 95.62% for murmur detection (Normal vs. Abnormal) and 99.00% precision for systolic vs. diastolic murmur classification, demonstrating marked improvements over prior methods with poor cross-dataset generalization (e.g., specificity as low as 4.3% in ResNet-based approaches). These results confirm the systems robustness across diverse, noisy clinical datasets. Additionally, we introduce a novel dynamic adaptive model selection mechanism that enables the framework to automatically select the most appropriate pretrained machine learning model based on extracted heart sound features, optimizing prediction accuracy for different regional or demographic populations. By incorporating this adaptive intelligence, RED-RHD addresses population variability and supports precision diagnostics in globally diverse patient groups, advancing the potential for scalable, AI-driven auscultation in low-resource environments.

Study ObjectivesTo evaluate wearable sleep staging across sleep apnea severity, including very severe sleep apnea defined as an apnea-hypopnea index (AHI)[&ge;] 50 events/h, and to assess how training-set composition affects performance in this subgroup. MethodsWe analyzed 552 overnight recordings, 318 from the Sleep Lab Dataset and 234 from the Hospital Dataset. In the Hospital Dataset, 26.5% had very severe sleep apnea. We developed a deep learning model for sleep staging using RR intervals from wrist-worn photoplethysmography and three-axis accelerometry. Baseline performance was assessed by cross-validation under 5-stage and 4-stage staging. We examined night-level associations with AHI severity. We also compared the baseline model with an ablation model trained on the same number of recordings but with more Sleep Lab Dataset and lower-AHI Hospital Dataset recordings, evaluating both models in the very severe subgroup. ResultsIn 5-stage classification, Cohens kappa was 0.586 in the Sleep Lab Dataset and 0.446 in the Hospital Dataset. Under 4-stage staging, the gap narrowed, with kappa values of 0.632 and 0.525, respectively. In the Hospital Dataset, performance declined with increasing AHI severity. Among 62 recordings with very severe sleep apnea, reducing high-AHI representation in training lowered kappa from 0.365 to 0.303. ConclusionsWearable sleep staging performance declined across greater sleep apnea severity in this clinical cohort. Clinical utility may benefit from training data that better represent the target severity spectrum and from selecting staging granularity to match the intended use case. Statement of SignificanceRepeated laboratory polysomnography is impractical for long-term sleep apnea management. Wearable sleep staging could support scalable monitoring, yet its reliability in clinically severe sleep apnea has remained unclear. This study developed and evaluated a wearable sleep staging approach in both sleep-laboratory and hospital cohorts. The hospital cohort included many severe and very severe cases. Performance was lower in the hospital cohort and declined with greater sleep apnea severity. A coarser staging scheme reduced the gap between cohorts, and models trained without representative very severe cases performed worse in this target population. These findings highlight the value of severity-aware model development and motivate future multi-night home validation with reliability cues.

Synchrony is a key mechanism that builds up the foundations of human interactions. Quantifying the level of physiological synchronization that occurs during dyadic exchanges is essential to fully comprehend social phenomena. We present a new index to characterize the coupling of complex physiological dynamics: the optimized Multichannel Complexity Index (opMCI). We validated this approach using synthetic time series of two coupled Henon Maps, with four different coupling levels in unidirectional and bidirectional manners. We demonstrated that the opMCI method allows to effectively discern between all coupling levels. Then, we applied the opMCI metric on heart rate variability data collected from 37 parent-infant dyads, during shared reading and playing activities, in the framework of the Shared Emotional Reading (SHER) project, with the aim of assessing the effects of early intervention in preterm babies. Two groups presented preterm infants: an intervention group, who participated in a two-month shared reading program, and a control group, who practiced shared play activities. A full-term group provided additional control data. The opMCI values were significantly higher for the intervention dyads with respect to the other groups during the shared reading task, showing that an early reading intervention program could increase parent-infant synchrony in preterm babies.

Structured AbstractO_ST_ABSObjectiveC_ST_ABSTo facilitate the scalability of EEG research, this paper compares the data quality and evaluates the absolute agreement of EEG features between laboratory and clinic settings. MethodsResting state EEG recordings were obtained from 36 participants (11 infants, 10 children, and 15 adults) from the waiting room of a primary care clinic and a laboratory. Intraclass correlation coefficients (ICC(2,1)) quantified the absolute agreement between laboratory and clinic settings for periodic power bands, alpha peak characteristics, and aperiodic components. The mean absolute difference (MAD) between laboratory and clinic recorded EEGs were calculated to describe signal consistency across settings. ResultsMore components were rejected from clinic-recorded EEGs, though data quality otherwise did not differ between settings. The ICC (2,1) for all EEG measures were generally in the good-to-excellent range across ages and regions of interest. The MAD decreased with age and was largest in the alpha frequency range. ConclusionsHigh quality EEG data can be collected from outpatient clinic settings among infants, children, and adults. There is high reliability in the parameterized periodic and aperiodic EEG features between laboratory and clinic settings. SignificanceFuture research may collect EEG datasets from naturalistic settings with confidence in their reliability relative to laboratory recordings.