Physiological Measurement

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.

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.

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.

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.

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.

Background: For decades, cardiovascular physiology has been built on the assumption that arterial baroreceptors adjust heart rate (HR) to maintain a defined blood pressure set point. We challenge this paradigm fundamentally. Blood pressure and heart rate both change substantially in response to physiological stress and neither returns reliably to a fixed baseline value. This raises the question of whether a higher-order variable, one that remains stable while blood pressure and heart rate reset freely might better represent a truly defended, set-point quantity. Hypothesis: We hypothesized that the coefficient of variation of the instantaneous baroreceptor gain (IBS CV), expressed as the change in R-R interval per unit change in systolic blood pressure (SBP), is invariant across different physiological challenges. If IBS CV is fixed, then HR and SBP must vary proportionally, maintaining a stable gain relationship even as each changes in magnitude. Methods: To test this hypothesis, we had healthy adult volunteers undergo either the cold pressor test or passive orthostatic challenge. HR, SBP, IBS, and the coefficients of variation (CV, i.e. standard deviation / mean value) of each were measured at baseline and during each stress perturbation. Results: During orthostatic challenge, HR rose significantly while SBP fell significantly. Classically, this HR rise is attributed to baroreflex compensation for falling pressure. However, the critical observation is that SBP was not restored to baseline. Instead, it remained substantially reduced while HR stayed persistently elevated and HR CV increased significantly. A system primarily defending a blood pressure set point should augment baroreflex gain and suppress pressure variability; instead mean IBS showed no significant change, SBP CV amplified more than threefold, and IBS CV was unchanged. During the cold pressor test, both HR and SBP rose simultaneously, which is inconsistent with a pressure-defending system that would have suppressed HR in response to the large rise in SBP. IBS CV was also stable across this perturbation while SBP CV amplified dramatically. Conclusion: These findings challenge the classical baroreceptor set-point model and suggest that IBS CV, not blood pressure, is the primary regulated cardiovascular variable. Furthermore, IBS CV is likely to prove to be a more sensitive marker than blood pressure or heart rate variability for risk stratification in patients with hypertension, heart failure, or autonomic insufficiency.

Aims: Explainable advanced electrocardiography (A-ECG) can be used to estimate heart age from the standard 12-lead ECG. A-ECG heart age gap (HAG) represents the difference between A-ECG heart age and chronological age. Increased A-ECG HAG is associated with cardiovascular outcomes and can be used to communicate risk. The aim was to investigate whether A-ECG heart age demonstrates acceptable within- and between-session reproducibility. Methods: Healthy adults (n=42, age 23+/-4 years, 52% male) attended up to two sessions ~14 days apart, with 36 participants completing both sessions. During each session, five standard resting 12-lead ECGs were obtained while lying in the supine position with unchanged electrode positions. A-ECG heart age was extracted using dedicated software. Within-session reproducibility was assessed using all five recorded ECGs with coefficient of variation (CV) and a two-way random effects intraclass correlation coefficient (ICC). Between-session reproducibility was assessed using the first recorded ECG of each session with a paired t-test, CV and ICC. A further analysis assessed the reproducibility of the parameters used in the A-ECG heart age regression model. Results: A-ECG heart age showed excellent within-session reproducibility in session one and two (both CV 5.8%, ICC 0.99). A-ECG heart age was slightly lower in session one than two (24.0+/-7.5 vs. 25.5+/-7.8 years, p=0.04) and showed good between-session reproducibility (CV 8.3%, ICC 0.84). All but one parameter used to estimate A-ECG heart age showed acceptable within- and between-session reproducibility (CV<10%). Conclusion: A-ECG heart age demonstrates excellent within-session reproducibility and good between-session reproducibility in healthy young adults.

General anesthetics enable invasive experimentation but can affect cardiovascular and respiratory physiology, biasing preclinical outcomes. We compared five anesthetic regimens in adult male Wistar rats, tribromoethanol (TBE, 250 mg/kg i.p.), chloral hydrate (CH, 400 mg/kg i.p.), ketamine-xylazine (KX, 80/10 mg/kg i.p.), thiopental (TP, 80 mg/kg i.p.), and isoflurane (ISO, 4% induction, 2% maintenance), to investigate integrated cardiorespiratory and biochemical markers. Femoral arterial catheterization allowed continuous blood pressure (BP) and derived heart rate (HR) recordings, while ventilation was assessed through pletysmography at baseline (awake), during induction, and recovery phases of anesthesia. Variability was evaluated in the time and frequency domains, including HR, systolic blood pressure (SBP), and spontaneous baroreflex sensitivity. In an independent cohort of rats, butyrylcholinesterase (BChE), CK-MB, cTnI, and LDH were measured. Baseline BP was unchanged by TBE and TP, whereas all anesthetics affected HR. Minute ventilation and breathing frequency were reduced with all agents, while tidal volume decreased with KX and TBE only. LDH and cTnI were unaffected, BChE was reduced by KX, TBE, and ISO, and CK-MB increased with CH and KX. Variability analysis showed that all anesthetics depressed pulse-interval and SBP variability and shifted spectral power toward higher frequencies, while baroreflex sensitivity and effectiveness were consistently reduced. During recovery, KX and TP restored most variability indices, whereas CH, TBE, and ISO showed persistent suppression. These findings highlight distinct profiles of cardiovascular depression and biomarker responses across anesthetics and underscore the importance of accounting for autonomic variability when selecting different anesthetics in experimental protocols. HighlightsO_LIFive anesthetic regimens were tested in rats. C_LIO_LIAll anesthetics reduced ventilation, and KX and TBE also reduced tidal volume. C_LIO_LICH and KX increased CKMB, while KX, TBE and ISO reduced BChE. C_LIO_LIAll anesthetics reduced blood pressure variability and baroreflex sensitivity. C_LIO_LIVariability recovered with TP and KX, whereas CH, TBE and ISO showed persistent suppression. C_LI

Background. Brain development is altered in neonates with congenital heart disease (CHD). However, development in the perioperative period remains incompletely understood. Purpose. This study used Structural Covariance Component (SCC) analysis to identify brain regions showing spatial patterns of coordinated expansion and contraction that differ between neonates with CHD after cardiac intervention and healthy controls, as well as pre-to postoperative changes and effects of perioperative risk factors. Study type. Prospective. Population. The cohort included 41 neonates with CHD who underwent cardiac surgery or catheterization and 359 healthy neonates. Field strength and sequence. 3 Tesla T2-weighted turbo-spin-echo sequence. Assessment: Brain MRI were motion-corrected and reconstructed using an established neonatal algorithm. Jacobian determinants calculated from non-linear registration of MRI to a neonatal template were input into an Independent Component Analysis to identify SCCs (N=40). SCC weightings were extracted, reflecting the degree to which the pattern of covariance is expressed in each neonate. Statistical tests. Postoperative SCC weightings were compared to healthy neonates using a general linear model or robust regression. Pre- and postoperative SCC weightings were compared using a linear mixed effect model. Pre- to postoperative differences were calculated and associations with age at surgery, cardiopulmonary bypass duration, and postoperative paediatric intensive care unit stay were assessed using partial spearman's rank correlation. Analyses were adjusted for covariates and corrected for multiple comparisons using False Discovery Rate. Results. 16/40 SCCs showed significant differences between neonates with CHD after surgery and controls, including white matter, cortical- and deep grey matter, brainstem, and CSF regions, with seven also showing significant perioperative change. A further nine SCCs only showed significant perioperative change. Perioperative risk factors were not associated with perioperative change. Data conclusion. This data-driven approach highlights region-specific postoperative alterations and perioperative changes in brain morphology of neonates with CHD. Evidence level. 1. Technical Efficacy. Stage 3.

BackgroundChronic gastroduodenal symptoms are challenging to diagnose and treat. Body surface gastric mapping provides non-invasive biomarkers of gastric function, but the requirement of a standard meal for postprandial assessment can be difficult for severely symptomatic patients. AimsTo assess the impact of reduced meal sizes and fasting on body surface gastric mapping metrics to determine clinical interpretability under non-standard nutritional loads. MethodsHealthy controls (n=60) underwent a 4.5-hour Gastric Alimetry test. Three age, sex, and BMI-matched groups (n=20 each) were compared: Standard Meal (482 kCal), Nutrient bar + Water (250 kcal), and Fasted (no meal). Principal Gastric Frequency, Gastric Alimetry Rhythm Index, BMI-Adjusted Amplitude, and fed:fasted Amplitude Ratio were analyzed against normative intervals. ResultsMeal status significantly affected amplitude-based metrics; the Standard Meal group exhibited higher BMI-Adjusted Amplitude (p<0.001) and fed:fasted Amplitude Ratio (p=0.001) than Fasted and Bar + Water groups. Frequency and rhythm-based metrics were resilient; Principal Gastric Frequency (p=0.245) and Gastric Alimetry Rhythm Index (p=0.336) showed no significant differences across conditions. While amplitude deviations were common in the Fasted group (20% fell below the normative range), Gastric Alimetry Rhythm Index and Principal Gastric Frequency remained within normal reference ranges for 95% of participants across all conditions. ConclusionsWhile consuming <50% of the standard meal significantly reduces gastric amplitude, gastric rhythm remains stable. Principal Gastric Frequency and Gastric Alimetry Rhythm Index function as reliable biomarkers of gastric myoelectrical function regardless of nutritional state.

Background Clinical prediction models degrade when deployed across hospitals, yet retraining requires technical expertise, labeled data, and regulatory re-approval. We investigated whether post-hoc retrieval augmentation of a frozen model's output, analogous to retrieval-augmented methods in natural language processing, can mitigate this degradation without any parameter modification. Methods We developed the Post-hoc Retrieval Augmentation Module (PRAM), which combines predictions from a frozen base model with outcome information retrieved from similar patients in a local patient bank. Five base models (logistic regression through CatBoost) and three retrieval strategies were evaluated on 116,010 ICU patients across three databases (MIMIC-IV, MIMIC-III, eICU-CRD) for acute kidney injury (AKI) and mortality prediction. A bank size deployment simulation modeled performance from zero to full local data accumulation, complemented by source bank cold start, stress tests, and calibration experiments. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results Retrieval benefit was inversely associated with base model complexity ({rho} = -0.90 for AKI, -1.00 for mortality): simpler models benefited more, consistent with retrieval capturing residual signal unexploited by the base model. PRAM showed a statistically significant monotone dose-response between bank size and prediction performance across all six outcome-target combinations (Kendall {tau} trend test, q = 0.031 for all). At the pre-specified primary comparison (bank = 5,000), the improvement was confirmed for the two largest-shift settings (eICU-CRD AKI: {Delta}AUROC = +0.012, q < 0.001; eICU-CRD mortality: {Delta}AUROC = +0.026, q < 0.001). Pre-loading a source bank bridged the cold-start gap, providing an immediate performance gain equivalent to approximately 2,000 to 5,000 local patients. Conclusions PRAM provides a parameter-free adaptation mechanism that requires no model retraining, gradient computation, or regulatory re-evaluation at the deployment site. Effect sizes were modest and did not reach cross-model superiority, but the consistent dose-response pattern and the absence of retraining requirements establish retrieval-based adaptation as a viable approach for clinical model transportability. The retrieval mechanism additionally opens a pathway toward case-based interpretability, where predictions are accompanied by identifiable similar patients from the deploying institution.

ObjectiveLong term monitoring of obstructive sleep apnea (OSA) severity may be relevant for several clinical applications. We developed a method for estimating the apnea-hypopnea index (AHI) using wrist-worn, reflective photoplethysmography (PPG). ApproachA neural network was developed to detect respiratory events using PPG and PPG-derived sleep stages as input. The development database encompassed retrospective data from three polysomnographic datasets (N=3111), including a dataset with concurrent reflective PPG recordings from a wrist-worn device (N=969). The model was pre-trained with (transmissive) finger-PPG signals from all overnight recordings and then fine-tuned to wrist-PPG characteristics using transfer learning. Validation was performed on the test portion of the development set and on a fourth, external hold-out dataset containing both wrist-PPG and PSG data (N=171). Performance was evaluated in terms of AHI estimation accuracy and OSA severity classification. Main ResultsThe fine-tuned wrist-PPG model demonstrated strong agreement with the PSG-derived gold-standard AHI, achieving intra-class correlation coefficients of 0.87 in the test portion of the development set and 0.91 in the external hold-out validation set. Diagnostic performance was high, with accuracies above 80% for all severity thresholds. SignificanceThe study highlights the potential of reflective PPG-based AHI estimation, achieving high estimation performance in comparison with PSG. These measurements can be performed with relatively comfortable sensors integrated in convenient wrist-worn wearables, enabling long-term assessment of sleep disordered breathing, both in a diagnostic phase, and during therapy follow-up.

AO_SCPLOWBSTRACTC_SCPLOWDecoding imagined speech from electroencephalography (EEG) recordings is potentially useful for brain-computer interfaces. Previous studies have focused on decoding semantic information from EEG, leaving the decoding of emotion - an important component of human communication - largely unexplored. Here, we report two experiments involving participants tasked with overt (n = 14) or imagined (n = 21) emotional vocalisation in five different categories: anger, happiness, neutral, sadness, and pleasure. Throughout, we recorded 64-channel EEG; we computed time-frequency features and used a logistic-regression classifier to evaluate emotion decoding accuracy. In five participants, we also recorded facial surface electromyography (sEMG) during imagined vocalisation, and studied the contamination of EEG by sEMG. Our results show that emotion can be decoded from single-trial EEG recordings of both overt (78.1%, chance = 20%) and imagined vocalisation (36.4%). The high-gamma band (50 to 100 Hz) and lateral EEG channels (T7, T8, and proximal) were important for decoding. sEMG analysis indicated that involuntary facial muscle activity contributed to these spectral and spatial patterns during imagined vocalisation, especially during happy vocalisations. We conclude that involuntary facial muscle activity is associated with certain emotion categories (i.e., happiness), and drives above-chance decoding of emotion from single-trial EEG recordings of imagined vocalisation.

Objectives: Heart failure is a leading cause of mortality, necessitating identification of patients at increased risk needing intervention. In this study, we investigated if Fitbit data can reveal physiological trends associated with hospital visit risk. Materials and methods: Individuals with heart failure (n=249) were randomized into three arms for prospective 180-day monitoring. All arms received a Fitbit and wireless weight scale. Arm 1 received devices only; Arm 2 received a mobile app with surveys; Arm 3 received the app plus financial incentives. Results: 51 participants had hospital visits during the study period. These individuals took fewer steps (p=.002) and reported increased symptom severity (p=.044). Resting heart rate increased three days prior to a visit (p=.022). Baseline steps revealed a higher visit probability for less active participants (p=.003). Discussion and conclusion: Passive physiological monitoring can effectively identify individuals at risk of health exacerbation, demonstrating the potential of wearable devices for timely clinical intervention.

Purpose: Early recognition of deterioration in patients with suspected infection at the emergency department (ED) is important. Current clinical scoring systems show limited discriminative performance for early deterioration. Continuous electrocardiogram (ECG) recordings may offer additional dynamic physiological information that can enhance early prediction of deterioration in patients with suspected infection. Methods: We developed a multimodal, ECG-derived spectrogram-based pipeline to predict deterioration within 48 hours of ED admission. We used the first 20 minutes of ECG recordings for the spectrograms. We compared the model with the National Early Warning Score (NEWS), quick Sequential Organ Failure Assessment (qSOFA), a baseline model with vital parameters, sex, and age, and a Heart Rate Variability (HRV) derived model. Results: In this study, 1321 patients were included, of whom 159 (12%) deteriorated. The multimodal model combining baseline data with spectrograms showed the best overall performance, with an Area Under the Receiver Operating Characteristic (AUROC) of 0.788, followed by the baseline model (age, sex, triage vitals) alone, with an AUROC of 0.730. The HRV-only model and the qSOFA showed the lowest performance (AUROC 0.585 and 0.693, respectively). Conclusion: This study shows that ECG-derived multimodal spectrogram models outperform those based solely on vital signs and HRV features, as well as established clinical scores such as NEWS and qSOFA. Spectrogram analysis represents a promising approach to enhance early risk stratification and support clinical decision-making for patients with suspicion of infection in the ED.