Physiological Measurement

The features of the PPG (photoplethysmography) morphology are known to reflect age-related cardiac and vascular changes. In most contemporary wearables, PPG signals are acquired from distal sites such as the wrist and finger. The superficial temporal artery (STA), accessible at the temple region, is reached via a shorter arterial path from the aortic root than the radial circulation, and may therefore carry hemodynamic and aging information with less distance-dependent attenuation. We hypothesized that the morphology of the PPG at temple region (STA) would show stronger and more numerous age correlates than the PPG at the wrist. To test this, we extracted a common set of 89 pulse-morphology features, spanning raw-waveform timing/amplitude/area measures, ratios among them, derivative-based ratios, and spectral harmonic-ratio features. We compared an in-house temple-worn device which has PPG as one of the sensors, with a publicly available Microsoft Aurora-BP wrist-worn PPG dataset, and tested each feature's association with age. We identified 14 robust age correlates at the temple region, compared to 3 at the wrist. The temple's correlates spanned multiple morphological categories and showed a larger age-association than at the wrist. These results support the hypothesis that the temple region may be a more robust PPG measurement site than the wrist to extract age-related cardiovascular information, which motivates further investigation of temple-based cardiovascular sensing.

Coronary flow waveforms have a distinct diastolic-dominant shape with periods of low or retrograde flow during systole. While the general waveform shape has been attributed to complex interactions between cardiac and vascular mechanics, there is limited research into the variability in coronary flow waveforms and what this variability may reveal about cardiac function. This work presents a shape analysis of left anterior descending artery (LAD) flow waveforms using Fourier transforms and Singular Value Decomposition (SVD) performed on baseline data collected from 32 pigs. Pigs included in the study reflect two breeds (Ossabaw and Yorkshire) and three different experimental conditions (lean-control, lean-paced, and obese-paced). Fourier transforms were used to decompose the waveforms into 15 harmonics for each pig. An SVD analysis is then used to extract temporal patterns of the waveforms. Correlations between pig-specific coefficients for the SVD modes and clinical metrics were used to investigate physiological explanations of LAD waveform variability. Temporal LAD flow patterns of the second SVD mode are significantly correlated with heart rate. The third SVD mode significantly correlates with mean blood pressure and maximum hyperemic flow. Furthermore, the fourth SVD mode is weakly correlated with left-ventricular end diastolic pressure and endocardial-epicardial flow ratios. This work demonstrates that LAD flow waveforms can be broken down into temporal patterns that correlate with physiological features. Furthermore, this shape-analysis method allows for waveform reconstruction and simplifies visualization of the temporal patterns identified using SVD, an advantage over existing methods that focus on characterizing flow waveforms by points of interest.

Conventional electrocardiography is highly effective for waveform and rhythm diagnosis, but it is less suited to showing how the internal shape of hundreds or thousands of consecutive heartbeats changes over time. We introduce FOXTAIL, a complementary view that represents each cardiac cycle as an ordered sequence of changes in signal direction. Overlaying these sequences in a fixed visual field makes beat-to-beat organization visible and allows the density, size, stability, and scale persistence of those changes to be measured. We evaluated the representation in recordings containing normal sinus rhythm, paroxysmal atrial fibrillation, severe heart failure, ventricular tachyarrhythmia, and controlled electrode-motion noise. Paired recordings showed that FOXTAIL descriptors can reveal within-person state changes that are not conveyed by a single average beat. The noise and pre-fibrillation analyses also showed that a dense event pattern is not automatically equivalent to physiological complexity, measurement artifact, or impending disease. FOXTAIL is therefore not proposed as a replacement for the diagnostic ECG or as a new classifier, but as an observation and measurement domain for asking a more basic question: how is the electrical organization of the heart changing from one beat to the next, and which of those changes persist across scale?

Background: Mortality prediction models often combine early electronic health record data, but the relative prognostic value of baseline vulnerability, physiological severity, treatment exposure, and procedure burden remains unclear. Objective: To compare routinely available first-24-hour clinical domains for visit-level 30-day mortality prediction and assess whether domain-level patterns replicated in MIMIC-IV. Methods: We used CHoRUS, an OMOP-formatted acute-care dataset, with independent domain-level replication in MIMIC-IV. CHoRUS included 22,098 visits among 5,892 unique patients, with 1,004 30-day mortality events and 4.5% mortality prevalence. MIMIC-IV included 23,000 acute-care visits among 10,006 unique patients, with 819 events and 3.6% prevalence. Across both datasets, 45,098 visits and 15,898 unique patients were analyzed. Predictors were restricted to the first 24 hours after visit start. Performance was evaluated using AUPRC, AUROC, Brier score, calibration, sensitivity at 90% specificity, highest-risk 10% analyses, decision-curve analysis, and SHAP summaries. Because 30-day mortality was infrequent, the classification task was class-imbalanced. Accordingly, AUPRC was interpreted relative to the prevalence-based no-skill baseline, rather than as an absolute measure alone. Results and Conclusion: Physiological severity produced the largest improvement beyond baseline in CHoRUS, with median AUPRC 0.38 and median AUROC 0.86, and showed the same primary domain-level pattern in MIMIC-IV. Treatment exposure and procedure burden provided smaller gains. In CHoRUS, the best pairwise model combined baseline, physiological severity, and procedure burden features, with median AUPRC 0.41; the all-domain model was slightly lower, with median AUPRC 0.40 and median AUROC 0.86. In MIMIC-IV, the all-domain model had the highest median AUPRC, 0.25, only modestly above the best pairwise model. First-24-hour physiological severity features therefore provided the most consistent prognostic information across datasets, supporting parsimonious, clinically interpretable acute-care risk models centered on high-quality early physiological data.

Background: While critical care pharmacists (CCPs) are broadly associated with improvements in outcomes for critically ill patients, operationalizing staffing in the intensive care unit (ICU) requires further study. The purpose of this evaluation was to determine the relationship of a CCP on interprofessional rounds for weekday admissions of ICU patients on patient-centered outcomes. Methods: This post-hoc analysis of the Optimizing Pharmacist-Team Integration for ICU Patient Management (OPTIM) study included adults admitted to an ICU on a weekday in the multicenter observational study. The primary outcome was in-hospital mortality. The primary exposure was level of comprehensive medication management (CMM) during the first 24 hours of ICU stay. A secondary exposure was pharmacist-to-patient ratio. Multivariable generalized estimating equations (GEE) were used to estimate associations between mortality and patient, ICU, and institution variables. Fine-Gray sub-distribution hazards regression estimated hazard of discharge alive (HDA) from the ICU and hospital and hazard of extubation alive. Results: 21,835 patients met inclusion criteria, and 76.1% of patients had CMM delivered on interprofessional rounds. Patients who had no CMM on the first ICU day had an increased risk of mortality of 23% (Odds Ratio (OR) 1.23, 95% Confidence Interval (CI) 1.04-1.46, p=0.02) compared to those who received CMM on interprofessional rounds. Patients with no CMM also had decreased HDA from the ICU and hospital and decreased hazard of extubation alive. No difference was seen in any outcomes when comparing other levels of CMM (CMM delivered outside of interprofessional rounds or abbreviated CMM) compared to CMM delivered on rounds. Conclusions: Absence of pharmacist CMM on the first day of ICU stay for patients with weekday admission was associated with an increased risk of in-hospital mortality, but no difference was seen in other levels of CMM: this signal supports further investigation in prospective analysis.

Background: Machine learning models leveraging electronic health records (EHRs) can support earlier detection of sepsis in intensive care units (ICUs). However, their clinical utility depends on reproducibility across institutions and patient populations. Building on a published pipeline from the Children's Hospital of Philadelphia (CHOP), this study examines how a neonatal sepsis prediction framework performs and can be adapted to a range of intensive care environments, paediatric, cardiac, and neonatal, at Great Ormond Street Hospital (GOSH). Methods: We extracted de-identified ICU EHR data from GOSH and applied feature derivation, unit harmonisation, and temporal sampling to align with the CHOP dataset used by Masino et al. (2019). Seven classifiers were first evaluated using CHOP-trained weights to characterise cross-domain behaviour and then retrained on local data to assess recoverability and site-specific adaptation. Model discrimination was summarised by AUC and F1, and learning curves were used to explore sample efficiency and bias-variance dynamics. Results: Models achieved strong discrimination on the CHOP neonatal cohort but demonstrated reduced performance when transferred to the mixed GOSH ICU population, reflecting anticipated domain and population shift. Retraining on GOSH data restored discrimination (AUC range 0.69-0.86), with Gradient Boosting (AUC 0.86 vs AUC 0.87 at CHOP) and KNN (AUC 0.80 vs AUC 0.79 at CHOP) models performing comparably to their CHOP benchmarks. DeLong's test confirmed statistically significant gains across all classifiers (p < 0.001). Conclusion: ICU cohort and baseline demographic differences between CHOP and GOSH introduced domain shift that limited direct model transfer. Elements of the original preprocessing pipeline could not be reproduced, further constraining transportability. Yet, retraining on local data restored high discrimination, showing that the modelling framework remains robust when re-estimated in new settings. These results highlight local adaptation as a practical route to recover performance and support safe, generalisable deployment of clinical prediction models in mixed clinical environments.

Neural temporal point processes (NTPPs) are powerful tools for modeling sequences of timestamped events with statistical temporal structure. Density-based NTPPs, in particular, are an interesting opportunity to merge the universal function approximation capability of neural networks with a defined statistical model in a way that has many potential applications. We demonstrate one such application to heartbeat dynamics, a physiologic point process. We specifically apply a lognormal mixture NTPP to compute instantaneous estimates of the mean and standard deviation of beat-to-beat intervals. We compare our results to the state of art (Barbieri et al.) point process model for heartbeat dynamics, which uses a more physiologically rigorous inverse Gaussian model. We find that the NTPP model maintains reasonable accuracy while improving upon robustness to noise.

Background Limited heart rate rise contributes to reduced exercise tolerance for people who have heart failure with reduced ejection fraction (HFrEF), yet rate-adaptive pacing does not improve functional capacity due to an attenuated force-frequency relationship (FFR). How the FFR relates to total peripheral resistance and sympathetic tone in HFrEF is unknown. Methods In a prospective, observational study, participants with HFrEF and controls underwent an incremental pacing protocol, during which heart rate was increased from 50 to 140 beats per minute. At each heart rate increment LV contractility was measured by echocardiography to determine the FFR, as well as continuous beat-to-beat measurement of systolic and diastolic blood pressures with a plethysmography device to determine cardiac output, total peripheral resistance and blood pressure variability (BPV). A microneurography study was then conducted to measure muscle sympathetic nerve activity (MSNA) during incremental pacing. Results A total of 157 participants with HFrEF and 55 controls (mean age 71.1{+/-}1.4 years, 172 (81.1%) male) underwent the pacing protocol. We observed single units in seven of 11 participants who participated in the microneurography study. In both groups, LV contractility and cardiac output increased until the peak of the FFR, after which these declined. We observed a reduction in total peripheral resistance, blood pressure variability, MSNA frequency and incidence coinciding with the peak of the FFR, beyond which these increased. Whilst these relationships were present in both groups, they were more evident in participants with HFrEF. Conclusions For people with HFrEF there is a bidirectional relationship between heart rate and sympathetic activation, with a nadir of sympathetic tone occurring at the peak of the FFR. Both excessively low and high heart rates are accompanied by greater sympathetic activation. Taken together, these data suggest that optimal heart rate targets for HFrEF are likely to be individual.

Uncertainty quantification is proposed as a safeguard for machine-learning systems in health-related signal analysis, but an uncertainty score is useful only if it behaves as a reliability signal. Free-living wearable electrocardiogram (ECG) signal-quality assessment provides a test bed because ambiguity, artifact, and acquisition shift can alter the relationship between confidence and correctness. This study evaluates predictive uncertainty under ambiguity, controlled corruption, and external distribution shift. 32,224 non-overlapping 10-s windows of synchronised single-lead ECG and three-axis accelerometry from 15 subjects in the Brno University of Technology ECG Quality Database were analysed. Two model families were compared: multinomial logistic regression and Classification and Regression Tree (CART), each progressing from a point estimate to a fixed-structure posterior and then a structure posterior. Expected conditional entropy and mutual information were evaluated as designated aleatoric and epistemic uncertainty measures, with max-softmax uncertainty as a confidence baseline. Validation covered error ranking, selective prediction, behavioural probes, posterior structural diversity, recorded-noise stress testing, and zero-shot external transfer. The logistic structure posterior retained an expected 8.5 of nine features and concentrated on near-complete masks, yielding little additional predictive diversity. Bayesian CART produced 221 distinct complete topologies among 238 retained draws and stronger score-dependent selective-risk behaviour. Conditional entropy increased with local class overlap, whereas mutual information increased when training information was reduced, although both showed cross-sensitivity. Under recorded noise, predicted quality severity changed more consistently than uncertainty, while external transfer preserved ordinal severity more reliably than uncertainty ordering. These findings show that posterior richness alone does not establish reliable uncertainty. Model-derived uncertainty should therefore be validated against prespecified ambiguity, information, and shift probes before supporting abstention, reacquisition, or downstream decisions.

Scaling laws help determine the optimal data size for training large models but are established in domains where the target is deterministic. Physiological signals are different: heartbeat sequences are stochastic, so part of the error is irreducible even with large amounts of data. Metrics such as MAE do not account for non-deterministic behavior, and therefore assessing scaling requires evaluating distributional calibration (measuring how well predicted probability densities capture true conditional characteristics). We formulate a scaling law metric(n) = E + A n- and evaluate it with five metrics: accuracy (MAE, RMSE), distributional calibration (KS distance, goodness-of-fit), and training objective (negative log loss) using a neural temporal point process trained on a cohort of four-ECG datasets. The law fits all five metrics. While point accuracy is near saturation at n = 183, KS distance and goodness-of-fit improve by 6% and 12% respectively when extrapolated to 10,000 subjects, showing that scaling decisions in stochastic domains must be guided by distributional calibration rather than point accuracy.

Cerebral hemodynamics are difficult to monitor continuously outside the laboratory. Optical head-worn wearables have been proposed for tracking cerebral blood-flow signals, but they require comparison with an established cerebrovascular reference before they can be interpreted. We evaluated a temple-worn optical wearable, Temple, that outputs a proprietary, dimensionless Brain Flow index, intended as a proxy for relative changes in cerebral hemodynamics, against transcranial Doppler (TCD) ultrasound, which measures blood-flow velocity in the middle cerebral artery (MCAv). Twenty-three healthy adults completed two physiological challenges that elicit distinct and acute cerebral hemodynamic responses: a cycle-ergometer exercise protocol and a stand-to-supine postural transition protocol. Twenty participants were analyzed per protocol. The Brain Flow index tracked MCAv in both protocols, with significant within-subject temporal correlations (median Pearson r = 0.795 and 0.799 for exercise and postural transition; p < 0.001) and directionally concordant, statistically significant transition responses for both increases and decreases in flow. Bland-Altman analysis of the normalized transition responses showed small mean biases between the two devices, consistent with similar relative response shapes. Because both signals were standardized within session before this comparison, it addresses the shape of the relative change rather than agreement in absolute units. The Brain Flow index reproduced the direction and time course of MCAv under both perturbations, including the postural transition, where heart rate moved in the opposite direction. Further studies using complementary modalities and additional cerebrovascular reactivity challenges are required to establish clinical use cases and cerebral specificity of the Brain Flow index.

Background: Blood volume (BV) in patients with chronic heart failure (HF) is characterized by heterogeneity in volume profiles; one profile being "normal BV". While overall intravascular volume may be considered normal clinically, the relative contributions of red blood cell (RBC) mass and plasma volume (PV) may not be. Objective: Assess how normal is a "normal BV" based on quantitative measures of RBC mass and PV. Methods: Retrospective analysis was undertaken in 395 patients with Class II-III HF. BV was quantitated using indicator-dilution methodology. Cohort was stratified by normal and hypervolemic BV. Results: Of the cohort, 31% (123/395) demonstrated normal total BV and 62% (244/395) hypervolemic BV. Of patients with "normal BV", 36% (44/123) demonstrated normal RBC mass and 60% normal PV (74/123). Importantly, 60% (74/123) demonstrated a deficit in RBC mass (true anemia), while a low hemoglobin (<12 g/dL) was present in just 29% (36/123). An excess in RBC mass (erythrocytosis) in 4% (5/123). Notably, true normal BV (i.e., normal RBC mass and normal PV) was observed in only 30% (37/123) of patients with an overall "normal" intravascular volume. Conclusions: Findings reveal that "normal BV" can be misleading by concealing substantial variability in RBC mass (including unrecognized anemia and erythrocytosis) as well as different degrees of PV expansion and contraction. An actual normal BV was identified in a minority of "normal BV" patients. This underscores the importance of looking beyond overall "normal BV" to the contributing elements of RBC mass and PV with significant implications for patient management and outcomes.

Interictal epileptiform discharges (IEDs) are diagnostically important EEG abnormalities observed between seizures. This study addresses a conditional spatial-classification task where every analyzed four-second epoch had already been reviewed and confirmed by experts as containing an IED, and the model assigned that epoch to one of five predefined scalp-distribution categories (generalized, frontal, temporal, occipital, or centro-parietal). The analysis therefore does not evaluate IED-versus-non-IED detection. After preprocessing, 2,514 IED-labelled epochs were analyzed using identical stratified epoch-level partitions, SMOTE based training, 26 handcrafted features per included channel, and multiple machine-learning classifiers. A staged channel ablation compared 19-channel scalp EEG, 21-channel EEG with ECG, and the complete 29-channel input containing scalp EEG, referential, ECG, and EMG channels. The best EEG-only result was obtained with linear discriminant analysis (88.89% test accuracy). CatBoost achieved 93.25% on EEG with ECG channel and 94.44% with the whole channel set. All eight directly comparable classifiers showed numerically higher test accuracy after ECG channel was added; for CatBoost, the increase was 6.35 percentage points. In the EEG with ECG channel, CatBoost model on ECG channel on right and left arm received respectively 15.79% and 15.12% of normalized global SHAP attribution, and beta-band power was the leading of all features (18.76%). These SHAP values indicate model-specific predictive contributions and do not establish physiological biomarkers, causal autonomic mechanisms, or clinical localization. The findings support a limited methodological conclusion which is ECG-derived features were associated with improved internal epoch-level categorization of expert-confirmed IED epochs. They do not establish IED detection, artifact rejection, independent EMG effects, or generalization to unseen patients.

Cardiac tissue is primarily made up of cardiomyocytes, which are regulated by the autonomic nervous system. We have used and developed approaches such as patch clamping and electrical stimulation-combined calcium imaging, computer modeling, optogenetics and chemogenetics combining with video-based Short-Time Fourier transformation (STFT) method to study the physiological activities of cardiomyocytes. The action potential of cardiomyocytes was found to be synchronized with calcium signals, which can be grouped into two categories by STFT. A mathematical model was developed to simulate the changes in electrical activities within cardiomyocytes caused by energy depletion, especially for 2-deoxy-D-glucose (2DG) treatment. Optogenetic and chemogenetics tools, such as ChR2(H134R), OptoXR-{beta}2AR and hM3Dq accelerated beating, while GR, ACR1 and hM4Di inhibited cardiomyocytes beating. A video-based STFT method was developed to visualize the beating frequency during these manipulations. An in vitro co-culture method was developed to study the relationship between sympathetic neuronal firing and calcium dynamics in cardiomyocytes. In vivo, electrocardiograph (ECG) measurements showed that Clozapine N-oxide (CNO) caused heart rates increasement in cTnT-hM3Dq virus injected mouse. However, it had no impact on cTnT-hM4Di virus injected mouse. This study provides comprehensive methodologies for studying cardiomyocyte physiology and manipulating heart rates in vitro and in vivo.

Background. Whether predictors of intraoperative hypotension (IOH) add information beyond the mean arterial pressure (MAP) already displayed on the monitor is contested: selection bias in common evaluation designs inflates apparent performance, and the field has called for comparisons against simple MAP-based references under bias-resistant protocols. Existing predictors also depend on proprietary waveform analysis or pulse-contour monitors, restricting both deployment and external validation. Methods. Using 807 non-cardiac surgery patients from the open VitalDB database, we derived an additive gradient boosting model (one split per tree: a learned shape function per variable, no interactions) from three variables computable from an arterial line alone: current MAP, its drift from the patient's own 20-minute baseline, and the growth of its rolling variance (critical slowing down). Evaluation used patient-level 5-fold cross-validation under a strict protocol - exclusion of the 65-75 mmHg grey zone and of all samples already hypotensive at prediction time - with MAP alone (same learner class) as comparator. The frozen model was then validated, without any refitting, on an independent cohort from another continent (MOVER, University of California Irvine) following a pre-registered plan sealed before external data access. Results. In development the pressure-only model reached AUROC 0.907 vs. 0.884 for MAP alone (Delta AUROC +0.023, 95% CI +0.017 to +0.029) at 5 min, with +0.031 and +0.032 at 10 and 15 min, and good calibration (Brier skill +0.418 vs. prevalence). In external validation on 3,069 patients (442,194 samples, 1-minute charting, event prevalence 5.8%), the advantage not only transferred but was larger than in development: AUROC 0.696 vs. 0.638, Delta AUROC +0.058 (95% CI +0.051 to +0.064), meeting both pre-registered gates. Discrimination transferred; calibration did not (external Brier skill -0.014), requiring local recalibration. In the unrestricted scenario, where samples already at threshold are retained, the advantage collapsed (+0.007), reproducing the selection effect this paper documents. A secondary model adding pulse-contour cardiac output and stroke volume variation improved development discrimination further (Delta AUROC +0.035) but could be externally validated in only 39 patients, because those signals are rarely recorded. Conclusions. The dynamics of arterial pressure itself - drift from a patient-specific baseline and variance growth - carry predictive information beyond its current value, in a fully interpretable additive model that requires only an arterial line, no waveform access and no proprietary hardware. The advantage is confirmed in a pre-registered frozen-model external validation of over three thousand patients, and is largest at coarse recording cadence, where instantaneous pressure is least informative.

Background and Objectives: Preterm infants are commonly transfused, yet direct cardiorespiratory effects of red blood cell (RBC) transfusions remain poorly understood. We explored the feasibility of using multicentre electronic health data (EHD) to study such cardiorespiratory responses. Methods: Highly granular routine EHD were collected from preterm infants born <32 weeks gestational age at three European centres. Heart rate, oxygen saturation, and respiratory rate were evaluated 12 hours before and after the RBC transfusion. Results: A total of 321 transfusions in 164 infants were analysed. Overall, there was no significant change in the rate of bradycardia and apnoea following transfusion. Cardiorespiratory parameters varied substantially between infants; e.g. 20% of transfusions were associated with an unexpected, significant increase in heart rate. Respiratory rate and oxygen saturation exhibited similarly heterogenous patterns following transfusion. In sub-group analysis, the proportion of transfusions with increased heart rate was significantly higher within the first two weeks than later (32% vs 13%, p=0.0019). Conclusions: Multicentre EHD extraction allows to identify otherwise masked short-term effects of RBC transfusions on cardiorespiratory parameters, possibly indicating cardiac or pulmonary overload. Such effects may vary with adaptation to anaemia. Analysing EHD may ultimately enable personalized transfusion practice.

Offline reinforcement learning (RL) provides a promising framework for learning and evaluating treatment policies from logged clinical data, particularly in sequential decision-making settings where prospective exploration would be unsafe. In ICU sepsis management, however, it remains unclear whether offline RL policies retain stable behavior under increasingly severe out-of-distribution (OOD) patient cohorts. In this paper, we evaluate standard offline RL methods on three severity-enriched OOD test mixtures from the MIMIC-III benchmark dataset to determine whether offline policies retain a stable, actionsensitive decision-support signal. Under the shared learned-dynamics offpolicy evaluation (OPE) protocol, as the severe-OOD ratio increases from 25% to 75%, observed clinical survival declines from 67% to 49%, while the best offline method in each mixture receives model-predicted terminal survival values of 87%, 86%, and 85%, respectively. Because observed clinical survival and model-predicted terminal survival are different quantities, this contrast suggests a stable model-based decision-support signal under severity shift. We further present a secondary physiological stabilization analysis using an episode-level physiological stabilization score (EPSS), a heuristic summary of whether selected physiological variables move in favorable directions during follow-up. In this analysis, model-generated rollouts under offline policies receive higher EPSS values than matched logged clinical trajectories for several physiological components. Together, these results support learned-dynamics OPE as a useful severity-OOD stress test for offline RL policies in ICU sepsis, while leaving prospective and causal validation as necessary next steps.

Seizure diaries are one of the most useful sources of information in the management of epilepsy, however patient engagement with them can be sporadic. Sustained participation with seizure diaries affects the completeness and reliability of self-reported data, so it is vital to be able to measure engagement. To facilitate this, we create a multidimensional engagement metric with which to characterize how patients interact with their seizure diary. We utilise data from the Helpilepsy, a seizure diary application, common features found in application engagement metrics in business settings, and well understood clinical features to do this. Clustering is then performed to isolate different user groups based on how engaged they are, and these groups are studied to understand what drives the differences in engagement. We found three groups emerge from the clustering: low, medium and highly engaged users. Investigating these groups further, we put together a ``profile" for highly-engaged users. We find that they tend to be older at the point of diagnosis, and have had epilepsy for longer than the other users. We also find they tend to have had more medications, have higher doses of common anti-seizure medications, and they have more medications typically given to those with refractory epilepsy. The implications for e-diary design are that more attention should be given to those newer to epilepsy in the onboarding phase. Also, engagement is not necessarily based on just the upload of seizures, with other features of an e-diary being important to be filled in.

Objective: Circadian rhythms are frequently disrupted in patients in the intensive care unit (ICU), potentially worsening clinical outcomes. Continuous enteral nutrition throughout the day and night is common in the ICU, but eliminates feeding-fasting cycles that serve as important timing cues for the circadian system. The objective of this study was to determine the effect of providing enteral nutrition in a cyclic daytime pattern, compared with continuous administration, on circadian rhythmicity in critically ill patients in the ICU. Design: Single-center randomized controlled trial Setting: Mixed medical-surgical tertiary intensive care unit in the Netherlands Patients: Adult ICU patients ([&ge;]18 yr) receiving enteral nutrition. Intervention: Patients were randomized to receive either continuous, or cyclic daytime enteral feeding (08:00-20:00), initiated from the start of nutritional support. Measurements and Main Results: Sixty-two ICU patients were enrolled, of whom 51 were included in the per-protocol analysis. While the amplitude of the 24-hour rhythm in core body temperature did not differ significantly between the cyclic daytime and continuous feeding groups (0.17 [interquartile range: 0.09-0.24] vs. 0.20 [0.13-0.30], p=0.182), the 24-hour rhythm in heart rate was enhanced in patients receiving cyclic daytime feeding, as reflected by significantly higher amplitudes and more synchronized peak times. No significant differences in 24-hour rhythmicity were observed between groups for the other vital signs or melatonin. Conclusions: Our findings suggest that cyclic daytime feeding may strengthen circadian rhythms in critically ill patients. Further studies are warranted to evaluate its impact on clinical outcomes.

BACKGROUND: Atrial fibrillation (AF) remains difficult to explain using a single focal driver or rotor-centered mechanism across disease stages. We tested whether progressive atrial substrate remodeling can drive a critical transition toward turbulence-like, decentralized multi wavelet electrical activity. METHODS: We constructed a controlled two-dimensional atrial reaction-diffusion model with six graded substrate-remodeling stages. We evaluated effective wavelength, theoretical wavelet capacity, AF inducibility, vulnerable-window dynamics, spatial randomness, temporal memory, spectral dispersion, nonlinear indices, virtual ablation response and ERP-prolongation reverse mechanistic testing. RESULTS: Progressive remodeling shortened effective wavelength from 12.0 to 2.4 cm and increased theoretical wavelet capacity from 0.69 to 17.36. Inducibility rose sigmoidally as wavelength shortened, with a model-derived transition near lambda50=4.5 cm. Advanced substrates showed increased wavebreak, spatial randomness, short-memory dynamics, broad spectral dispersion, positive nonlinear indices and resistance to random local ablation. Culprit atrial premature beats within the vulnerable window efficiently triggered AF, whereas counter pacing at 20 to 35 ms reduced inducibility from 52% to 11% in stage 2. CONCLUSIONS: In this controlled model, AF initiation and maintenance were linked to substrate-dependent wavelength, wavelet capacity and vulnerable-window triggering. The model-derived transition provides a testable framework for future high-density mapping, patient30 specific modeling and device-based studies. Key Words atrial fibrillation; turbulence-like electrical activity; substrate remodeling; critical wavelength; multi-wavelet re-entry; vulnerable window; culprit premature atrial beat; counter pacing