Physiological Measurement

Body surface gastric mapping (BSGM) enables non-invasive measurement of gastric electrophysiology, but the signals are approximately 100 times weaker than cardiac potentials and overlap spectrally with motion artifacts, necessitating labor-intensive manual review that limits clinical scalability. We present an uncertainty-aware deep learning framework combining a signal reconstruction network with a parallel uncertainty estimation network to automate artifact correction in high-resolution BSGM. Models were trained on 2,398 multihour, 64-channel recordings from 27 international clinical sites using weak supervision, a physiology-aware loss function, and uncertainty-gated quality control. In an independent cohort of 127 patients, the system achieved relative reductions of 39% in signal reconstruction error, 9% in total data removed, and 23% in amplitude--movement correlation compared with the industry-standard Wiener filter. Improved signal fidelity altered automated clinical phenotyping in 7% of patients by recovering previously obscured gastric rhythms. Uncertainty-aware deep learning enables reliable automated artifact correction in body-surface gastric mapping, improving signal fidelity and enabling scalable clinical interpretation. The system is FDA-cleared (510(k) K252504) and deployed in clinical practice, demonstrating that data-driven artifact correction can meet regulatory requirements for medical devices and reduce dependence on specialist manual review.

Adhesive electrocardiography (ECG) electrodes used in neonatal intensive care units (NICUs) may cause skin injury in premature infants. Although photoplethysmography (PPG)-based ECG reconstruction has been explored, existing studies have mainly focused on adult data and often rely on direct PPG-to-ECG mapping or artificial signal alignment, which may be unsuitable for neonates with highly variable pulse arrival time (PAT). In this study, we propose an alignment-free RoPE-based dual-stream Transformer for reconstructing missing neonatal ECG segments using concurrent PPG signals and bidirectional ECG context. A total of 52,566 10-second ECG-PPG windows were extracted from 159 NICU patients and split at the patient level to prevent data leakage. The model was designed to learn ECG-PPG temporal coupling without forced synchronization by integrating PPG-derived hemodynamic timing information with lead-specific ECG context. Under a 40% random missing condition, the model achieved a Pearson correlation coefficient of 0.96, mean absolute error of 0.04, and root mean square error of 0.07. It also maintained robust performance under 4.0-second continuous block loss and 60% random patch loss, preserving a PCC of at least 0.90. These findings suggest that the proposed framework may serve as a signal imputation module for maintaining ECG monitoring continuity in NICU environments. Prospective validation is required before clinical diagnostic use.

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the leading electrical causes of sudden cardiac death, but automated detection is limited by strong class imbalance, where lethal arrhythmias account for fewer than 22% of ECG segments. In this setting, standard classifiers can achieve high accuracy by predicting normal rhythm in most cases while missing many lethal events, a failure mode referred to as rare-class collapse. We evaluated six imbalance-handling approaches: naive logistic regression, inverse-frequency reweighting, label-distribution-aware margin loss (LDAM), cost-sensitive training, two-stage cascade classification, and anomaly detection on 15,614 ECG segments from three PhysioNet databases (VTaC, VFDB, CUDB), with an overall normal-to-lethal ratio of 3.6:1. All methods were assessed at a fixed operating point of 95% specificity using recall, area under the precision-recall curve (AUPRC), and missed-lethal-event rate (MLER). The naive model achieved 45.1% recall (MLER = 0.549), missing 564 of 1,027 lethal events despite 84.1% accuracy. The two-stage cascade performed best, with 65.2% recall, AUPRC of 0.821, and MLER of 0.348, reducing missed events by 37% and achieving the highest decision-curve net benefit. Per-source analysis showed near-complete VF detection (recall up to 0.975) but much lower VT detection (recall 0.183), suggesting a feature-space limitation due to spectral similarity between organized VT and rapid sinus rhythm. Overall, the results show that evaluation metrics strongly influence the visibility of rare-class failure, and that cascade-based methods outperform simpler reweighting approaches for detecting lethal arrhythmias.

Background Continuous health monitoring enables early detection of diseases and improves therapeutic outcomes. Non-intrusive biosignal sensors, such as capacitive ECG (cECG), offer a practical solution for daily monitoring in private environments, such as smart homes and vehicles. However, artifacts reduce signal quality and compromise reliability. Methods Following a registered report protocol (Warnecke JM et al. Plos One. 2021; 16(7):e0254780), we record data of 44 subjects and develop an artifact index for cECG. We use three signal quality indices (SQIs): the correlation of QRS complexes (corSQI), the R-peak detection consistency (bSQI) and the absolute amplitude ratio (aSQI). Our index classifies overlapping 10s segments with a step-width of 2s into clean or artifact segments. We label a 2s interval as artifacts if all five overlapping segments indicate artifacts. We record cECGs using an armchair with integrated electrodes in a single-arm study involving 44 subjects performing two activities -- reading and watching television (TV); for 11 minutes each. We record a time-synchronized reference ECG with skin electrodes on the chest. To evaluate the artifact index, we compare it with manually generated ground truth. Moreover, we evaluate the clothing materials cotton, linen, jeans, and polyester in 5 subjects. Results Watching TV results in longer, continuously clean signal durations than reading. On average, 88.3% of the signal has a minimum continuous clean duration of 10s, versus 79.8% during reading. All clothing configurations achieve a clean signal duration exceeding 10s. Among the SQI metrics, bSQI performs best, achieving an accuracy of 90.7% and an F1 score of 79.9%. Combining the three SQI metrics in a voting approach improves accuracy to 92.0% and F1 score to 82.1%. Discussion Our artifact index automatically distinguishes clean from artifact cECG segments, promoting health monitoring in unsupervised real-world settings, earlier disease detection, and preventive health management. A limitation is the investigation of only two scenarios (reading and watching TV).

**Background:** Acute pancreatitis (AP) is a common gastrointestinal emergency with a subset of patients progressing to severe acute pancreatitis (SAP), which carries substantial morbidity and mortality. Current clinical severity scores such as BISAP, APACHE II, Ranson, and the Modified CT Severity Index require upon 48 hours of observation before reliable assessment is possible, limiting early triage. Machine learning (ML) approaches using routine admission laboratory values may enable earlier, more accurate prediction. **Methods:** We evaluated 11 models spanning three architectural families classical ML (Logistic Regression, Random Forest, Gradient Boosting), feedforward deep learning (MLP, Residual MLP, Attention MLP), and recurrent deep learning (LSTM, Stacked LSTM, Bidirectional LSTM, LSTM+Attention, CNN-LSTM) on a Chinese AP cohort of 722 patients (585 severe, 137 mild) labelled according to the 2012 Revised Atlanta Classification. Performance was assessed via 5-fold stratified cross-validation using AUC-ROC, F1 score, sensitivity, specificity, and PPV, with decision thresholds optimised for maximal F1. **Results:** Random Forest achieved the highest AUC of 0.877 (F1=0.917, sensitivity=96.8%, PPV=87.1%), followed closely by Gradient Boosting (AUC=0.874, F1=0.918). Classical ML models consistently outperformed deep learning counterparts. CNN-LSTM was the best recurrent model (AUC=0.777) but remained inferior to all classical approaches. LSTM-family models produced AUC values of 0.684-0.777, reflecting the cross-sectional tabular nature of the data. **Conclusions:** Random Forest provides robust, high-sensitivity early prediction of SAP severity using routine admission data. External prospective validation is required before clinical deployment. **Keywords:** acute pancreatitis; severity prediction; machine learning; random forest; deep learning; LSTM; Revised Atlanta Classification; early triage

Background and ObjectiveAccurately parameterizing dynamic, time-varying interactions in physiological systems is a methodological challenge, as global causal discovery methods may obscure transient, local fluctuations. This study introduces tempord, an open-source Python library designed to estimate local temporal orders and evaluate the short-term stability, directionality, and strength of causal links in non-stationary biological signals. MethodsThe algorithm estimates temporal relationships by keeping one signal stationary while iteratively shifting another one within a sliding window. To parameterize optimal inter-signal shifts (causal vector, CV), the framework utilizes linear modeling or time series distance metrics. The methodology was validated through a simulation study on synthetic bivariate signals with mathematically imposed dynamic phase delays, under both deterministic and noisy conditions. Furthermore, in-vivo capabilities were demonstrated by evaluating cardiorespiratory coupling dynamics across spontaneous and music-induced relaxation breathing states. ResultsThe simulation study demonstrated that the extracted CV trajectories precisely aligned with ground-truth temporal delays, assessed using mean absolute error and root mean square error for both noise-free and noisy synthetic data. In-vivo application demonstrated dynamic temporal stability and the detection of minor step changes during autonomic nervous system state transitions. ConclusionsThe tempord Python package bridges the gap between global causal discovery and local beat-by-beat statistical parameterization. It provides a robust "bottom-up" analytical instrument for investigating the transient mechanisms governing complex biological networks.

Continuous, noninvasive blood pressure monitoring remains an unmet clinical need, particularly in the intensive care unit (ICU) where hemodynamically unstable patients need high-frequency monitoring. Invasive arterial catheterization represents the current standard of care for continuous blood pressure (BP) monitoring, but it carries risks and limits patient mobility. In this study, we evaluate the MOSAIC system, a novel multi-modal, multi-nodal wearable, wireless sensor system placed on multiple locations on the body, for continuous noninvasive BP estimation in a cohort of ICU patients. Unlike existing continuous BP sensors, the MOSAIC system offers an ideal form factor for continuous BP monitoring, enabling a fully untethered setup which minimally impacts activities of daily living. Leveraging sensor-derived biosignals to compute continuous BP, we determine the accuracy of our BP regression models using arterial line-derived blood pressure reading as a ground truth. Using a Light gradient boosted machine (LGBM)-based regression model, we demonstrate strong beat-to-beat agreement with a mean absolute error (MAE) of 5.66 +/- 5.94 mmHg for systolic BP (SBP) prediction and 2.45 +/- 2.87 mmHg for diastolic BP (DBP) prediction, and average ratio variability (ARV) of 0.527 +/- 0.185 and 0.489 +/- 0.170 for SBP and DBP, respectively, compared to linear and deep-learning regression baselines. Our findings demonstrate strong agreement between the predicted BP values and invasive, arterial-line BP measurements, supporting the feasibility of wearable, wireless, and cuffless blood pressure monitoring in high-acuity clinical settings.

Purpose: Continuous intracranial pressure (ICP) monitoring requires invasive instrumentation, reaching only a narrow subset of critically ill patients. We tested whether deep learning models trained on routinely acquired extracranial signals can reconstruct continuous ICP waveforms at clinically relevant accuracy in an independent external cohort. Methods: In adults admitted to the ICU at a single quaternary health system, five deep learning architectures were trained on high-frequency arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG) waveforms, using invasive (intraparenchymal) ICP as ground truth. Two fusion strategies (early and late) and three training objectives (waveform-morphology, baseline robust regression, and weighted robust regression) were evaluated. Models were externally validated on the held-out MIMIC-III Waveform Database. Performance was assessed by mean absolute error (MAE) and waveform similarity by Pearson correlation (r). Results: We analyzed data from 158 critically ill adults (~5,322 hours) across two quaternary health systems (Johns Hopkins Hospital, Baltimore; Beth Israel Deaconess Medical Center, Boston). Validation MAE ranged from 4.276 mmHg [95% CI 4.269, 4.283] (gated recurrent, late fusion) to 4.946 mmHg [95% CI 4.938, 4.956] (attention-based, early fusion), with Pearson r ranging from 0.599 [95% CI 0.599, 0.600] to 0.722 [95% CI 0.722, 0.723]. The multiscale encoder-decoder model demonstrated the most favorable MAE-correlation tradeoff. Conclusion: This is the first demonstration that continuous ICP waveform reconstruction from bedside signals generalizes across institutions at clinically relevant accuracy, establishing a foundation for non-invasive ICP monitoring and motivating validation across broader populations and ICP ranges.

Objective: Patient-ventilator dyssynchrony (PVD) is a common and clinically consequential problem in critically ill patients receiving invasive mechanical ventilation. Yet automated identification of PVD subtypes at scale remains an unmet clinical need, owing to the lack of large annotated bedside waveform datasets. Methods: We developed and validated a semi-supervised algorithm for automated annotation of PVD. In two medical ICUs at a tertiary academic center, bedside devices continuously collected airway flow and pressure waveforms from the ventilators. We developed a software interface with an information retrieval system that grouped similar breaths for expert human review, yielding 1,542,296 labeled breaths across eight categories: 2 labels for breath delivery mode, 5 labels for PVD subtypes, and 1 label denoting a normal breath. Two pulmonary physicians with expertise in ventilator training and education provided the expert reference labels. We trained an initial classification model on a model-derivation set of 771,148 breaths (divided into training and validation) and evaluated it on a hold-out test set of 771,149 breaths A semi-supervised approach was utilized to extend labeling to an additional 12,965,000 unlabeled breaths. Results: The supervised model performed well across all labels, with Macro-F1 scores between 0.96 and 1.00. Semi-supervised learning across 12 rounds expanded the training set from 771,148 to 8,563,995 breaths without significant performance degradation. Conclusion: We developed a practical and scalable system for automated PVD annotation that performed well across all subtypes. This work provides a reproducible foundation for automated PVD labeling to support the development of machine-learning-based clinical decision support systems for identifying patient-level asynchrony.

Deep learning reads 12 lead electrocardiograms at close to expert level on public benchmarks, yet most reports give one accuracy figure for the whole test set and stop there. We trained three architectures that are standard in this field, a 1D ResNet, a convolutional network with a bidirectional LSTM, and a convolutional network with a bidirectional LSTM followed by a transformer encoder, on the PTB-XL dataset to classify the five diagnostic superclasses, and then looked at how each one performed across sex and age. On the held out fold all three reached a macro AUC near 0.92, in line with the strongest published results on this benchmark, and the simplest model, the 1D ResNet, was marginally the best at 0.9241. The averages hid a steady pattern. Every model scored lower for female patients than for male patients, and every model scored lowest for patients aged 80 and over, where the 1D ResNet fell to 0.8878 and the transformer to 0.8693. Adding complexity did not close either gap and slightly widened the gap by age. Overall accuracy on PTB-XL is close to solved for these model families, but the benefit is not shared evenly, and a single headline number hides the patients a model serves worst. We release the full stratified evaluation to support fairness aware reporting.

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.

Fetal-monitoring biomarkers for neonatal hypoxic-ischemic brain injury face a structural gap: the mechanistic ground truth that would label a training set -- perfusion pressure, the moment of decompensation, the injury time course -- cannot be measured at scale or ethically in human pregnancy or labor, and generative synthetic data carry no mechanistic labels. We address this with a mechanistic computational model of the fetal cardiovascular, autonomic, and metabolic response to controlled hypoxic stress, and use it to test how beat detection and acquisition fidelity alter the interpretation of fetal-heart-rate-variability (HRV) biomarkers. The model integrates these systems forward in time across antepartum development (gestational-age growth scaling) and intrapartum stress (umbilical-cord occlusions), emitting synthetic monitoring signals (fetal heart rate, RR intervals) co-registered with model-computed latent labels (pH, base deficit, lactate, perfusion pressure, decompensation and injury states). In a fetal-sheep-derived autonomic-loop configuration we report three results. First, a phase-accumulator beat detector shows that the apparently physiologic baseline HRV of an earlier build was largely a detector artifact, and a noise-off control shows beat-to-beat variability requires an explicit stochastic driver rather than self-sustained autonomic oscillation. Second, a sampling-fidelity sweep yields a fidelity-matched selection rule: a deceleration-area biomarker is preserved at CTG-grade 4 Hz whereas RMSSD is corrupted there (inflated about 8-fold by timing quantization) and recovers only at fetal-ECG rates. Third, autonomic modulation alone does not reproduce the published RMSSD rise-then-collapse -- a negative result that motivates, but does not prove, an intrinsic sinoatrial-pacemaker contribution as a testable hypothesis. This is an in-silico, hypothesis-generating study: the model is not validated for individual fetal prediction, clinical risk estimation, or clinical decision-making. The model is implemented as Fetal Twin (engine fetaltwin), a source-available research instrument released under a noncommercial license, together with all figure configurations, so that these controlled experiments are reproducible. Key PointsO_LIProgress on fetal-monitoring biomarkers for neonatal brain-injury risk is constrained by a structural gap: the mechanistic ground truth that would label a training set -- perfusion pressure, the moment of cardiovascular decompensation, the time course of injury -- cannot be measured at scale or ethically during human pregnancy or labor. C_LIO_LIWe present Fetal Twin (source-available engine fetaltwin), a publicly available, noncommercially-licensed mechanistic testbed for fetal physiological development. It integrates the fetal cardiovascular, metabolic, and autonomic systems forward in time across antepartum development (gestational-age growth scaling) and intrapartum stress (umbilical-cord occlusions), and emits synthetic monitoring signals (fetal heart rate, RR intervals) co-registered with model-computed latent labels (pH, lactate, perfusion pressure, decompensation and injury states). C_LIO_LIThe names digital-twin connotation is deliberate but bounded: Fetal Twin is a mechanistic, population-level twin of fetal physiology used as a research instrument -- not a validated, patient-specific clinical digital twin or predictor. Its purpose is to interrogate what candidate biomarkers can and cannot mean, via in-silico controls impossible in vivo -- swapping the beat detector, turning a noise source off, ablating a reflex, or quantizing the signal to a monitors sampling grid. C_LIO_LIDemonstrations in a fetal-sheep-derived autonomic-loop configuration show that beat-to-beat HRV amplitude can be a numerical artifact of the beat detector, and that in this model class beat-to-beat variability requires an explicit stochastic driver -- it does not arise as a self-sustained oscillation of the deterministic autonomic loop. C_LIO_LIA further demonstration establishes a fidelity-matched biomarker-selection rule -- a deceleration-area biomarker survives CTG-grade 4 Hz sampling whereas RMSSD is corrupted at that rate and needs fetal-ECG timing -- and a negative result shows the published RMSSD "rise-then-collapse" is not reproducible from autonomic modulation in this model class, motivating (but not proving) an intrinsic-pacemaker hypothesis. C_LI

Consumer wearable devices increasingly use single-lead electrocardiograms (ECGs) for cardiac monitoring, but these signals contain substantially less spatial information than the clinical 12-lead standard. Whether this reduction dispro- portionately affects older adults, who often present with more complex cardiac conditions, remains poorly understood. In this study, we evaluated the impact of lead reduction on AI-ECG diagnostic performance across age groups. A 1D resid- ual neural network was trained on 21,091 PTB-XL ECG recordings spanning five diagnostic superclasses and assessed using 12-, 6-, 2-, and 1-lead configurations. Under the full 12-lead setting, model accuracy declined from 84.5% in patients younger than 40 years to 66.2% in patients aged 75 years or older. Progressive lead reduction further widened this gap. Under the 1-lead configuration, accuracy decreased by 14.1 percentage points in the 75+ group but by only 0.4 percent- age points in the <40 group, representing an approximately 40-fold differential degradation confirmed by three independent statistical tests (all p < 0.0001). Older adults also exhibited greater multi-condition diagnostic complexity, pro- viding a plausible explanation for their increased vulnerability to information loss. External validation on the MIT-BIH Arrhythmia Database confirmed cross- dataset model stability. These findings suggest that age-stratified performance reporting should be a minimum standard in wearable AI-ECG validation and regulatory assessment.

High-frequency physiological monitoring in ICUs can identify impending deterioration hours before clinical recognition yet extracting reliable early-warning signals from noisy vital-sign streams remains challenging. We present SIgnose, an interpretable prediction framework for early detection of abnormal shock index (SI), built from routinely monitored vital signs using physiologic variability and nonlinear time-series features. SIgnose was developed on the eICU Collaborative Research Database and externally validated on the MIMIC-III adult database and a pediatric SafeICU cohort (AIIMS New Delhi), with additional prospective validation in the pediatric ICU. We benchmarked three representation strategies: (i) engineered physiologic variability and nonlinear time-series features, (ii) deep learning, and (iii) Llama-3.1-8B embeddings with low-rank adaptation. Physiologic variability features consistently demonstrated superior cross-cohort generalization. The final model used 3,970 features from five vital signs to predict abnormal SI up to 8 hours ahead, achieving AUROC 0.861 (95% CI 0.859-0.863) and AUPRC 0.927 (95% CI 0.925-0.929) on eICU. External validation yielded AUROC 0.870 (95% CI 0.863-0.876) and AUPRC 0.935 (95% CI 0.930-0.940) on MIMIC-III, and AUROC 0.875 (95% CI 0.863-0.888) and AUPRC 0.915 (95% CI 0.898-0.930) on SafeICU; prospective pediatric validation (n = 88) achieved AUROC 0.885 (95% CI 0.868-0.902) and AUPRC 0.911 (95% CI 0.882-0.936). SHAP interpretability analysis identified heart rate variability, respiratory trend dynamics, and multi-scale blood pressure variability as key early-warning signatures. These findings establish SIgnose as a reproducible, low-compute, early-warning framework and demonstrate that physiologic variability features provide robust, generalizable representations for early deterioration detection across adult and pediatric critical care.

The electrocardiogram (ECG) contains rich nonlinear and non-stationary dynamic information that is only partly captured by conventional ECG interpretation and beat-to-beat metrics, and is increasingly analyzed using black-box artificial intelligence models that often lack interpretability. Here, we introduce the ECG time-frequency "eyeball", an interpretable framework that transforms a brief single-lead ECG recording into a geometric signature and a set of low-dimensional rotational and geometrical features using empirical mode decomposition and Hilbert-based analytic signal mapping. In 30-second lead I-equivalent recordings from 170 healthy subjects and 80 patients with acute myocardial infarction (AMI), the proposed "eyeball" metrics significantly differentiated groups, with AMI associated with higher rotational frequency metrics, lower envelope metrics, and displaced centroid location. Representative examples revealed a coherent morphologic spectrum from normal patterns to geometries consistent with myocardial ischemia, injury, and infarction. The representation remained stable across recording windows from 30 seconds to 5 minutes, and individual "eyeball" features achieved areas under the receiver operating characteristic curve (AUCs) of up to 0.78 for AMI detection. These findings suggest that the ECG time-frequency "eyeball" condenses clinically relevant nonlinear ECG dynamics into an interpretable representation that may reveal hidden AMI signatures, complement conventional ECG interpretation, and provide a foundation for accessible single-lead cardiovascular screening using future smart wearables.

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.

Blood pressure (BP) is a vital sign which is measured to diagnose and manage hypertension. However, current methods to measure BP use inflatable cuffs which cause discomfort and limit the frequency at which measurements can be made, or intra-arterial catheters which are invasive and pose infection risks. Here, we propose and evaluate the use of Diffuse Speckle Pulsatile Flowmetry (DSPF) as a cuffless BP measurement method to address these limitations. DSPF is a laser speckle-based technique which simultaneously records blood flow rate and blood volume (i.e. photoplethysmography or PPG) signals from relatively deep vascular tissue. Using information from these signals, we studied DSPFs effectiveness in measuring systolic BP (SBP) and diastolic BP (DBP) through an outpatient study in which 133 patients were recruited, and in measuring beat-to-beat BP waveforms through an inpatient study in which two patients were recruited. In the outpatient study, the DSPF method was able to achieve mean absolute errors (MAEs) of 4.17 mmHg and 2.42 mmHg for SBP and DBP respectively compared to conventional cuff-based methods. It was also able to fulfil the requirements of the AAMI/ESH/ISO 81060-2:2018 standard for BP measurement devices and attain an "A" grade according to the British Hypertension Society grading scheme. For the inpatient study, it produced BP waveforms which had MAEs of 2.35 mmHg and 3.06 mmHg compared to arterial-line measurements for the two patients, respectively. Compared to PPG which has been studied more extensively as a cuffless BP measurement method, we found through ablation studies that DSPF was able to reach significantly lower MAEs and hence better accuracies. DSPF augments the performance of PPG-only methods by leveraging additional information from the blood flow rate signal, and we therefore find it to be a superior cuffless BP measurement method which can potentially be used in outpatient, inpatient, and remote settings.

The Empatica E4 wristband provides continuous multi-modal physiological monitoring including blood volume pulse (BVP), electrodermal activity (EDA) and skin temperature (TEMP) but its validity for sleep-stage-specific autonomic and thermoregulatory monitoring has not been systematically evaluated against concurrent polysomnography (PSG). Using the Wearanize+ dataset which provides synchronised PSG, Empatica E4, and Zmax EEG recordings from 100 home-recorded participants; a systematic validation of Empatica E4 physiological signals against PSG ground truth across five sleep stages was conducted. Of 100 participants, 92 had Empatica data; 69 met Zmax EEG signal quality criteria and formed the analysis sample. Heart rate (HR) from the pre-computed Empatica HR channel showed valid stage-specific patterns (Wake: 70.9 bpm, N3: 61.2 bpm) and moderate inter-device MeanNN correspondence with PSG ECG (Spearman r=0.35-0.42 across stages). Skin temperature showed the expected thermoregulatory pattern (Wake: 33.92C, N3: 35.48C) and is recommended for downstream analyses. Tonic EDA showed an inverted stage pattern attributable to wrist sweat accumulation during deep sleep, representing a known confound for wrist-worn EDA during sleep. Phasic EDA showed plausible patterns and may be used with caution. These findings establish a validated feature set for Empatica E4 sleep research and directly inform multimodal psychiatric biomarker studies using the Wearanize+ dataset.

Background: Spinal cord injury (SCI) is frequently associated with orthostatic hypotension, defined by a sustained decrease in blood pressure upon assuming an upright posture due to impaired autonomic regulation. Cardiovascular spinal cord epidural stimulation (CV-scES) can regulate systolic blood pressure (SBP) in people with SCI, but stimulation paradigms are highly individualized. To make this treatment available to more patients, we developed an algorithm to tailor individualized CV-scES paradigms that closely mimic researcher-developed paradigms. Methods: We performed an offline analysis using datasets collected from eight individuals with SCI with epidural stimulators implanted over the lumbosacral spinal segments. During data collection, researchers modulated stimulation parameters with the goal of maintaining SBP between 110-120 mmHg. Each two-hour dataset included synchronized SBP and stimulation recordings. We ran optimization analyses offline to determine temporal requirements before modifying stimulation amplitude to mitigate out-of-range SBP. Results: The algorithm parameters that best matched researcher-selected stimulation changed relatively quickly during the first 12 min (one every ~40 sec), and more slowly thereafter (one every ~79 sec). Overall, algorithmic stimulation closely tracked researcher-controlled stimulation, with a mean correlation coefficient of 0.94. To evaluate online performance, we tested the algorithm in real time with a single participant. We found that a faster approach was needed to respond to changes in SBP caused by rapid, unpredictable events, such as postural changes. We implemented a sigmoid-based paradigm that determined the time to wait before changing stimulation as a function of the current SBP, with worse SBP values requiring faster responses. The new paradigm outperformed the original algorithm and researcher-controlled stimulation across measures of SBP stability, though recovery from a postural tilt maneuver remained slower than with researcher control. Conclusions: Our results indicate that algorithmic stimulation may minimize assistance required from researchers and participants, making CV-scES more feasible for clinical translation.

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?