Physiological Measurement

BackgroundObstructive sleep apnea (OSA) is a highly prevalent sleep disorder. Major risk factors include obesity, older age, and male sex. Given the well-established association between OSA and cardiovascular disease, markers of cardiovascular health derived from electrocardiography (ECG) may help identify individuals with undiagnosed OSA. This study evaluated whether 12-lead ECG features improve machine learning-based prediction of OSA beyond established clinical factors. MethodsWe conducted a retrospective cohort study of individuals who underwent diagnostic sleep studies at the University of Kansas Health System Sleep Disorders Center (2004-2025) and had a 12-lead ECG acquired within one year. Standard ECG features (intervals, amplitudes, voltage-time integrals, morphology descriptors) were extracted from the representative beat. Latent representations were derived using two variational autoencoder (VAE) models pretrained on >1 million ECGs: a 30-dimensional representative-beat VAE and a 90-dimensional full 10-second ECG VAE. Three LightGBM models were trained: clinical factors only (age, sex, weight, height, body mass index [BMI], Epworth sleepiness scale [ESS]), ECG features only, and a combined model. Feature selection and hyperparameter tuning were performed separately for each model within a 5-fold cross-validation scheme. The outcome was moderate-to-severe OSA (AHI[≥]15 events/h), evaluated by AUROC (bootstrapped 95% CI) in a 10% holdout test set. ResultsThe dataset included 7,699 individuals (52.2% women, mean age 58.0 {+/-} 14.2 years, BMI 33.6{+/-}8.2 kg/m{superscript 2}, ESS 8.8{+/-}5.1; 36.4% with moderate-to-severe OSA) Training AUROC (95% CI) was 0.71 (0.66-0.76) for the combined model, versus 0.65 (0.59-0.70) for the clinical-only model and 0.66 (0.61-0.72) for the ECG-only model. Holdout test AUROC was 0.78 (combined), 0.69 (clinical-only), and 0.71 (ECG-only). Feature importance identified age, BMI, weight, ECG VAE latent variables, height, sex, and P-wave voltage-time integral as relevant contributor to the predictions. ConclusionsLatent ECG features extracted by VAE improve prediction of moderate-to-severe OSA when combined with clinical factors.

Background and Objective: Heart rate variability (HRV) analysis provides a non-invasive method for quantifying autonomic modulation from electrocardiographic recordings. However, practical HRV analysis often depends on fragmented workflows, limited signal-quality review, and software tools optimized for either human or preclinical recordings, but not both. This study developed and evaluated HRV-GUI, a MATLAB-based graphical interface for electrocardiogram (ECG)-derived HRV analysis in translational biomedical research. Methods: The HRV-GUI integrates electrocardiographic and RR interval loading, human and rat analysis modes, preprocessing, segment selection, automated R-peak detection, manual peak correction, RR interval generation, multi-domain HRV computation, diagnostic visualization, result export, and session saving/loading. The software was evaluated using deterministic synthetic RR interval datasets, baseline recordings from healthy human controls and healthy rats, and a clinical use-case comparison between healthy controls and patients with diabetic gastroparesis. Results: The HRV-GUI produced expected outputs in synthetic RR validation tests, including constant RR sequences, alternating RR sequences, outlier-containing RR sequences, and low-frequency- or high-frequency-dominant sinusoidal RR modulation. The software generated physiologically plausible HRV profiles in both human and rat recordings. In the clinical use-case analysis, patients with diabetic gastroparesis showed higher heart rate and sympathetic index, together with lower respiratory sinus arrhythmia, absolute low- and high-frequency spectral power, standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), percentage of successive RR intervals differing by more than 50 ms (pNN50), Poincare short-term variability (SD1), and Poincare long-term variability (SD2) compared with healthy controls. Conclusions: HRV-GUI provides an integrated biomedical software workflow for ECG-derived HRV analysis. The validation results support its use for controlled RR testing, human and rodent ECG recordings, and clinical autonomic assessment in diabetic gastroparesis.

This study provides a direct and objective comparison of ECG signal quality and morphological feature preservation across four leading commercial smartwatches: the Apple Watch Series 9, Samsung Galaxy Watch 6, Fitbit Sense 2, and Withings ScanWatch, using a controlled dataset. The Philips TC30 is used as the reference standard. ECG signal quality is assessed using Signal-to-Noise Ratio (SNR), Standard Deviation of Baseline Wander, and Power Spectral Density (PSD). Preservation of morphological features is assessed by comparing the performance of our automatic J-point and ST deviation detection algorithms when applied separately to recordings from each smartwatch. J-point detection is evaluated by comparing each device's performance against the reference device, while ST deviation is assessed using simulator-defined ground truth values. The Apple Watch and Withings ScanWatch achieved the best SNR, while the Samsung Galaxy Watch showed substantially lower values. The Withings ScanWatch demonstrated the most stable baseline, whereas the Fitbit Sense 2 showed the greatest baseline variability. All devices exhibited a consistent negative bias in J-point detection, suggesting that internal signal processing tends to smooth small changes in waveforms. For ST-segment classification, the Apple Watch achieved the most balanced performance in terms of sensitivity and specificity. These results provide researchers and clinicians with data-driven guidance for selecting the most appropriate device for their specific needs.

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.

Explicit timing and context features may complement short electrocardiogram waveforms, but aggregate gains on imbalanced benchmarks can conceal errors in individual classes. We therefore compared Logistic Regression, Random Forest, XGBoost, and a one-dimensional convolutional neural network (CNN) in four cumulative input stages. Stage S1 used a 255-sample beat waveform alone, and stages S2 to S4 added 6, 13, and 21 engineered features. Using the inter-patient division of the MIT-BIH Arrhythmia Database, with 50,992 development and 49,686 evaluation beats in four classes, we estimated 95% intervals with a paired bootstrap over the 22 evaluation recordings. Adding six RR interval features increased macro F1 by 0.025 to 0.093, with intervals excluding zero for every model family. Further context helped Random Forest and XGBoost, whereas later CNN gains fell within resampling uncertainty. At the final stage, XGBoost and CNN reached macro F1 of 0.580 and 0.574, and the interval for their difference included zero. Despite opposite operating points, their precision for fusion beats was similar at matched recall, suggesting that neither separated fusion beats better. The most accurate pipeline, Random Forest at S2 with 94.45% accuracy, detected only 1.4% of supraventricular beats, and development and evaluation favored the same stage for only one family. The results for the minority classes were based on single recordings. Record 213 supplied 203 of the 207 correct CNN fusion detections, and elsewhere both final models detected at most 4 of 26 fusion beats. Record 232 contains 75% of the evaluation supraventricular beats, and nearly all were missed. Excluding this record nearly doubled XGBoost supraventricular F1, from 0.199 to 0.383. These findings are limited to retrospective analysis with annotated beat positions and a single training seed.

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.

ECG waveform data, which is increasingly required for artificial intelligence analyses, is often unavailable in raw data formats. Digitization can approximate original raw data from ECG images, but digitizers reconstruct ECG waveforms only approximately. Exact waveform reconstruction is needed to guarantee that any errors, even if small, do not unexpectedly propagate into downstream analyses. Portable document format (PDF) files containing vector graphics store waveforms derived directly from raw data. This improves reconstruction accuracy, but existing PDF reconstruction methods remain approximate because raw data values are rounded during PDF encoding/generation. Despite rounding, however, there is a 1:1 mapping between raw data and PDF-encoded values which can be reversed to recover the original raw data with no error. We developed open-source software in MATLAB and Python (http://github.com/BIVectors/pdf2ECG) for bit-exact waveform reconstruction of General Electric MUSE ECG PDFs based on double quantization of ECG waveform data. The software supports multiple PDF layouts and voltage gains, automatically flags waveform clipping under which exact reconstruction is not possible, and certifies if parameter values used for waveform reconstruction are consistent with parameter values used during PDF encoding. Accuracy was assessed by comparing PDF reconstructed waveforms to raw XML waveforms, and reconstruction parameters were perturbed to determine the accuracy of waveform certification. 1,632 PDF-XML pairs were assessed in 5 layouts, each at 3 gains. After software-identified waveform clipping was excluded, the software reconstructed 100% of ECG waveforms with zero error compared to ground-truth XML. Certification tests were extremely sensitive to minor perturbation in encoding parameters, and if passed, confirmed bit-exact waveform reconstruction without need for ground-truth comparisons. Although this method does not replace the need for general ECG image digitization, for MUSE generated PDFs this method is preferable to rasterization followed by image-based digitization.

Background: While Heart Rate Variability (HRV) is a cornerstone of non-invasive autonomic assessment, the sensitivity and sustainability of specific HRV parameters remain debated. This study introduces a "sustainability score" to systematically validate which metrics best reflect autonomic nervous system (ANS) modulation following pharmacological blockade. Methods: Ten male Sprague-Dawley rats were implanted with ECG electrodes. After recovery, rats underwent randomized atropine (1 mg/kg, intraperitoneal) and propranolol (10 mg/kg, intraperitoneal) sessions separated by at least 3 days. Baseline ECG was recorded for 30 min before each session, followed by 90-min post-drug ECG recording. HRV parameters were analyzed in sequential 15-min and 30-min segments. Reliability was assessed using average p-values across post-drug segments and a sustainability score, defined as the number of segments significantly different from baseline. Results: After atropine, HF Power showed the strongest rapid parasympathetic-related response in 15-min segments, with the lowest average p-value (0.1476) and highest sustainability score (4), followed by RSA (p=0.16148; score=4). HFn was less reliable in 15-min segments (p=0.23147; score=2) but became the most reliable parasympathetic-related metric in 30-min segments (p=0.0001; score=3). After propranolol, SI was the most reliable sympathetic-related metric in both 15-min (p=0.18225; score=2) and 30-min segments (p=0.0393; score=2), while SD2/SD1 showed the strongest 30-min sympathovagal-balance response (p=0.0200; score=3). Conclusion: HF Power is recommended for rapid parasympathetic-related assessment in short windows, HFn for longer-window parasympathetic assessment, SI for sympathetic-assessment, and SD2/SD1 for 30-min sympathovagal-balance analysis. Sustainability scoring provides a structured framework for selecting ECG-derived autonomic markers.

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.

Time-embedded electrocardiographic descriptors combine high-order derivatives in nonlinear ratios, making their numerical calibration essential. We analyzed 200 LUDB participants (1,748 QRS windows) and a fixed external sample of 1,000 PTB-XL participants (9,735 windows). Four prespecified derivative operators were evaluated at three nominal Butterworth cutoffs; a fifth, regularized operator was added post hoc. Extensions added transfer functions, matched response crossings and a Gaussian transient with exact derivatives. The expanded calibration comprised 1,005 mathematical signal/noise realizations. At 150 Hz, spectral differentiation (SPEC) increased external participant-level mean absolute torsion by a median 78.97% relative to successive finite differences (FD; 95% confidence interval 77.39-80.53). Reducing the cutoff to 40 Hz yielded weak rank preservation: Spearman correlations were 0.045 for SPEC and 0.099 for FD. Matching each derivative's first normalized -3-dB crossing at 20 Hz reduced the external SPEC-FD median difference to 0.016%, but introduced 4.26-4.70% noiseless error on the Gaussian transient. Agreement was therefore not accuracy against the known target. For the Gaussian transient with 20-microvolt noise, relative root mean squared errors at 150 Hz were 719.52% for SPEC and 99.96% for the regularizer; the latter error remained comparable to the target magnitude, and regularization also introduced noiseless bias. Almost-curvature approximated a normalized voltage slope under the specified coordinates and did not converge to classical curvature under reparametrization. Explicit transfer functions, absolute descriptor levels and analytic targets explain important processing effects while separating numerical agreement from clinical validity. No universally optimal estimator or diagnostic benefit is established.

Abstract Background: Conventional cuff based methods report single systolic and diastolic pressure values and depend either on Korotkoff sounds or on empirical oscillometric algorithms. Methods: We developed a patented cuff based method, termed plethysmometry, that simultaneously records cuff pressure and finger photoplethysmograms from the cuffed and contralateral arms. The contralateral photoplethysmogram serves as an internal physiological reference for pulse amplitude. Following an initial pressure ramp, 16 cuff pressure plateaus are applied. Absolute and normalized pulse amplitudes in the cuffed arm are related to cuff pressure to estimate lower and upper systolic and diastolic pressures. Mean arterial pressure is estimated independently from cuff pressure oscillation amplitude. The estimations are done by automated algorithms developed in MATLAB. Results: The method reports lower and upper systolic, diastolic and mean arterial pressure ranges (during the recording period), together with their average values. Average pulse pressure is also reported. Graphs are provided for reference to check the accuracy of reported pressures. Conclusion: Plethysmometry provides an objective digital record of the cuff pressure and pulse traces and estimates systolic and diastolic pressure ranges from the pulse response of the individual subject to graded cuff pressure. It also reports mean arterial pressure. Comparison of plethysmometric systolic and diastolic pressures with sphygmomanometrically derived pressures and diagnostic agreement are reported in a companion paper.

Background Implantable loop recorders (ILRs) provide continuous rhythm monitoring. Professional society guidelines and expert consensus statements recommend ILR use for patients with recurrent syncope and cryptogenic stroke, but recommendations are weaker for other indications. We assessed ILR use over time and concordance between indication and clinical guidance. Methods We conducted a retrospective cohort study using Epic Cosmos data from 1/1/2020-12/31/2025. Among active patients with at least one of 13 diagnoses potentially eligible for ILR placement, we identified ILR placements and classified them by concordance with professional society guidance: concordant, possibly, or non-addressed. Annual incidence was estimated overall and by indication. We also described the proportion of patients who received longer-term (>7 days) rhythm monitoring in the year prior to placement. Results Of 47,046,706 patients with diagnoses potentially eligible for ILR placement, 201,103 (0.4%) had an ILR placed. ILR placements increased from 3.08 ILRs per 100,000 patient-days in 2020 to 4.13 in 2025 (5.9% annual growth). The most common indications were cryptogenic stroke (28.6%) and atrial fibrillation without prior ablation (21.4%). Overall, 43.9% were guidance concordant, 28.5% possibly concordant, and 24.8% non-addressed. Relative annual growth in ILR placements per 100,000 patient-days was significant across all guidance categories: concordant (1.3%, 95% CI, 0.9%-1.7%), possibly concordant (5.5%, 95% CI, 5.0%-6.0%) and non-addressed (7.4%, 95% CI, 6.8%-8.0%). Among 125,631 outpatient placements, 50,181 (39.9%) had any prior long-term rhythm monitoring. Conclusion ILR placements are uncommon but increasing, with faster annual growth for indications that may not be guidance concordant. More than half of patients lacked long-term rhythm monitoring prior to outpatient ILR placement.

The Muse 2 EEG headset also records forehead photoplethysmography (PPG). We tested whether its pulse intervals can replace Polar H10 RR intervals for ultra-short heart-rate variability in 28 seated recordings (60 s eyes open, then 60 s eyes closed), using fixed processing, no exclusions and eleven sensitivity analyses. Mean heart rate agreed almost perfectly (ICC [≥] 0.999; bias -0.22 and 0.00 beats min-1). SDNN agreement was good to excellent (ICC 0.86 and 0.91; bias +8.6 and +8.2 ms). RMSSD agreement was moderate (ICC 0.60): Muse exceeded Polar in at least 27 of 28 sessions (bias +19.2 and +18.5 ms; about 1.9-fold), and the ratio bias fell as RMSSD rose. Interval differences (SD 23 ms) implied about 16 ms of independent timing error per pulse, which reproduced the RMSSD bias; without ECG, sensor jitter could not be separated from variation in pulse transit time. Tracking of the eyes-closed change in ln RMSSD was not demonstrated ({rho} = 0.35, 95% CI -0.07 to 0.67; detectable {rho} [≥] 0.51). Muse 2 PPG can supply mean heart rate and, with a stated bias, SDNN; its ultra-short RMSSD is not interchangeable with Polar RMSSD and should be reported as pulse-rate variability.

Introduction: Fighting the ventilator (FV) is characterized by a patients active counteraction of respiratory gas flow across multiple breaths during mechanical ventilation. An effective ventilation is prevented during such episodes. The objective of this exploratory study was to evaluate clinicians knowledge, perception and agreement regarding this condition. Methods: In a cross-sectional online survey, medical professionals with experience in intensive care medicine (mostly neonatal and pediatric patients) responded to 11 questions covering various clinical aspects of FV. Results: All respondents were familiar with the phenomenon and assessed it to be clinically relevant. Neonates were perceived to be predominantly affected. Promoting factors of FV included inadequate sedation and ventilation-associated factors. In clinical practice, the participants identified FV by clinical evaluation, analysis of ventilator waveforms and capnography and used both pharmacological and non-pharmacological intervention strategies. There was no consensus concerning the English terminology of the condition. Only few respondents were familiar with local guidelines to manage FV. Conclusion: Although there was consensus on the relevance of FV, diagnostics and management strategies were highly heterogeneous. Many participants agreed that poor patient-ventilator synchrony was an important cause of FV. Future work should focus on characterizing and generating evidence on FV.

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.

Background: Rheumatic mitral stenosis (RMS) disproportionately affects low-resource settings where echocardiography is limited. We evaluated foundation model (FM) feasibility for RMS detection using limited, indirectly labeled ECGs. Methods: An ECG-FM was fine-tuned using 804 RMS-positive ECGs per fold and externally validated across clinical and wearable lead configurations. Results: Internal AUROCs were 0.89, 0.87, and 0.87, for 12-Lead, 6-Lead, and 1-Lead respectively. External AUROCs were 0.82, 0.80, and 0.77. Performance remained comparable across demographic subgroups. Conclusions: FM fine-tuning enables RMS detection using limited ICD-derived labels, supporting further evaluation of reduced-lead ECG screening and echocardiography triage in resource-constrained settings.

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: Gastrointestinal (GI) motility disorders are prevalent, impose a significant burden, and are associated with disrupted spatial propagation of bioelectrical gastric slow waves. High-resolution endoscopic gastric mapping (EGM) is an emerging minimally invasive technique for imaging gastric electrical activity; however, EGM is performed alongside standard gastroscopy and requires procedural sedation, which may interact with gastric electrical activity, distorting clinical insights. This study explored the influence of procedural sedation on EGM-recorded slow-wave activity. Methods: EGM was performed for {approx}7 min in 32 patients undergoing upper GI endoscopy at two New Zealand hospitals. Endoscopists administered procedural sedation (fentanyl and midazolam) according to best clinical practice. The relationship between slow-wave amplitude and patient characteristics was then investigated using a variety of statistical techniques. Key Results: Gastric slow waves were detected in 30 of 32 patients who underwent EGM. Symptom scores and demographic measures were comparable between sites. Fentanyl (A, 93.4 {+/-} 13.7 g vs B, 137.5 {+/-} 33.7 g, p[≤]0.001) and midazolam (A, 5.0 {+/-} 0.9 mg vs B, 7.4 {+/-} 1.6 mg, p[≤]0.001) doses were higher at Hospital B than Hospital A. Correspondingly, slow-wave amplitudes were lower at Hospital B than Hospital A (A, 2.33 {+/-} 1.61 mV vs B, 0.86 {+/-} 1.01 mV, p=0.004). All other slow-wave metrics were consistent between hospitals. Combined sedative dose was negatively correlated with slow-wave amplitude (p=0.037). Conclusions & Inferences: Higher procedural sedative doses are associated with lower-amplitude gastric slow-waves. Procedural sedation dose and opiate administration should be carefully considered when interpreting GI electrophysiology measurements.

BackgroundTranscutaneous auricular vagus nerve stimulation (taVNS) depends on activating afferent vagal pathways, leading to central and physiological (parasympathetic) modulation. Explaining how taVNS changes cardiorespiratory physiology requires systematic characterization of the effects of electrode location and stimulation waveform using rigorous experimental controls. ObjectiveTo determine the acute effects of four taVNS stimulation configurations (left unilateral 25 Hz, 100 Hz, burst, and bilateral 25 Hz) on physiological markers of parasympathetic activation compared with configuration-matched earlobe stimulation in healthy subjects at rest. MethodsCustomized 8-mm ear-clip electrodes delivered monophasic 500 s pulses (anode anterior) to the tragus or a configuration-matched earlobe control across four stimulation configurations (left unilateral 25 Hz, 100 Hz, burst, and bilateral 25 Hz). Twenty-five healthy participants completed a randomized, single-blind, within-subject crossover study, undergoing both active and control stimulation for all four configurations (200 sessions total). Each session consisted of five 60-second stimulation blocks (1,000 stimuli total). Heart rate (HR), heart rate variability (HRV; RMSSD), and respiration were recorded continuously. Linear mixed-effects models compared physiological responses between tragus and earlobe stimulation during the first stimulation block (primary analysis), across 5-second HR intervals (secondary analysis), and averaged across all five stimulation blocks (post hoc analysis). ResultsDuring the first stimulation block, HRV was significantly higher during unilateral left 25 Hz tragus stimulation than during the matched earlobe control ({beta} = -10.34, t(33) = -2.63, p = .01, 95% CI [-18.05, -2.64]), consistent with increased parasympathetic activity. No significant differences were observed for HRV with the other stimulation configurations, or for HR or respiration under any configuration. Analysis of 5-second HR intervals likewise revealed no significant temporal effects for block one. Analysis of 5-second heart rate intervals across the five blocks identified differences between tragus and earlobe at discrete time points but did not show a consistent temporal pattern across the stimulation period. Across all five stimulation blocks, unilateral left 25 Hz tragus stimulation remained associated with higher HRV ({beta} = -6.56, t(124) = -3.85, p < .01, 95% CI [-9.90, -3.22]) and also produced an increase in HR relative to earlobe stimulation ({beta} = -1.05, t(24) = -2.57, p = .01, 95% CI [-1.85, -0.24]). Respiration was unaffected throughout. ConclusionIn this systematic study, unilateral left 25 Hz tragus stimulation produced physiological modulation consistent with vagal target engagement. These findings underscore the importance of rigorous dose-specific taVNS studies to establish reproducible physiological biomarkers.