Niema M. Pahlevan

BACKGROUND Aortic morphology is an important consideration in patients who present with acute type B aortic dissection (TBAD). This study evaluates the relationship between the distal extent of dissection with malperfusion syndromes, clinical outcomes after thoracic endovascular aortic repair (TEVAR) and mortality. METHODS The Vascular Quality Initiative (VQI) database was queried from 2012 to 2022 for patients undergoing TEVAR for acute TBAD. Primary exposure variable was distal extent of dissection, categorized as thoracic (zones 2-5), abdominal (zones 6-9) or iliac (zones 10-11). Primary endpoints were 30-day and 2-year mortality. Secondary endpoints included postoperative malperfusion, resolution of malperfusion following TEVAR, and reoperation. Outcomes were compared between cohorts. RESULTS 2,455 patients were included. A more distal extent of dissection was associated with a stepwise increase in risk of lower extremity (5.8% vs. 8.7% vs. 27.4%), intestinal (5.1% vs. 13.6% vs. 19.8%) and renal (6.7% vs. 17.7% vs. 27.3%) ischemia on presentation (p<0.0001 for all). Distal extent of dissection was associated with increased rates of postoperative mesenteric (2.4% vs. 4.6% vs. 6.1%, p=0.0009) and renal (2.7% vs. 5.5% vs. 6.0%, p=0.0036) ischemia, and increased rates of reoperation (11.3% vs. 15.0% vs. 17.3%, p=0.0017). Distal extent of dissection was not associated with resolution of malperfusion after TEVAR, and not independently associated with 30-day or 2-year mortality (p>0.05 for all). CONCLUSION Although a more distal extent of aortic dissection is associated with increased rates malperfusion on presentation and increased rates of complications after TEVAR, it is not independently associated with mortality.

Background Clinical studies have shown that aortic arch pulse‐wave velocity (PWV aa ), a measure of local aortic stiffness, is a strong independent predictor of subsequent white matter hyperintensity volume and white matter integrity, both associated with cognitive decline, elevated stroke risk, vascular dementia, and neurodegenerative diseases. Total arterial compliance (TAC), a measure of global arterial stiffness, has been recognized as a marker of preclinical vascular disease. This study introduces a smartphone‐based method for the noninvasive measurement of PWV aa and TAC using carotid pressure waveforms acquired via smartphone. Methods This method uses intrinsic frequency analysis of smartphone‐acquired (iPhone) carotid pressure waveforms to assess PWV aa and TAC. The method was trained, validated, and blind‐tested on a cohort of 132 participants aged 20 to 90 years, including both healthy individuals and those with cardiovascular disease, all of whom underwent cardiac magnetic resonance imaging, tonometry, and iPhone waveform measurements. Results In the blind test set, our method achieved Pearson correlations of 0.81 and 0.80 for PWV aa and TAC, with biases of −0.20 m/s and −0.06 mL/mm Hg and limits of agreement of −4.09 to 3.68 m/s and −0.52 to 0.40 mL/mm Hg, respectively. In the heart failure population, correlations were 0.81 for both, with a PWV aa a bias of −1.07 m/s and TAC bias of −0.06 mL/mm Hg. Conclusions Our smartphone‐based method enables accurate assessment of local and global arterial stiffness metrics (PWV aa and TAC). It offers easy‐to‐use monitoring of vascular aging and arterial health, with important implications for identifying patients at higher risk of neurodegenerative and cardiovascular diseases. Registration URL: clinicaltrials.org ; Unique Identifier: NCT02240979.

BACKGROUND: Measurement of left ventricular end-diastolic pressure (LVEDP) is an established diagnostic method to evaluate heart failure but requires an invasive procedure. A noninvasive technique for detecting elevated LVEDP would improve the diagnosis of heart failure. Herein, we present the results of a multicenter study to validate a noninvasive system to detect elevated LVEDP. METHODS: The Vivio System includes a modified blood pressure cuff and single-lead ECG to capture brachial artery waveforms. A model was created to identify patients with elevated LVEDP (>18 mm Hg). For the invasive cohort, all patients were referred for coronary angiography and left heart catheterization for clinical indications. A group of 321 patients with no significant health problems were enrolled as a control cohort. Invasive LVEDP measurements were performed using Millar catheters. The training data set (n=262) contained 101 patients with LVEDP measurements (n=44 with LVEDP >18 mm Hg) and 161 controls. The validation data set (n=155) contained 75 patients with LVEDP measurements (n=40 with LVEDP >18 mm Hg) and 80 controls. RESULTS: Leave-one-out cross-validation on the training data set yielded a sensitivity of 0.84 (95% CI, 0.70-0.93]) and a specificity of 0.84 (95% CI, 0.79-0.89). The validation data set showed a sensitivity of 0.80 (95% CI, 0.64-0.91) and a specificity of 0.83 (95% CI, 0.75-0.90). CONCLUSIONS: The Vivio System can accurately detect elevated LVEDP and has the potential to significantly improve early detection of heart failure in the outpatient setting.

Introduction: Cerebral blood flow (CBF) signals contain physiological information about the dynamics of the heart, brain, and their interaction during ischemic stroke. Traditional frequency-based analyses may miss critical dynamic changes due to inter-subject variability. We hypothesize that a time-frequency approach can extract more detailed information from CBF’s physiological frequency bands to better detect stroke-induced cerebral hemodynamic changes. This study applies empirical mode decomposition (EMD) and the Hilbert transform to extract dynamic-specific features from CBF signals in a rat stroke model. Methods: Sixteen rats underwent transient right middle cerebral artery (MCA) occlusion, with CBF recorded continuously from the right cortex. 5-minute CBF recordings were analyzed at three timepoints: baseline, 1 hour post-MCA occlusion, and 3 hours post-reperfusion. EMD decomposed each signal into 13 intrinsic mode functions (IMFs), with instantaneous frequencies extracted via the Hilbert transform. IMFs were then recombined into five physiological bands: cardiac (~2–5 Hz), respiratory (~0.4-2 Hz), myogenic (~0.15-0.4 Hz), sympathetic (~0.04-0.15 Hz), and endothelial (~0.0095-0.04 Hz). The Hilbert transform was then applied to each band to compute a 95% area metric (CBF 95%-Area Index) from the analytic signal’s complex-plane trajectory. Statistical tests compared physiological states across timepoints. P-value &lt; 0.05 was considered significant. Results: The myogenic band (~0.15-0.4 Hz), associated with vascular smooth muscle activity, showed a significant reduction in the 95% area metric from baseline to occlusion (p&lt;0.0001), and from baseline to reperfusion (p&lt;0.05). No significant change between occlusion and reperfusion, suggesting persistent suppression of myogenic vasomotion during early reperfusion. Other bands showed minor or nonsignificant changes. Conclusion: Persistent myogenic oscillatory suppression during early reperfusion suggests a form of cerebral vascular stunning, analogous to stunned myocardium, where contractile function remains impaired despite restored perfusion. This may represent post-ischemic vascular dysfunction in the brain. Alternatively, it may reflect a microvascular no-reflow phenomenon within the cerebral circulation. Frequency-resolved CBF analysis offers a noninvasive approach to detect reperfusion-related vascular dysfunction, with the potential to guide acute stroke therapies and improve cerebrovascular outcomes.

Introduction: Myocardial infarct size (IS) is the most robust endpoint for evaluating cardioprotective strategies in preclinical ischemia/reperfusion studies. The gold standard for IS quantification in preclinical studies (triphenyl tetrazolium chloride (TTC) staining) is traditionally performed manually and is prone to inter-operator variability. Here, we propose a deep learning segmentation pipeline to automate IS quantification in TTC-stained rat heart sections. Methods: We used n=165 Sprague-Dawley rats (150–300 g, 1–2 months, 69% female). Myocardial infarction (MI) was induced using a standard occlusion/reperfusion model by occluding the proximal left coronary artery for 30 minutes, followed by 3 hours of reperfusion. After euthanasia, the left ventricle (LV) was excised, transversely sliced, and incubated in 1% TTC at 37 °C for 15 minutes to distinguish necrotic myocardium (pale white) from viable tissues (brick red, Fig. 1). Manual IS was quantified by contouring infarcted and total LV areas in each slice using ImageJ (NIH, USA). To automate the IS measurement from TTC-stained heart slices, we implemented a deep learning segmentation pipeline based on the mask region-based convolutional neural network (Mask R-CNN) architecture. Ground truth masks for infarcted regions and LV area were created using VGG Image Annotator. Images from n=140 rats were used for training, as well as an additional 1,400 images generated by data augmentation. All training and preprocessing pipelines were conducted in Python. Dice similarity coefficient (Dice score) was used to evaluate the model performance. The best-performing Mask R-CNN model was blindly tested on 25 additional MI rats. Results: Infarct sizes calculated from Mask R-CNN-generated segmentations showed strong agreement with the ones from expert-annotated manual segmentations from TTC-stained LV slices (R = 0.97, p &lt; 0.0001) when tested on heart slices from 25 additional MI rats, supporting the model’s accuracy and validity. Conclusions: Our results demonstrate that deep learning segmentation accurately and automatically quantifies infarct size from TTC-stained images without operator input. This automated approach is rapid, reproducible, and unbiased, significantly reducing inter-operator variability and manual workload in preclinical studies. By streamlining infarct size assessment in preclinical cardio-protection studies, it has the potential to improve consistency and translational value in cardiac research.

Background: We previously reported that empagliflozin (EMPA), a sodium-glucose co-transporter 2 inhibitor, significantly reduced infarct size and cerebral edema in a rat model of ischemic stroke when administered either chronically via oral route for 7 days prior to middle cerebral artery occlusion (MCAO) or given IV acutely 10 minutes before MCAO (doi: 10.1038/s41598-025-93483-7). This is clinically relevant for patients already receiving this drug for diabetes, heart failure, or other indications. However, the vast majority of individuals presenting with acute stroke are unlikely to be on EMPA at the time of stroke onset. The neuroprotective potential of EMPA when administered after stroke onset remains unclear. Therefore, we investigated the effect of post-stroke EMPA administration on infarct size following MCAO. Methods: Male Sprague-Dawley rats were randomly assigned to (1) control group, receiving intravenous saline 10 minutes after MCAO (n =13); and (2) acute EMPA group, treated with EMPA (20 mg/kg, IV, n = 12) at the same post-occlusion time point. Transient focal cerebral ischemia was induced by intraluminal filament occlusion of the MCA for 60 minutes, followed by a 3-hour reperfusion period. Brain tissues were then collected and sectioned for 2,3,5-triphenyltetrazolium chloride (TTC) staining to quantify cerebral infarct and edema volumes. Results: Rats treated with EMPA post-MCAO exhibited significantly reduced infarct volumes (4.89 ± 1.02% of total brain volume) compared to controls (9.99 ± 1.59%, p = 0.0137). However, there was no significant difference in cerebral edema between groups: 6.93 ± 0.91% in the EMPA group versus 7.84 ± 0.81% in controls (p = 0.463). Conclusion: This study shows that a single intravenous dose of EMPA administered 10 minutes after MCAO effectively reduces cerebral infarction but does not impact edema formation in a rat model of stroke.

Introduction: Myocardial ischemia results from an imbalance between oxygen supply and demand, typically affecting the subendocardium. The diastolic pressure time index (DPTI) and systolic pressure time index (SPTI) are well-established surrogates for myocardial oxygen supply and demand, respectively. Their ratio (DPTI: SPTI), the subendocardial viability ratio (SEVR), serves as an indicator of subendocardial perfusion adequacy. Methods: A total of 41 young, healthy adult male and female Sprague Dawley rats (n=20 female; weight 187–273 g) were randomized and exposed to: 1) purified air (n=20) and 2) electronic cigarette vapor without nicotine (EC NIC(-), n=21). Rats were exposed by nose-only inhalation for 4-5 hours/day, 4 days/week, for a total of 8 weeks (puff frequency=1 puff/min, puff duration=2 seconds, at a flow rate of 1.67 L/min). For EC(-), a third-generation mod-type vaporizer (VaporFi VEX 150 TC mod, with Volt Tank) was used with a tobacco-flavored e-liquid containing a 50/50 volume ratio of propylene glycol and vegetable glycerin (PG/VG). After 8 weeks of exposure, the rats were anesthetized and catheterized to measure invasive aortic and left ventricular pressure waveforms. These waveforms were used to quantify SPTI, DPTI, and SEVR (Fig. 1). Results: Exposure to EC NIC(-) resulted in a significant change in myocardial oxygen supply in male rats (p&lt;0.05) but not in females (Fig. 2). As there was no significant change in oxygen demand in either sex, male rats showed a significant reduction in the supply: demand ratio (SEVR) compared to air-exposed controls (Fig. 3). Conclusion: Our results suggest that chronic exposure to nicotine-free EC vapor alters myocardial oxygen dynamics in a sex-specific manner. Male rats exhibited impaired subendocardial perfusion following EC NIC(-) exposure, suggesting heightened vulnerability to ischemic imbalance. We propose these effects may be driven by sex-specific cardiac responses to components in the PG/VG base and flavoring agents, which can induce mild oxidative stress and vascular dysfunction, disrupting myocardial oxygen balance differently in males and females.

Introduction: Heart rate fragmentation (HRF) is an inter-beat interval dynamics approach that enhances the analysis of short-term heart rate variability. HRF has been shown to reflect disruptions in the neuroautonomic-electrophysiologic control of the sino-atrial node. Migraine, the fifth leading cause of disability worldwide, is increasingly recognized as a neurovascular disorder with significant autonomic dysfunction. However, HRF signatures in the context of migraine remain unexplored. Here, we investigate HRF dynamics in a validated rodent model of chronic migraine to identify electrophysiological correlates of altered heart-brain connectivity. Methods: We used a total number of n=20 Sprague-Dawley rats (50% female; 150-250g; 6-8 weeks), which were randomized into two groups: (1) Chronic migraine Group (n=10, 50% female): rats received repeated intraperitoneal (IP) administration of Nitroglycerin (NTG, 10 mg/kg), a well-established migraine trigger, every other day over a nine-day period (Fig1); (2) Control Group (n=10, 50% female): rats received an equivalent volume of saline at the same regime. At the last injection day, rats were anesthetized prior to NTG or saline injection for invasive electrocardiogram (ECG) recording via subcutaneous needle electrodes. HRF metrics were computed using a 2-minute ECG recording at 2 hours after NTG/saline administration. These included quantification of soft and hard inflection points (Soft: transitions between steady and accelerating/decelerating heart rate; Hard: abrupt switches between acceleration and deceleration). Results: Significant differences were observed between the control and chronic migraine groups (Fig.2) for three HRF metrics. PIPH% (percentage of hard inflection points in heart rate acceleration sign throughout the ECG recording) was significantly increased in the migraine group. W1s% and W2s% (percentages of only one and only two soft inflection points, respectively, in sequences of five consecutive heart rate intervals) were both significantly decreased in the migraine group. Conclusions: Our findings suggest alterations in heart-brain connectivity in rats with chronic migraine, as evidenced by distinct changes in the HRF. The results also indicate that HRF metrics may serve as novel, noninvasive biomarkers for detecting and monitoring migraine-related autonomic dysfunction. Future studies may explore the clinical utility of HRF-based techniques in management and monitoring of migraine patients.

Introduction: Healthy Vascular Aging (HVA) refers to the preservation of vascular function with advancing age, challenging the common belief that hypertension and increased arterial stiffness are unavoidable outcomes of aging (Hypertension, PMID: 28559398). This definition identifies individuals who retain young vascular characteristics beyond midlife. While traditional clinical metrics are used to assess HVA, waveform-derived biomarkers, such as cardiovascular intrinsic frequencies (IFs), may provide a non-invasive and physiologically grounded alternative. IFs extracted from carotid pressure waveforms have previously been shown to be associated with cardiovascular function and vascular health (Hypertension, PMID: 33390053). In this study, we examine the association between carotid-derived IFs and the HVA status. Methods: The study sample was drawn from the Original (Exam 26), Offspring (Exam 7), and Third Generation (Exam 1) cohorts of the Framingham Heart Study. As established in the literature, we assigned HVA to the individuals with age ≥ 50 years, systolic blood pressure &lt;140 mmHg, diastolic blood pressure &lt;90 mmHg (without hypertension medication), and a carotid-femoral pulse wave velocity &lt;7.6 m/s. After applying these criteria, a total of N = 2,242 participants remained for analysis. Carotid pressure waveforms were acquired using applanation tonometry. We used these waveforms and computed the IF metrics for each individual: ω 1c (IF during systole, related to left ventricular inotropic function and LV-arterial coupling) and ω 2c (IF during diastole, related to arterial function). The associations between the HVA status and carotid-derived IFs were assessed using box-and-whisker plots and the Mann–Whitney U test. Results: The carotid-based IF metrics showed statistically significant differences between individuals with and without HVA (p &lt; 0.0001 for all comparisons). The group-wise distributions of these parameters are illustrated in Figure 1, highlighting distinct shifts in IF values between the HVA and no-HVA groups. Lower ω 1c but higher ω 2c were observed in the HVA group. Conclusion: Our results demonstrated that Intrinsic frequencies of carotid pressure waveforms are associated with the status of vascular aging. The findings of this study support the potential utility of IFs as a non-invasive approach to assess vascular health aging.

Introduction: The American Heart Association’s (AHA) Life’s Simple 7 (LS7) score defines cardiovascular health (CVH) and promotes healthy lifestyle behaviors through seven key components (Circulation, PMID: 20089546). Intrinsic frequencies (IFs) derived from carotid pressure waveforms are shown to be associated with cardiovascular performance in the Framingham Heart Study (Hypertension, PMID: 33390053). Here, we study whether IFs derived from carotid pressure waveforms relate to the AHA CVH score in a large community cohort. Methods: The study population was drawn from the Original, Offspring, and Third Generation Cohorts of the Framingham Heart Study where all the required metrics were available (N=5460; mean age 48 years). Per AHA LS7, we calculated CVH scores using fasting glucose, cholesterol, blood pressure, BMI, smoking status, and physical activity (excluding diet). Carotid pressure waveforms were acquired using an arterial tonometry device. From each non-calibrated waveform, we computed IF parameters: ω 1c , IF of the coupled heart and vascular system during systole corrected by systolic period; ω 2c , IF of the decoupled vasculature during diastole corrected by cardiac period; and △ω c , the difference between the two, which is a metric for left ventricle (LV)-arterial coupling. The association between AHA CVH score, and carotid-derived IFs was evaluated using box-and-whisker plots. Box plots were used to visualize the distribution of IFs across CVH score groups, with red lines connecting the group medians to highlight trends. Statistical significance across groups was assessed using either ANOVA or Kruskal–Wallis tests. Results: All three IF parameters showed clear, systematic trends across the CVH score groups. Specifically, ω 1c declined, ω 2c increased, and △ω c exhibited a steep decreasing trend with increasing the AHA CVH score (Figs. 1–3), reflecting improved LV-arterial coupling and vascular function with better cardiovascular health. Both one-way ANOVA and Kruskal–Wallis tests confirmed statistically significant differences (p &lt; 0.0001) across the CVH score groups for all three IF parameters. Conclusion: Our results revealed that IFs of carotid pressure waveforms are associated with AHA CVH scores. The observed trends between IFs and CVH score were consistent with previous clinical and preclinical studies where higher ω 1 and △ω but lower ω 2 were associated with higher risk for incident composite cardiovascular disease events and incident heart failure.

Introduction: Myocardial infarction (MI) alters the heart’s electrophysiology, often seen as ST-segment deviation, T-wave inversion, or QRS distortion in electrocardiogram (ECG). While these features support diagnosis, they may miss early or subtle waveform changes in single-channel ECGs during ischemic injury. Capturing such changes could enhance MI detection, particularly in non-classical presentations. Here, we propose a new analytical approach that reveals advanced ECG morphology changes, to capture MI-related signatures not easily detectable by standard interpretation. Methods: Acute MI was induced in SD rats (n=13; Male; ~300g) via 30 minutes of proximal left coronary artery occlusion, followed by 3 hours of reperfusion. Necrosis was confirmed post-surgery via triphenyl tetrazolium chloride (TTC) staining. ECG signals were continuously recorded via subcutaneous needle electrodes. The ECG time-frequency eyeball method involves: (1) empirical mode decomposition to extract intrinsic mode functions (IMFs) from ECG signal; (2) the Hilbert Transform to derive analytic signal of each IMF; (3) rotational mapping of the analytic signals onto the complex plane, where they exhibited a distinct eyeball-shaped pattern for IMF1 (ECG eyeball, Fig1). To quantify ECG dynamic changes, we performed symmetry analysis on the ECG eyeballs using the Structural Similarity Index Measurement (SSIM). Specifically, each eyeball was mirrored across incrementally rotated axes, and SSIM was calculated between the original and mirrored images at each angle. The normalized area under the SSIM curve over all rotation angles (SSIM-AUC) was used as a global symmetry metric (Fig1). SSIM-AUC was computed at three time points: baseline, pre-reperfusion (MI with occluded coronary), and 3 hours post-reperfusion (early recovery after MI). 2-minute ECG recordings were used at each time point for computing the eyeballs. Results: SSIM-AUC significantly decreased after MI (P&lt;0.05), from baseline to both pre-reperfusion and post-reperfusion (Fig2). A modest post-reperfusion increase vs. pre-reperfusion was observed but not significant. Conclusion: We introduced a time-frequency-based analytics approach (ECG Eyeball) that maps multi-minute ECG data into a single interpretable pattern. Symmetry analysis of the ECG eyeball effectively captured MI-induced electrical changes. This method offers new directions for leveraging single-channel ECG data in noninvasive and interpretable tools for MI detection.

Introduction: Left ventricular pressure (LVP) waveforms offer critical insight into cardiac function after myocardial infarction. LVP is typically assessed via invasive catheterization, limiting routine use and longitudinal monitoring. We propose a novel analytical approach to reconstruct the entire LVP waveform using only carotid pressure waveforms (now measurable noninvasively with a phone camera) and standard echocardiography. We validated the method under normal and acute physiological conditions in an experimental model of myocardial ischemia and myocardial infarction (MI). Methods: Thirty-nine Sprague Dawley rats (28% female) were anesthetized and underwent coronary artery occlusion/reperfusion (30 min occlusion, 3 h reperfusion). Simultaneous LV and carotid pressures (via Millar Mikro-Tip catheters) and echocardiograms were recorded. LVP waveforms were reconstructed using a novel five-step analytical method at baseline, 15 min post-occlusion (ischemia), and 3 h post-reperfusion (acute MI, confirmed by TTC staining) for total of 71 cases. The reconstruction approach incorporated models of ventricular relaxation, diastolic filling, and systolic ejection governed by arterial-ventricular coupling, with constraints on temporal and morphological continuity of the waveform. Reconstructed waveforms were compared to invasive LVP recordings. Evaluation focused on accuracy in key clinical ischemia/MI metrics, including LV end-diastolic pressure (LVEDP) and subendocardial viability ratio (SEVR). SEVR is calculated as the ratio of myocardial oxygen supply to demand, serving as a surrogate for myocardial perfusion and correlates with cardiovascular risk. Results: Reconstructed LVP waveforms from carotid pressure closely matched invasive measurements during control, ischemia, and infarction (Figure 1). Reconstructed LVEDP strongly correlated with catheter measurements (Figure 2; r = 0.91), capturing its elevation during ischemia and partial recovery post-reperfusion. SEVR derived from reconstructions also closely matched invasive values (Figure 3; r = 0.96). Conclusions: Our findings show that the algorithm accurately reconstructs LVP waveforms and predicts clinically relevant metrics across physiological states. It captured elevated LVEDP and reduced SEVR during ischemia, with partial recovery after reperfusion. These results support its potential for noninvasive, longitudinal monitoring of left ventricular pressure in managing heart failure and myocardial infarction.

Introduction: Accurate assessment of cardiac output (CO), a standard cardiovascular performance index, is essential for diagnosing and managing a wide range of cardiovascular conditions, including heart failure, shock, and valvular disease (Eur Heart J.PMID: 2092985). However, standard methods such as echocardiography or thermodilution are either operator-dependent, resource-intensive, or invasive. This limits their use in routine screening and outpatient monitoring. A noninvasive, rapid method to estimate CO from a single arterial pressure waveform could transform cardiovascular care by enabling continuous or point-of-care evaluation. Aim: This study aimed to develop and validate a noninvasive method for estimating cardiac output using features extracted from a single carotid pressure waveform captured with a tonometry measurement device. Methods: A cohort of 2448 individuals (age range: 19–90 years) from the Framingham Heart Study was analyzed. All participants had consistent CO measurements across multiple echocardiographic recordings. Carotid pressure waveforms were obtained using an arterial tonometry device and calibrated using cuff-based brachial pressures. Reference aortic flow values were computed by first measuring the left ventricular outflow tract diameter from 2D echocardiography (parasternal long-axis view) to calculate the cross-sectional area. Then, the pulsed Doppler velocity waveform from the apical 5-chamber view is multiplied by this area to generate the aortic flow waveform over time. CO values were computed by averaging the flow waveform over the entire cardiac cycle. Intrinsic frequency (IF) parameters were computed from the carotid waveforms and used as inputs for machine learning models. Eighty percent of the data was used for model training, and the remaining twenty percent was reserved for blind testing. Results: Single-waveform CO estimation showed a Pearson correlation of 0.76, limits of agreement of ±1.09, and a bias of 0.00 compared to reference values in the blinded test set (Fig. 1 and 2). Conclusions: Estimating cardiac output from a single carotid pressure waveform offers a non-invasive and scalable tool for hemodynamic monitoring. This method may improve early detection and management of various cardiovascular conditions where cardiac output is critical, such as heart failure, cardiogenic shock, and myocardial infarction. This method is well-suited for both in-patient and remote patient monitoring.

Introduction: Left ventricular pulsatile workload (LVPW) is a clinically established marker of cardiac afterload and function, and it is strongly associated with cardiovascular morbidity and mortality. Elevated LVPW contributes to adverse ventricular remodeling, impaired cardiac performance, and the development of heart failure (HF) (Eur Heart J. PMID: 29947746). However, clinical adoption of LVPW assessment remains limited due to the requirement for simultaneous pressure and flow measurements. This study introduces a smartphone-based approach for estimating LVPW noninvasively from only carotid pressure waveforms, enabling accessible and scalable cardiovascular monitoring using only smartphone camera-derived signals. Methods: A clinical cohort of 115 participants (41% women, BMI 25.9 ± 5.5, age range 20–92 years, mean 53 ± 18) was studied, including 43 individuals with cardiovascular disease (17 ambulatory HF patients). Reference LVPW values were calculated using ascending aorta flow from phase-contrast MRI combined with carotid pressure waveforms acquired via applanation tonometry. Carotid pressure waveforms were also recorded using a custom iPhone 5S (Apple Inc.) application by placing the camera against the neck, over the carotid artery (Crit Care Med. PMID: 28441235). These waveforms were calibrated using cuff-based brachial pressures, and their intrinsic frequency (IF) parameters were extracted. Using these IF metrics, a physics-based machine learning model was trained on 80% of the dataset to approximate LVPW and evaluated on the remaining 20% in a blinded test. Results: Smartphone-derived LVPW estimates showed a strong correlation with the gold standard reference computed from pressure-flow values. The Pearson correlation coefficient was 0.83 for the blind test set and 0.86 among the HF patient subgroup, as shown in Fig. 1. Conclusions: LVPW can be reliably and non-invasively estimated using only carotid pressure waveforms captured with an unmodified smartphone camera (iPhone in this study). This noninvasive, low-cost approach may enable routine assessment of pulsatile afterload for both clinical and at-home cardiac monitoring. This may facilitate the delivery of precision medicine with timely treatment plan modifications in patients with HF whose outcomes are highly sensitive to increases in pulsatile afterload. This technique may also expand access to early cardiovascular risk stratification in other diseases.

Background Blood pressure variability (BPV) is an age-related hemodynamic risk factor for neurodegeneration, but it remains unclear whether distinct forms of BPV display independent or interactive effects on brain health. Objective Investigate whether high beat-to-beat BPV, when combined with increased pulse pressure variability, a form of BPV associated with arterial stiffness, exacerbates markers of neurodegeneration. Methods Older adults (N = 105) without major neurological or systemic disease were recruited for brain magnetic resonance imaging and continuous blood pressure (BP) monitoring to quantify beat-to-beat BPV through systolic average real variability (ARV) and pulse pressure variability using arterial stiffness index (ASI). The interactive effect of ARV and ASI on medial temporal lobe atrophy, plasma neurofilament light chain (NfL), and plasma glial fibrillary acidic protein (GFAP) was studied using hierarchical linear regression in older adults . Voxel-based morphometry was used to confirm region-of-interest analysis findings. Results The interaction between higher ARV and higher ASI was significantly associated with left-sided medial temporal lobe atrophy in both the region-of-interest (left hippocampus β = −252.79, p = 0.0002, right hippocampus β = −193.56, p = 0.001, left entorhinal cortex β = −0.13, p = 0.007), and false discovery rate-corrected voxel-based morphometry analysis (p = 0.03). The interactive effect was also significantly associated with increased plasma NfL (β = 3.88, p = 0.01), but not GFAP. Conclusions The interaction between ARV and ASI is independently associated with neurodegenerative markers, including medial temporal lobe atrophy and plasma NfL, in older adults. These findings suggest that greater hemodynamic instability is associated with increased risk for neurodegenerative processes.

Abstract Aims Non-invasive estimation of left ventricular pressure (LVP) is crucial for managing cardiovascular diseases such as heart failure and myocardial infarction (MI). Current clinical practices rely on invasive catheterization, limiting its feasibility for routine or longitudinal monitoring. This study evaluates the accuracy of a novel LVP reconstruction algorithm in preclinical (rat) experiments. Methods and results Using a standard coronary occlusion/reperfusion model (n = 39 rats), we validated our algorithm across three physiological states: baseline, myocardial ischaemia, and MI. LVP waveforms were reconstructed using only carotid pressure waveforms and echocardiographic measurements. Algorithm performance was assessed by comparing reconstructed LVP waveforms to invasively measured LVP, using key haemodynamic metrics such as left ventricular end-diastolic pressure (LVEDP) and the subendocardial viability ratio (SEVR). Agreements between waveforms were assessed using intraclass correlation coefficients (ICC), normalized Euclidean distance (NED), and differences in harmonic modulus. The algorithm accurately estimated LVEDP across all physiological states (mean absolute error: 1.5 mmHg), with strong correlation to invasively measured LVEDPs (r = 0.91). Predicted SEVR also showed strong agreement with measured values (r = 0.96). The algorithm captured the expected LVEDP elevation and SEVR reduction during myocardial ischaemia, and the metric's partial recovery after reperfusion. Waveform-level agreement demonstrated near-perfect alignment, with high ICC (98.5%), low NED (0.062), and minimal harmonic modulus differences (0.043) for all tested cases. Conclusion This study demonstrates that LVP can be accurately reconstructed using the proposed algorithm in rats. Our algorithm reliably captured key LVP metrics and waveform features across varying physiological states, supporting its potential for cardiac monitoring.

This study addresses discrepancies in the literature regarding the measurement of vortex formation time (VFT) as an index of diastolic function. We evaluate the clinical relevance of three existing VFT formulations and introduce a novel, fluid dynamics-based method (VFT piston ) that models the left ventricular base as a moving piston. Using healthy and heart-failure participants, we demonstrate that each VFT formulation captures distinct aspects of ventricular mechanics and VFT piston offers physiologically meaningful correlations with diastolic metrics.

Motivation: Maps of cerebrovascular reactivity (CVR) to CO2 facilitates detection of cerebrovascular disease and assess stroke risk, but the cumbersome acquisition and analysis of synchronized imaging and physiological data limits their use in large studies. Goal(s): Demonstrate a Blood Oxygen-Level Dependent (BOLD) and end-tidal CO2 (ETCO2) acquisition and analytical approach suitable for mapping CVR in a large study. Approach: We acquired and analyzed BOLD and ETCO2 changes during breathing tasks in 53 aging adults and calculated BOLD-CVR to CO2 with a frequency-domain coherence-weighting. Results: During breathing tasks, female versus male participants showed an age-dependent increase in BOLD-CVR to CO2. Impact: More accessible BOLD and ETCO2 acquisition and analysis techniques using mild breathing tasks can lead to more widespread use of BOLD-CVR to CO2 in a variety of populations and might facilitate more widespread characterization of cerebrovascular health and risk factors.

Myocardial infarction (MI) is a leading cause of death worldwide and the most common precursor to heart failure, even after initial treatment. Precise evaluation of myocardial injury is crucial for assessing interventions and improving outcomes. Extensive evidence from both preclinical models and clinical studies demonstrates that the extent and severity of myocardial injury (i.e., myocardial infarct size, ischemic risk zone, and no-reflow area) are critical determinants of long-term outcomes post-MI. This study aims to assess whether carotid pressure waveforms, analyzed using an intrinsic frequency (IF)-machine learning (ML) approach, can accurately quantify myocardial injury sizes: myocardial infarct size, ischemic risk zone, and no-reflow area. Acute MI was induced in N = 88 Sprague-Dawley rats using a standard coronary occlusion/reperfusion model. MI-injury sizes were obtained via histopathology. IF metrics were extracted from carotid pressure waveforms post-MI. ML classifiers were developed using 66 rats and externally tested on 22 additional rats. Our best developed model for infarct size achieved an accuracy of 0.95 (specificity = 0.95, sensitivity = 0.96). For the ischemic risk zone, the best model showed an accuracy of 0.85 (specificity = 0.90, sensitivity = 0.80), and for the no-reflow area, we reached an accuracy of 0.88 (specificity = 0.89, sensitivity = 0.86). To conclude, a hybrid physics-based ML approach applied to carotid pressure waveforms successfully classified MI-injury severity. As carotid pressure waveforms can be measured non-invasively and remotely (e.g., via smartphones), this proof-of-concept preclinical study suggests a translational potential for post-MI management, enabling timely interventions, improved patient monitoring, and mitigating adverse outcomes.

OBJECTIVE: Instantaneous determination of left ventricular (LV) diastolic function would be a useful aid in diagnosis and treatment of heart failure. The time constant of LV pressure decay (also known as Tau) is an established metric for evaluating LV stiffness and assessing LV diastolic function. &#xD;&#xD;Approach: In this study, we present a novel approach that uses a single arterial (aortic) pressure waveform to classify abnormal Tau through a physics-based machine learning (ML) methodology. This study is based on a clinical LV catheterization at the University of Southern California Keck Medical Center. We included 54 patients (13 females, age 36-90 (66.3±10.8), BMI 20.2-38.5 (27.8±4.6)) that were subjected to the same exclusion criteria of the primary study. Invasive pressure waveforms at LV and ascending aorta were measured using 2.5 F transducer tipped electronic micro-catheters. Intrinsic frequency (IF) parameters were computed from aortic pressure waveforms. Tau was calculated using an exponential curve-fitting approach based on LV pressure. Tau ranges were 25.7-86.5 ms (50.3±11), and Tau = 48 ms was used as a binary classification cut-off. Random forest and K-nearest neighbors classifiers were trained on 44 patients and blindly tested on 10 patients. 3- fold cross-validation was used to prevent overfitting. &#xD;&#xD;Main Results: Our proposed ML classifier model accurately predicts true Tau classes using physics-based features, where the most accurate one demonstrates 80.0% (elevated) and 100.0% (normal) success in predicting true Tau classes on blind data. &#xD;&#xD;Significance: We demonstrate that our proposed physics-based ML models can instantaneously classify Tau using information from a single aortic pressure waveform. Although an invasive proof, the required model inputs can be acquired non-invasively using carotid waveforms, working toward a smartphone-based, patient-activated tool for assessing diastolic dysfunction.&#xD.

Abstract Objective. The left ventricle (LV) pressure–volume (PV) loop provides comprehensive characteristic information into ventricular mechanics, aiding in the assessment of systolic and diastolic function. However, its routine clinical application is limited due to the invasiveness of conventional LV catheterization procedures. This study introduces a novel analytical framework for estimating LV pressure (LVP) waveforms noninvasively, using carotid pressure waveforms and routine cardiac imaging. Approach. The proposed method consists of a five-step analytical approach that integrates physical and physiological LV-aortic coupling relationships with a novel ventricular filling model. To assess the sensitivity and effectiveness of our method, we applied it on a clinical sample of 77 people (42% female), comprising healthy volunteers and heart failure (HF) patients, and analyzed the reconstructed PV-loops for key hemodynamic metrics. Main results. The proposed method robustly captured key hemodynamic changes associated with HF patients, including elevated LV end-diastolic pressure ( p &lt; 0.01), loss of inotropy ( p &lt; 0.001), and impaired ventricular efficiency ( p &lt; 0.001). Additionally, HF patients exhibited significantly smaller stroke work ( p &lt; 0.001), mean external power ( p &lt; 0.01), and contractility ( p &lt; 0.001) compared to the control group. These results align well with established clinical observations for HF, demonstrating the method’s ability to detect pathological ventricular modifications. Significance. The proposed noninvasive LVP estimation method provides physiologically and clinically relevant PV-loop metrics without requiring invasive catheterization. By reliably capturing ventricular dysfunction in HF patients, this approach offers a promising alternative for noninvasive cardiac assessment. Its ability to enable routine evaluation of LV mechanics has the potential to improve HF diagnosis and therapeutic management, facilitating earlier intervention and more personalized treatment strategies.

The non-Newtonian properties of blood flow have been widely debated in hemodynamic research, particularly for congenital heart defects. Many studies comparing Newtonian and non-Newtonian models have overlooked dimensional group consistency, resulting in comparisons influenced by inconsistent Reynolds numbers rather than viscosity effects. In this study, we address this issue by applying a generalized Reynolds number formulation to ensure consistent dimensionless group comparisons. We compare flow structures and hemodynamic metrics in 20 pediatric Fontan circulations using the non-Newtonian Casson model against both conventional and generalized Reynolds number-corrected Newtonian models. Our results show that the conventional Newtonian model significantly overestimates flow rotation and underestimates stagnation regions, potentially misrepresenting thrombosis risk. The generalized Reynolds number method, however, predicts flow structures, wall shear stress, and energy-based metrics more in line with the non-Newtonian model. Percentage of power loss estimates from the generalized method (17.7 [10.1, 22.7]; p &amp;lt; 0.05) align more closely with the non-Newtonian model (12.9 [7.0, 17.1]) than with the conventional approach (8.5 [4.3, 10.2]; p &amp;lt; 0.001), offering a more clinically relevant prediction. Additionally, indexed viscous dissipation from the generalized method (2.14 [1.17, 3.69] n.d.) is statistically indistinguishable (p = 0.97) from the non-Newtonian model (2.42 [1.07, 3.60] n.d.; p &amp;lt; 0.05). Our analysis highlights that while the generalized Reynolds number method cannot fully replicate local shear-thinning effects, it substantially improves upon the conventional Newtonian approach by correcting for viscosity mismatch. We emphasize the importance of dimensionless group consistency before drawing conclusions in hemodynamic studies and advocate for broader adoption of non-Newtonian models to obtain critical clinical insights.

Accurate and robust numerical simulation of hemodynamics is of interest for the identification and investigation of important physical or physiological quantities and their relationships to various cardiac and vascular functions. Comprehensive analysis requires efficiently simulating pressure and flow waves that travel and reflect through a complex network of vessels with varying geometric/material properties. This work introduces a new numerical methodology for modeling such wave propagation throughout the (closed-loop) circulation, employing a fast high-order (pseudo)spectral approach for resolving the well-established reduced-order one-dimensional Navier–Stokes hemodynamics formulations (coupled to hyperelastic tube laws) that govern the corresponding fluid–structure dynamics in each vascular segment. The model includes both systemic and pulmonary circulations and four heart chambers and four heart valves. Together with a correspondingly high-order treatment of multiscale zero-dimensional boundary conditions based on time-dependent ordinary differential equations, the overall solver has a number of attractive qualities: high-order accuracy in time and space; fast Fourier transform (FFT)-level computational efficiency; little to no numerical pollution errors (faithfully preserving the diffusion and dispersion characteristics of the underlying continuous operators); relatively mild Courant–Friedrichs–Lewy constraints for explicit temporal integration methods; robustness to extreme physiological parameters; and stable incorporation of the nonlinear and nonstationary coupling to other cardiovascular system components (e.g., heart chambers, valves, and microvasculature). The convergence properties, computational performance, and physiological accuracy of the proposed framework are demonstrated through a variety of numerical experiments that include applications to community benchmark problems previously proposed for mutual validation with other solvers (and three-dimensional or in vitro reference solutions).

Aims: Clinical studies show that pulsatile haemodynamics and pressure waveform analysis are valuable for the diagnosis and prognosis of hypertension and heart failure (HF). While generalized transfer functions (GTFs) have shown clinical significance, some studies report limitations with GTF in capturing central pulsatile haemodynamics. This study introduces a hybrid time-frequency, machine learning-based transfer function that reconstructs central pressure waveforms from peripheral measurements, accurately capturing central pulsatile haemodynamics and arterial wave-based information. Methods and results: Our method uses Fourier harmonics for approximating the pressure waveform. The model is trained on these harmonics using a feed-forward neural network (FNN) with a custom time-domain cost function that captures the full temporal dynamics of physiological events during a cardiac cycle. The final hybridized-FNN transfer function model is trained, tested, and validated on data from the Framingham Heart Study (6698 participants). Our method produces carotid waveforms with median normalized mean squared error (%NMSE) values of 0.09 and 0.10 for brachial and radial inputs, compared to 0.42 and 0.26 for GTF, with similar accuracy improvements in other metrics. Correlation coefficients for the first and second forward wave times and amplitudes are 0.97, 0.93, 0.82, and 0.79 with brachial input, and 0.97, 0.92, 0.87, and 0.80 with radial input, vs. as low as 0.22 and 0.31 for GTF. Overall, our method significantly improved correlations across similarity, morphology, and wave-based parameters. Conclusion: Our hybridized FNN transfer function approach enables robust calculation of the central arterial pressure waveform from a single measured peripheral waveform, preserving key physiological sequences in a cardiac cycle.

BACKGROUND: Poor cerebrovascular reactivity is associated with a higher risk of cerebrovascular disease. The most common method to study cerebrovascular reactivity in aging adults, transcranial Doppler ultrasound, yields measurements in large intracranial arteries, but not in regional brain parenchyma that may be more impaired in some disease processes. Measurements derived from transcranial Doppler ultrasound suggest that there are sex differences in cerebrovascular reactivity for aging adults. We investigated the association between age and sex on cerebrovascular reactivity using blood oxygen level dependent (BOLD) magnetic resonance imaging in a representative group of aging adults. METHODS: This cross-sectional study investigated BOLD cerebrovascular reactivity to CO 2 in a representative group of aging adults, 51 to 83 years old. We manipulated end-tidal carbon dioxide with breathing exercises and evaluated changes in 6 brain regions: whole brain, white matter, cortical gray matter, subcortical gray matter, left hippocampus, and right hippocampus. We used 1 linear regression per region to investigate the effects of age, sex, and their interaction on BOLD cerebrovascular reactivity. RESULTS: We report an age-by-sex interaction for all brain regions ( P ≤0.050), except cortical gray matter ( P =0.062). For white matter and subcortical gray matter, female participants trended toward an age-related BOLD cerebrovascular reactivity increase ( P ≤0.058), while male participants did not change with age ( P &gt;0.580). In the whole brain and bilateral hippocampi, the age trends for each sex were in opposite directions but not significant ( P &gt;0.211). We report a main effect of sex (female greater than male participants) for subcortical gray matter and the right hippocampus ( P ≤0.048) and no main effect of age in any model. CONCLUSIONS: We present the first report of age-related BOLD cerebrovascular reactivity increases in older female participants and higher BOLD cerebrovascular reactivity in older female compared with male participants. Sex and age-by-sex-based differences seem to be driven by changes in white matter, subcortical gray matter, and bilateral hippocampi.

Sodium-glucose co-transporter 2 (SGLT2) inhibitors have demonstrated potential neuroprotective and cardioprotective effects in preliminary studies. This study evaluates the efficacy of empagliflozin (EMPA) in reducing ischemia/reperfusion damage in both the brain and heart using rat models. Ischemic stroke and myocardial infarction (MI) were induced in male Sprague-Dawley rats, which were randomized into three groups: (1) Control (no EMPA), (2) Acute treatment (EMPA, 10 mg/kg IV, administered 10 min before ischemia and 1 min before reperfusion), and (3) Chronic treatment (EMPA, 20 mg/kg in food for 7 days before ischemia). Stroke was induced by middle cerebral artery occlusion (MCAO) for one hour, followed by 3 h of reperfusion, and MI was induced by left coronary artery occlusion for 30 min, followed by 3 h of reperfusion. Brain and heart tissues were analyzed for anatomic size of myocardial infarction and stroke. In the brain, cerebral infarction was significantly smaller in both EMPA treatment groups compared to controls (acute: 3.7 ± 1.2%, chronic: 6.9 ± 2.1% vs. control: 14.5 ± 2.5%, p < 0.05). Edema was also reduced in the EMPA groups (acute: 5.5 ± 0.9%, chronic: 5.9 ± 0.8% vs. control: 9.6 ± 1.2%, p < 0.05). In the heart, MI size was significantly reduced in both EMPA groups (acute: 46.9 ± 2.0%, chronic: 48.8 ± 5.8% vs. control: 70.0 ± 2.6%, p < 0.05), and no-reflow size was smaller in the EMPA groups (acute: 36.3 ± 3.3%, chronic: 33.9 ± 4.3% vs. control: 53.4 ± 3.3%, p < 0.05). EMPA treatment, both acute and chronic, significantly reduces cerebral infarct volume and edema, as well as myocardial infarct size and no-reflow in rat models of ischemic stroke and myocardial ischemia/reperfusion, indicating substantial neuroprotective and cardioprotective effects.

Wave propagation in the heart tube is key to establishing an early pumping mechanism, as explained by impedance pump theory in zebrafish. Though initially proposed for embryonic blood circulation, the role of impedance-like behaviour in the mature cardiovascular system remains unclear. This study focuses on the understudied physiological mechanism of longitudinal displacement in the adult aorta caused by the long-axis motion of the heart. Using magnetic resonance imaging on 159 individuals, we compared aortic displacement profiles between a control group and those with heart failure, revealing a significant difference in aortic stretch between the two groups. Building on this clinical evidence, we conducted in vitro experiments to isolate the effects of longitudinal aortic wave pumping by eliminating the pumping action of the heart. We identified three biomechanical properties of stretch-related longitudinal wave pumping that exhibit characteristics like impedance pump: (i) a nonlinear flow–frequency relationship, (ii) bidirectional flow, and (iii) the potential for both positive and negative flow at a fixed frequency, contingent upon the aorta’s wave speed dictating the wave state. Our results demonstrate for the first time that this mechanism generates a significant flow, potentially providing a supplementary pumping mechanism for the heart.

Background: Emerging evidence suggests that empagliflozin (EMPA) may offer additional benefits beyond its primary use in diabetes. Ischemic stroke is known to cause distinctive alterations in EEG signals. However, the influences of EMPA on the stroke-induced EEG changes has not been investigated. Here, we study such influences via EEG time-frequency features using the Hilbert-Huang Transform (HHT) and examine how these features correlate with the cerebral infarction. Methods: n= 47 male SD rats were randomized into 3 groups: 1] Control (n = 16, regular diet); 2] Acute EMPA treatment (n = 16, EMPA 10 mg/kg, IV given at 10 mins prior to middle cerebral artery occlusion (MCAO) and 1 min prior to reperfusion); 3] Chronic EMPA treatment (n = 15, EMPA by food, 20 mg/kg for 7 days before MCAO). To induce ischemic stroke, standard MCAO was performed for 1 hour followed by 3 hours of reperfusion. Post-surgery, triphenyltetrazolium chloride (TTC) staining was used to measure volume of cerebral infarction. EEG signals were continuously recorded throughout the procedure. We applied the Hilbert-Huang Transform (HHT) to analyze the EEG signals. HHT includes 2 steps: 1] empirical mode decomposition (EMD) to decompose the EEG signal into a set of intrinsic mode functions (IMFs); 2] the Hilbert Transform is applied to each IMF to compute analytic signals. Such analytic signals are represented in the complex plane, where they often form circular or elliptical contours (Fig. 1). The area of these contours in the complex plane contains unique information about variations in the signal properties. We computed the HHT area metric for the first four IMFs by using EEG recordings at 3 hours post-reperfusion (duration: 2 minute). Results and Conclusions: Our results showed the control group demonstrates higher correlations between the HHT area metric and the cerebral infarction size, compared to acute or chronic EMPA treatments (Fig. 2), with an especially significant correlation for IMF1in controls (R= -0.76). No significant difference was found in the HHT area metric between control, acute, and chronic EMPA (Fig. 3). It revealed that while EMPA treatments do not alter the EEG time-frequency features compared to controls, control group exhibits a stronger correlation between EEG features and cerebral infarction size. This indicates that EMPA's neuroprotective effects might not be directly reflected in EEG changes, highlighting the need for further investigation of its mechanisms.

Background: Ischemic stroke remains a major cause of mortality and disability, significantly affecting patient quality of life despite advancements in treatment. Analysis of electroencephalogram (EEG) is crucial for assessing brain activity, offering insights into different physiological states. While ischemic stroke induces notable changes in EEG signals, the specific patterns and their implications are not fully understood. Here, we use the Hilbert-Huang transform (HHT) to analyze the EEG signal before, during, and after ischemic stroke in rats. Methods: The standard intraluminal filament middle cerebral artery (MCA) occlusion model was used to induce ischemic stroke in 16 male Sprague Dawley rats (8-10 weeks old). A suture was advanced into the internal carotid artery to occlude the MCA for 1 hour, followed by 3 hours of reperfusion (Fig. 1). Cerebral infarction was confirmed post-surgery using TTC staining technique. EEG signals were continuously recorded throughout the procedure. We applied the Hilbert-Huang Transform (HHT) to analyze the EEG signals. HHT involves two main steps: 1) empirical mode decomposition (EMD), to decompose the EEG signal into a set of intrinsic mode functions (IMF); 2) the Hilbert Transform, which is applied to each IMF to obtain analytic signals. These analytic signals are represented in the complex plane, where they often exhibit circular or elliptical forms (Fig. 2). The area of these circles in the complex plane reflects unique information on variations in the signal properties. We computed the HHT area metric for the first 4 IMFs at three timepoints: baseline, 1 hour post-MCA occlusion (pre-reperfusion), and 3 hours post-reperfusion. 2-minute EEG recordings were used at each timepoint for the HHT analysis. Results and Conclusions: Significant changes (P&lt;0.05) were observed in the HHT area metric after the ischemic stroke occurrence (Fig. 3). For all IMFs, the HHT area metric reduced significantly from baseline to pre-MCA-reperfusion. However, it increased significantly from pre-MCA-reperfusion to 3 hours post-reperfusion. Comparing the baseline with post-reperfusion timepoint, significant reduction was still observed. Our results showed for the first time that the HHT can effectively analyze EEG signals to capture changes induced by ischemic stroke. Our new finding highlights the potential of HHT in providing insights into stroke-related changes in EEG, offering a valuable tool for detection or monitoring ischemic stroke.

Introduction: Heart rate variability (HRV), which represents fluctuations in heart rate, provides critical insights into autonomic regulation and cardiovascular function. This study aims to evaluate how HRV metrics change before, during, and after an ischemic stroke in a rat model, with a focus on understanding the impact of stroke and subsequent reperfusion on autonomic control. Methods: We employed the standard intraluminal suture middle cerebral artery (MCA) occlusion model to induce ischemic stroke in anesthetized adult male and female Sprague Dawley rats (n=11, 2-3 months old, average body weight: 296 ± 47 g, 27% female). The procedure involved temporary occlusion of the common carotid artery (CCA) while advancing a suture into the internal carotid artery to occlude the MCA for 1 hour, followed by 3 hours of reperfusion (Fig. 1). Electrocardiogram (ECG) signals were continuously recorded throughout the procedure. Post-surgery, cerebral infarction was confirmed via the triphenyl tetrazolium chloride (TTC) staining technique. HRV metrics were analyzed from 1-minute ECG recordings taken at three time points: baseline, 1 hour post-MCA occlusion (pre-reperfusion), and 3 hours post-reperfusion. Results and Conclusions: Significant alterations in HRV metrics were observed between baseline and 3 hours after reperfusion (p&lt;0.05; Fig. 2; see figure’s caption for the metric descriptions). Specifically, high-frequency relative power percentage (HF%) and the SD1/SD2 ratio decreased significantly, while low-frequency relative power percentage (LF%) and the LF/HF ratio increased significantly. Additionally, the LF/HF power ratio showed a notable increase from baseline to 1 hour post-MCA occlusion. Our findings reveal distinct HRV metric changes associated with the ischemic stroke. The observed alterations reflect a shift of autonomic regulation towards sympathetic dominance, which intensifies after reperfusion, supporting other preclinical and clinical findings. These results suggest the potential for developing non-invasive, HRV-based techniques for real-time detection and monitoring of ischemic stroke. Further research could enhance these techniques' applicability in clinical settings.