Sleep-wake body temperature regulates tau secretion in mice and correlates with CSF and plasma tau in humans.

The sleep-wake cycle regulates interstitial fluid and cerebrospinal fluid (CSF) tau levels in both mouse and human by mechanisms that remain unestablished. Here, we reveal a novel pathway by which wakefulness increases extracellular tau levels in mouse and humans. In mice, higher body temperature (BT) associated with wakefulness and sleep deprivation increased CSF tau. In vitro, wakefulness temperatures upregulated tau secretion via a temperature-dependent increase in activity and expression of unconventional protein secretion pathway-1 components, namely caspase-3-mediated C-terminal cleavage of tau (TauC3), and membrane expression of PIP2 and syndecan-3. In humans, the increase in both CSF and plasma tau levels observed post-wakefulness correlated with BT increase during wakefulness. Our findings suggest sleep-wake variation in BT may contribute to regulating extracellular tau levels, highlighting the importance of thermoregulation in pathways linking sleep disturbance to neurodegeneration, and the potential for thermal intervention to prevent or delay tau-mediated neurodegeneration.

Reconsider photoplethysmogram signal quality assessment in the free living environment.

Objective.Assessing signal quality is crucial for biomedical signal processing, yet a precise mathematical model for defining signal quality is often lacking, posing challenges for experts in labeling signal qualities. The situation is even worse in the free living environment.Approach.We propose to model a PPG signal by the adaptive non-harmonic model (ANHM) and apply a decomposition algorithm to explore its structure, based on which we advocate a reconsideration of the concept of signal quality.Main results.We demonstrate the necessity of this reconsideration and highlight the relationship between signal quality and signal decomposition with examples recorded from the free living environment. We also demonstrate that relying on mean and instantaneous heart rates derived from PPG signals labeled as high quality by experts without proper reconsideration might be problematic.Significance.A new method, distinct from visually inspecting the raw PPG signal to assess its quality, is needed. Our proposed ANHM model, combined with advanced signal processing tools, shows potential for establishing a systematic signal decomposition based signal quality assessment model.

Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: the ARIC Neurocognitive Study.

BACKGROUND: Low physical activity (PA) measured from accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used. These monitors can provide long-term HRV data and, if embedded with an accelerometer, they can also provide PA data. Whether PA or HRV measured from long-term ECG monitors is associated with cognitive function among older adults is unknown. METHODS: Free-living PA and HRV were measured simultaneously over 14-days using the Zio ® XT Patch among 1590 participants in the Atherosclerosis Risk in Communities Study [aged 72-94 years, 58% female, 32% Black]. Total amount of PA was estimated by total mean amplitude deviation (TMAD) from the 14-day accelerometry raw data. HRV indices (SDNN and rMSSD) were measured from the 14-day ECG raw data. Cognitive factor scores for global cognition, executive function, language, and memory were derived using latent variable methods. Dementia or mild cognitive impairment (MCI) status was adjudicated. Linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Models were adjusted for demographic and medical comorbidities. RESULTS: Each 1-unit higher in total amount of PA was significantly associated with 0.30 higher global cognition factor scores (95% CI: 0.16-0.44), 0.38 higher executive function factor scores (95% CI: 0.22-0.53), and 62% lower odds of MCI (OR: 0.38, 95% CI: 0.22-0.67) or 75% lower odds of dementia (OR: 0.25, 95% CI: 0.08-0.74) versus unimpaired cognition. Neither HRV measure was significantly associated with cognitive function or dementia. CONCLUSIONS: PA derived from a 2-week ECG monitor with an embedded accelerometer was significantly associated with higher cognitive test performance and lower odds of MCI/dementia among older adults. By contrast, HRV indices measured over 2 weeks were not significantly associated with cognitive outcomes. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia. CLINICAL PERSPECTIVE: What Is New?: This cross-sectional study evaluated associations between physical activity (PA) and heart rate variability (HRV) measured over 14 days from a wearable ECG monitor with cognitive function.Higher total amount of PA was associated with higher global cognition and executive function, as well as lower odds of mild cognitive impairment or dementia.HRV indices measured over 2 weeks were not significantly associated with cognitive outcomes.What Are the Clinical Implications?: These findings replicate positive associations between PA and cognitive function using accelerometer data from a wearable ECG monitor with an embedded accelerometer.These findings raise the possibility of using wearable ECG monitors (with embedded accelerometers) as a promising tool for digital phenotyping of dementia.

Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration.

Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

Signal quality assessment of peripheral venous pressure.

Develop a signal quality index (SQI) for the widely available peripheral venous pressure waveform (PVP). We focus on the quality of the cardiac component in PVP. We model PVP by the adaptive non-harmonic model. When the cardiac component in PVP is stronger, the PVP is defined to have a higher quality. This signal quality is quantified by applying the synchrosqueezing transform to decompose the cardiac component out of PVP, and the SQI is defined as a value between 0 and 1. A database collected during the lower body negative pressure experiment is utilized to validate the developed SQI. All signals are labeled into categories of low and high qualities by experts. A support vector machine (SVM) learning model is trained for practical purpose. The developed signal quality index coincide with human experts' labels with the area under the curve 0.95. In a leave-one-subject-out cross validation (LOSOCV), the SQI achieves accuracy 0.89 and F1 0.88, which is consistently higher than other commonly used signal qualities, including entropy, power and mean venous pressure. The trained SVM model trained with SQI, entropy, power and mean venous pressure could achieve an accuracy 0.92 and F1 0.91 under LOSOCV. An exterior validation of SQI achieves accuracy 0.87 and F1 0.92; an exterior validation of the SVM model achieves accuracy 0.95 and F1 0.96. The developed SQI has a convincing potential to help identify high quality PVP segments for further hemodynamic study. This is the first work aiming to quantify the signal quality of the widely applied PVP waveform.

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising.

BACKGROUND: Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis. NEW METHOD: In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered. RESULT: SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates. COMPARISON WITH EXISTING METHODS: SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering. CONCLUSION: The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Arterial blood pressure waveform in liver transplant surgery possesses variability of morphology reflecting recipients' acuity and predicting short term outcomes.

We investigated clinical information underneath the beat-to-beat fluctuation of the arterial blood pressure (ABP) waveform morphology. We proposed the Dynamical Diffusion Map algorithm (DDMap) to quantify the variability of morphology.  The underlying physiology could be the compensatory mechanisms involving complex interactions between various physiological mechanisms to regulate the cardiovascular system. As a liver transplant surgery contains distinct periods, we investigated its clinical behavior in different surgical steps. Our study used DDmap algorithm, based on unsupervised manifold learning, to obtain a quantitative index for the beat-to-beat variability of morphology. We examined the correlation between the variability of ABP morphology and disease acuity as indicated by Model for End-Stage Liver Disease (MELD) scores, the postoperative laboratory data, and 4 early allograft failure (EAF) scores. Among the 85 enrolled patients, the variability of morphology obtained during the presurgical phase was best correlated with MELD-Na scores. The neohepatic phase variability of morphology was associated with EAF scores as well as postoperative bilirubin levels, international normalized ratio, aspartate aminotransferase levels, and platelet count. Furthermore, variability of morphology presents more associations with the above clinical conditions than the common BP measures and their BP variability indices. The variability of morphology obtained during the presurgical phase is indicative of patient acuity, whereas those during the neohepatic phase are indicative of short-term surgical outcomes.

Using the ear photoplethysmographic waveform as an early indicator of central hypovolemia in healthy volunteers utilizing LBNP induced hypovolemia model.

Objective. To study the photoplethysmographic (PPG) waveforms of different locations (ear and finger) during lower body negative pressure (LBNP) induced hypovolemia. Then, to determine whether the PPG waveform can be used to detect hypovolemia during the early stage of LBNP.Approach. 36 healthy volunteers were recruited for progressive LBNP induced hypovolemia, with an endpoint of -60 mmHg or development of hypoperfusion symptoms, whichever comes first. Subjects tolerating the entire protocol without symptoms were designated as high tolerance (HT), while symptomatic subjects were designated as low tolerance (LT). Subjects were monitored with an electrocardiogram, continuous noninvasive blood pressure monitor, and two pulse oximetry probes, one on the ear (Xhale) and one the finger (Nellcor). Stroke volume was measured non-invasively utilizing Non-Invasive Cardiac Output Monitor (NICOM, Cheetah Medical). The waveform morphology was analyzed using novel PPG waveforms indices, including phase hemodynamic index (PHI) and amplitude hemodyamaic index and were evaluated from the ear PPG and finger PPG at different LBNP stages.Main results. The PHI, particularly the phase relationship between the second harmonic and the fundamental component of the ear PPG denoted as∇φ2,during the early stage of LBNP (-15 mmHg) in the HT and LT groups is statistically significantly different (pvalue = 0.0033) with the area under curve 0.81 (CI: 0.616-0.926). The other indices are not significantly different. The 5 fold cross validation shows that∇φ2during the early stage of LBNP (-15 mmHg) as the single index could predict the tolerance of the subject with the sensitivity, specificity, accuracy andF1 as 0.771 ± 0.192, 0.71 ± 0.107, 0.7 ± 0.1 and 0.771 ± 0.192 respectively.Significance. The ear's PPG PHI which compares the phases of the fundamental and second harmonic has the potential to be used as an early predictor of central hypovolemia.

Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data.

Given a large clinical database of longitudinal patient information including many covariates, it is computationally prohibitive to consider all types of interdependence between patient variables of interest. This challenge motivates the use of mutual information (MI), a statistical summary of data interdependence with appealing properties that make it a suitable alternative or addition to correlation for identifying relationships in data. MI: (i) captures all types of dependence, both linear and nonlinear, (ii) is zero only when random variables are independent, (iii) serves as a measure of relationship strength (similar to but more general than R2), and (iv) is interpreted the same way for numerical and categorical data. Unfortunately, MI typically receives little to no attention in introductory statistics courses and is more difficult than correlation to estimate from data. In this article, we motivate the use of MI in the analyses of epidemiologic data, while providing a general introduction to estimation and interpretation. We illustrate its utility through a retrospective study relating intraoperative heart rate (HR) and mean arterial pressure (MAP). We: (i) show postoperative mortality is associated with decreased MI between HR and MAP and (ii) improve existing postoperative mortality risk assessment by including MI and additional hemodynamic statistics.

Application of de-shape synchrosqueezing to estimate gait cadence from a single-sensor accelerometer placed in different body locations.

Objective.Commercial and research-grade wearable devices have become increasingly popular over the past decade. Information extracted from devices using accelerometers is frequently summarized as 'number of steps' (commercial devices) or 'activity counts' (research-grade devices). Raw accelerometry data that can be easily extracted from accelerometers used in research, for instance ActiGraph GT3X+, are frequently discarded.Approach.Our primary goal is proposing an innovative use of thede-shape synchrosqueezing transformto analyze the raw accelerometry data recorded from a single sensor installed in different body locations, particularly the wrist, to extractgait cadencewhen a subject is walking. The proposed methodology is tested on data collected in a semi-controlled experiment with 32 participants walking on a one-kilometer predefined course. Walking was executed on a flat surface as well as on the stairs (up and down).Main results.The cadences of walking on a flat surface, ascending stairs, and descending stairs, determined from the wrist sensor, are 1.98 ± 0.15 Hz, 1.99 ± 0.26 Hz, and 2.03 ± 0.26 Hz respectively. The cadences are 1.98 ± 0.14 Hz, 1.97 ± 0.25 Hz, and 2.02 ± 0.23 Hz, respectively if determined from the hip sensor, 1.98 ± 0.14 Hz, 1.93 ± 0.22 Hz and 2.06 ± 0.24 Hz, respectively if determined from the left ankle sensor, and 1.98 ± 0.14 Hz, 1.97 ± 0.22 Hz, and 2.04 ± 0.24 Hz, respectively if determined from the right ankle sensor. The difference is statistically significant indicating that the cadence is fastest while descending stairs and slowest when ascending stairs. Also, the standard deviation when the sensor is on the wrist is larger. These findings are in line with our expectations.Conclusion.We show that our proposed algorithm can extract the cadence with high accuracy, even when the sensor is placed on the wrist.

Amplitude and phase measurements from harmonic analysis may lead to new physiologic insights: lower body negative pressure photoplethysmographic waveforms as an example.

The photoplethysmographic (PPG) waveform contains hemodynamic information in its oscillations. We provide a new method for quantitative study of the waveform morphology and its relationship to the hemodynamics. A data adaptive modeling of the waveform shape is used to describe the PPG waveforms recorded from ear and finger. Several indices, based on the phase and amplitude information of different harmonics, are proposed to describe the PPG morphology. The proposed approach is illustrated by analyzing PPG waveforms recorded during a lower body negative pressure (LBNP) experiment. Different phase and amplitude dynamics are observed during the LBNP experiment. Specifically, we observe that the phase difference between the high order harmonics and fundamental components change more significantly when the PPG signal is recorded from the ear than the finger at the beginning of the study. In contrast, the finger PPG amplitude changes more when compared to the ear PPG during the recovery period. A more complete harmonic analysis of the PPG appears to provide new hemodynamic information when used during a LBNP experiment. We encourage other investigators who possess modulated clinical waveform data (e.g. PPG, arterial pressure, respiratory, and autonomic) to re-examine their data, using phase information and higher harmonics as a potential source of new insights into underlying physiologic mechanisms.

Subthalamic high-beta oscillation informs the outcome of deep brain stimulation in patients with Parkinson's disease.

Background: The therapeutic effect of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease (PD) is related to the modulation of pathological neural activities, particularly the synchronization in the ß band (13-35 Hz). However, whether the local ß activity in the STN region can directly predict the stimulation outcome remains unclear. Objective: We tested the hypothesis that low-ß (13-20 Hz) and/or high-ß (20-35 Hz) band activities recorded from the STN region can predict DBS efficacy. Methods: Local field potentials (LFPs) were recorded in 26 patients undergoing deep brain stimulation surgery in the subthalamic nucleus area. Recordings were made after the implantation of the DBS electrode prior to its connection to a stimulator. The maximum normalized powers in the theta (4-7 Hz), alpha (7-13 Hz), low-ß (13-20 Hz), high-ß (20-35 Hz), and low-γ (40-55 Hz) subbands in the postoperatively recorded LFP were correlated with the stimulation-induced improvement in contralateral tremor or bradykinesia-rigidity. The distance between the contact selected for stimulation and the contact with the maximum subband power was correlated with the stimulation efficacy. Following the identification of the potential predictors by the significant correlations, a multiple regression analysis was performed to evaluate their effect on the outcome. Results: The maximum high-ß power was positively correlated with bradykinesia-rigidity improvement (r s = 0.549, p < 0.0001). The distance to the contact with maximum high-ß power was negatively correlated with bradykinesia-rigidity improvement (r s = -0.452, p < 0.001). No significant correlation was observed with low-ß power. The maximum high-ß power and the distance to the contact with maximum high-ß power were both significant predictors for bradykinesia-rigidity improvement in the multiple regression analysis, explaining 37.4% of the variance altogether. Tremor improvement was not significantly correlated with any frequency. Conclusion: High-ß oscillations, but not low-ß oscillations, recorded from the STN region with the DBS lead can inform stimulation-induced improvement in contralateral bradykinesia-rigidity in patients with PD. High-ß oscillations can help refine electrode targeting and inform contact selection for DBS therapy.

Prenatal stress perturbs fetal iron homeostasis in a sex specific manner.

The adverse effects of maternal prenatal stress (PS) on child's neurodevelopment warrant the establishment of biomarkers that enable early interventional therapeutic strategies. We performed a prospective matched double cohort study screening 2000 pregnant women in third trimester with Cohen Perceived Stress Scale-10 (PSS-10) questionnaire; 164 participants were recruited and classified as stressed and control group (SG, CG). Fetal cord blood iron parameters of 107 patients were measured at birth. Transabdominal electrocardiograms-based Fetal Stress Index (FSI) was derived. We investigated sex contribution to group differences and conducted causal inference analyses to assess the total effect of PS exposure on iron homeostasis using a directed acyclic graph (DAG) approach. Differences are reported for p < 0.05 unless noted otherwise. Transferrin saturation was lower in male stressed neonates. The minimum adjustment set of the DAG to estimate the total effect of PS exposure on fetal ferritin iron biomarkers consisted of maternal age and socioeconomic status: SG revealed a 15% decrease in fetal ferritin compared with CG. Mean FSI was higher among SG than among CG. FSI-based timely detection of fetuses affected by PS can support early individualized iron supplementation and neurodevelopmental follow-up to prevent long-term sequelae due to PS-exacerbated impairment of the iron homeostasis.

The Manifold Scattering Transform for High-Dimensional Point Cloud Data.

The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-like operators to general manifolds. The initial work on this model focused primarily on its theoretical stability and invariance properties but did not provide methods for its numerical implementation except in the case of two-dimensional surfaces with predefined meshes. In this work, we present practical schemes, based on the theory of diffusion maps, for implementing the manifold scattering transform to datasets arising in naturalistic systems, such as single cell genetics, where the data is a high-dimensional point cloud modeled as lying on a low-dimensional manifold. We show that our methods are effective for signal classification and manifold classification tasks.

Is the Median Hourly Ambulatory Heart Rate Range Helpful in Stratifying Mortality Risk among Newly Diagnosed Atrial Fibrillation Patients?

Background: The application of heart rate variability is problematic in patients with atrial fibrillation (AF). This study aims to explore the associations between all-cause mortality and the median hourly ambulatory heart rate range (AHRR˜24hr) compared with other parameters obtained from the Holter monitor in patients with newly diagnosed AF. Material and Methods: A total of 30 parameters obtained from 521 persistent AF patients' Holter monitor were analyzed retrospectively from 1 January 2010 to 31 July 2014. Every patient was followed up to the occurrence of death or the end of 30 June 2017. Results:AHRR˜24hr was the most feasible Holter parameter. Lower AHRR˜24hr was associated with increased risk of all-cause mortality (adjusted hazard ratio [aHR] for every 10-bpm reduction: 2.70, 95% confidence interval [CI]: 1.75-4.17, p < 0.001). The C-statistic of AHRR˜24hr alone was 0.707 (95% CI: 0.658-0.756), and 0.697 (95% CI: 0.650-0.744) for the CHA2DS2-VASc score alone. By combining AHRR˜24hr with the CHA2DS2-VASc score, the C-statistic could improve to 0.764 (95% CI: 0.722-0.806). While using 20 bpm as the cut-off value, the aHR was 3.66 (95% CI: 2.05-6.52) for patients with AHRR˜24hr < 20 bpm in contrast to patients with AHRR˜24hr ≥ 20 bpm. Conclusions:AHRR˜24hr could be helpful for risk stratification for AF in addition to the CHA2DS2-VASc score.

Denoising click-evoked otoacoustic emission signals by optimal shrinkage.

Click-evoked otoacoustic emissions (CEOAEs) are clinically used as an objective way to infer whether cochlear functions are normal. However, because the sound pressure level of CEOAEs is typically much lower than the background noise, it usually takes hundreds, if not thousands, of repetitions to estimate the signal with sufficient accuracy. In this paper, we propose to improve the signal-to-noise ratio (SNR) of CEOAE signals within limited measurement time by optimal shrinkage (OS) in two different settings: covariance-based optimal shrinkage (cOS) and singular value decomposition-based optimal shrinkage (sOS). By simulation, the cOS consistently enhanced the SNR by 1-2 dB from a baseline method that is based on calculating the median. In real data, however, the cOS cannot enhance the SNR over 1 dB. The sOS achieved a SNR enhancement of 2-3 dB in simulation and demonstrated capability to enhance the SNR in real recordings. In addition, the level of enhancement increases as the baseline SNR decreases. An appealing property of OS is that it produces an estimate of all single trials. This property makes it possible to investigate CEOAE dynamics across a longer period of time when the cochlear conditions are not strictly stationary.

A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification.

Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects-the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.

Fetal heart rate during maternal sleep.

Despite prolonged and cumulative exposure during gestation, little is known about the fetal response to maternal sleep. Eighty-four pregnant women with obesity (based on pre-pregnancy BMI) participated in laboratory-based polysomnography (PSG) with continuous fetal electrocardiogram monitoring at 36 weeks gestation. Multilevel modeling revealed both correspondence and lack of it in maternal and fetal heart rate patterns. Fetal heart rate (fHR) and variability (fHRV), and maternal heart rate (mHR) and variability (mHRV), all declined during the night, with steeper rates of decline prior to 01:00. fHR declined upon maternal sleep onset but was not otherwise associated with maternal sleep stage; fHRV differed during maternal REM and NREM. There was frequent maternal waking after sleep onset (WASO) and fHRV and mHRV were elevated during these episodes. Cross-correlation analyses revealed little temporal coupling between maternal and fetal heart rate, except during WASO, suggesting that any observed associations in maternal and fetal heart rates during sleep are the result of other physiological processes. Implications of the maternal sleep context for the developing fetus are discussed, including the potential consequences of the typical sleep fragmentation that accompanies pregnancy.

Cardiorespiratory coupling is associated with exercise capacity in patients with chronic obstructive pulmonary disease.

BACKGROUND: The interaction between the pulmonary function and cardiovascular mechanics is a crucial issue, particularly when treating patients with chronic obstructive pulmonary disease (COPD). Synchrogram index is a new parameter that can quantify this interaction and has the potential to apply in COPD patients. Our objective in this study was to characterize cardiorespiratory interactions in terms of cardiorespiratory coupling (CRC) using the synchrogram index of the heart rate and respiratory flow signals in patients with chronic obstructive pulmonary disease. METHODS: This is a cross-sectional and preliminary data from a prospective study, which examines 55 COPD patients. K-means clustering analysis was applied to cluster COPD patients based on the synchrogram index. Linear regression and multivariable regression analysis were used to determine the correlation between the synchrogram index and the exercise capacity assessed by a six-minute walking test (6MWT). RESULTS: The 55 COPD patients were separated into a synchronized group (median 0.89 (0.64-0.97), n = 43) and a desynchronized group (median 0.23 (0.02-0.51), n = 12) based on K-means clustering analysis. Synchrogram index was correlated significantly with six minutes walking distance (r = 0.42, p = 0.001) and distance saturation product (r = 0.41, p = 0.001) assessed by 6MWT, and still was an independent variable by multivariable regression analysis. CONCLUSION: This is the first result studying the heart-lung interaction in terms of cardiorespiratory coupling in COPD patients by the synchrogram index, and COPD patients are clustered into synchronized and desynchronized groups. Cardiorespiratory coupling is associated with exercise capacity in patients with COPD.

Interpretable morphological features for efficient single-lead automatic ventricular ectopy detection.

OBJECTIVE: We designed an automatic, computationally efficient, and interpretable algorithm for detecting ventricular ectopic beats in long-term, single-lead electrocardiogram recordings. METHODS: We built five simple, interpretable, and computationally efficient features from each cardiac cycle, including a novel morphological feature which described the distance to the median beat in the recording. After an unsupervised subject-specific normalization procedure, we trained an ensemble binary classifier using the AdaBoost algorithm RESULTS: After our classifier was trained on subset DS1 of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database, our classifier obtained an F1 score of 94.35% on subset DS2 of the same database. The same classifier achieved F1 scores of 92.06% on the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia database and 91.40% on the MIT-BIH Long-term database. A phenotype-specific analysis of model performance was afforded by the annotations included in the St. Petersburg INCART Arrhythmia database CONCLUSION: The five features this novel algorithm employed allowed our ventricular ectopy detector to obtain high precision on previously unseen subjects and databases SIGNIFICANCE: Our ventricular ectopy detector will be used to study the relationship between premature ventricular contractions and adverse patient outcomes such as congestive heart failure and death.