Reconsider photoplethysmogram signal quality assessment in the free living environment

BACKGROUND

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.

METHOD

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.

RESULT

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.

CONCLUSION

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.

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.

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.

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.

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.

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.

[Effects and clinical significance of NLRP3 inflammasome activated by IL-17A in CRSwNP]

Objective: To investigate the effects and clinical significance of NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome activated by interleukin (IL)-17A in chronic rhinosinusitis with nasal polyps (CRSwNP). Methods: Patients underwent nasal endoscopic surgery in the Third Affiliated Hospital of Sun Yat-sen University from January 2020 to December 2021 were collected, including 28 CRSwNP (including 19 males and 9 females, aged 19 to 67 years), 22 chronic rhinosinusitis without nasal polyps (CRSsNP) and 22 controls. qRT-PCR was used to detect the expressions of IL-17A, NLRP3, IL-1β and IL-18 in the three groups, and their correlations were analyzed. The positions of IL-17A, NLRP3 and IL-18 in nasal polys were analyzed by immunofluorescence. Western Blotting and ELISA were employed to detect the expression of NLRP3, IL-1β and IL-18 in the human nasal epithelial cells after using IL-17A stimulation or IL-17A receptor inhibitor. Immunofluorescence was used to observe the NLRP3, IL-1β, and IL-18 protein expression after IL-17A stimulating human nasal epithelial cells, and after the use of IL-17A receptor inhibitor and NLRP3 inhibitor MCC950. The correlations between NLRP3, IL-1β, IL-18 and CT scores, nasal endoscopic scores, visual analogue scale (VAS) scores, and sino-nasal outcome test (SNOT) 22 scores of CRSwNP patients were analyzed. SPSS 20.0 software was used for statistical analysis. Results: The expressions of IL-17A, NLRP3, IL-1β and IL-18 in the tissues of CRSwNP patients were significantly higher than those in CRSsNP group(P=0.018,P<0.001,P=0.005, P=0.016) and the control group(all P<0.001). IL-17A was positively correlated with the expression of NLRP3, IL-1β, and IL-18(r ralue was 0.643,0.650,0.629,respectively, all P<0.05). IL-17A, NLRP3, and IL-18 were co-localized in the epithelial propria of polyp tissue. IL-17A stimulated the expressions of NLRP3, IL-1β, and IL-18 in human nasal epithelial cells. After the use of IL-17A receptor inhibitor, the expressions of NLRP3, IL-1β, and IL-18 were significantly down-regulated. After the use of NLRP3 inhibitor MCC950, IL-17A was significantly down-regulated to promote the expression of NLRP3, IL-1β, and IL-18. The expressions of NLRP3, IL-1β and IL-18 were positively correlated with CT, nasal endoscopy, VAS, and SNOT22 scores in patients with CRSwNP. Conclusions: IL-17A promotes the release of IL-1β and IL-18 by activating the NLRP3 inflammasome and aggravates the severity of the disease in CRSwNP.

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.&#xD;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 (CNAP), and two pulse oximetry probes, one on the ear (Xhale) and one the finger (Nellcor). Stroke volume (SV) was measured non-invasively utilizing NICOM (Cheetah Medical). The waveform morphology was analyzed using novel PPG waveforms indices, including phase hemodynamic index (PHI) and amplitude hemodyamaic index (AHI) and were evaluated from the ear PPG and finger PPG at different LBNP stages.&#xD;Main Results: The PHI, particularly the phase relationship between the second harmonic and the fundamental component of the ear PPG denoted as 〖∇φ〗_2, during the early stage of LBNP (-15 mmHg) in the HT and LT groups is statistically significantly different (p value=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 〖∇φ〗_2 during the early stage of LBNP (-15 mmHg) as the single index could predict the tolerance of the subject with the sensitivity, specificity, accuracy and F1 as 0.771±0.192, 0.71±0.107, 0.7±0.1 and 0.771±0.192 respectively.&#xD;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. &#xD.

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&#xD;popular over the past decade. Information extracted from devices using accelerometers is fre- quently 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.&#xD;&#xD;Approach: Our primary goal is proposing an innovative use of the de-shape synchrosqueez- ing transform to analyze the raw accelerometry data recorded from a single sensor installed in different body locations, particularly the wrist, to extract gait cadence when 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).&#xD;&#xD;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.&#xD;&#xD;Conclusion: We show that our proposed algorithm can extract the cadence with high accuracy, even when the sensor is placed on the wrist.&#xD.

[Establishment and behavioral evaluation of a mouse model of long-term free-choice alcohol drinking]

OBJECTIVE

To establish a model of long-term free drinking mouse by feeding mice with alcohol to simulate the state of human voluntary long-term drinking, and on this basis, to further discuss the evaluation criteria of long-term free drinking mice model in sports, anxiety and cognitive behavior.

METHODS

Forty six-week-old SPF C57BL/6 male mouse were randomly divided into two groups: Long-term free drinking group (n=20) and normal control group (n=20). The two groups were given solid feed normally. The long-term free drinking group was free to take 10% alcohol and water every day, while the normal drinking group only took water every day. The mice were fed for 7 months, and were evaluated by a series of behavioral methods, including Rota-rod test, balance beam test, open filed test, the elevated plus maze, two-box social behavior, new object recognition, Y maze and water maze.

RESULTS

With the increase of drinking days, the mice showed significant alcohol addiction in the alcohol preference test. With the increase of alcohol intake, the mice in the long-term free choice drinking group had slightly shiny fur and reduced diet. Compared with the control group, the weight gain began to slow down from the third month, and the weight decreased significantly by the sixth and seventh months (P=0.006, P < 0.001). The mice showed reduced balance locomotion ability (P=0.003, P=0.001) in the rotary bar and balance beam test. In the open field and elevated cross test, the mice had obvious anxiety-like behavior (P < 0.001). The mice showed decreased social ability in the two boxes of social behavior (P < 0.016). In the experiment of new object recognition and Y maze, the exploration of new object decreased (P=0.018, P=0.040). In the water maze, cognitive functions, such as learning and spatial memory were reduced (P < 0.001).

CONCLUSION

The successful establishment of the long-term free drinking mouse model is more convenient for us to carry out further research on the neural mechanism of alcohol addiction, and lays an experimental foundation for exploring the neural mechanism of alcohol addiction and related new targets.

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.

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.

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 β band (13–35 Hz). However, whether the local β activity in the STN region can directly predict the stimulation outcome remains unclear.

Objective

We tested the hypothesis that low-β (13–20 Hz) and/or high-β (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-β (13–20 Hz), high-β (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-β power was positively correlated with bradykinesia–rigidity improvement (rs = 0.549, p &lt; 0.0001). The distance to the contact with maximum high-β power was negatively correlated with bradykinesia–rigidity improvement (rs = −0.452, p &lt; 0.001). No significant correlation was observed with low-β power. The maximum high-β power and the distance to the contact with maximum high-β 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-β oscillations, but not low-β oscillations, recorded from the STN region with the DBS lead can inform stimulation-induced improvement in contralateral bradykinesia–rigidity in patients with PD. High-β oscillations can help refine electrode targeting and inform contact selection for DBS therapy.

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.

[The expression profile and potential regulatory mechanism of ACE2 in chronic rhinosinusitis with nasal polyps]

Objective: To preliminarily analyze the expression of angiotensin-converting enzyme 2 (ACE2) and to investigate its potential regulatory mechanism in chronic rhinosinusitis with nasal polyps (CRSwNP). Methods: Patients underwent nasal endoscopic surgery in the Third Affiliated Hospital of Sun Yat-sen University from February 2020 to May 2021 were selected, including 17 males and 6 females, aging from 23 to 66 years old. Expression of ACE2 was evaluated via immunohistochemical staining in controls with non-chronic rhinosinusitis, non-eosinophilic CRSwNP (non-ECRSwNP), and eosinophilic CRSwNP (ECRSwNP) tissue, respectively. Correlations between ACE2 and the indicated Th1/Th2-related cytokines (IFN-γ, IL-4, IL-5, IL-13, IL-25, IL-33, TSLP and periostin) were analyzed based on GSE72713 dataset. Protein-protein interaction (PPI) network was constructed via string database, immune infiltration of GSE72713 dataset was evaluated using cibersort algorithm. ACE2 was comprehensively analyzed by microRNA regulatory network, gene set enrichment analysis (GSEA) and pharmacological analysis. Statistical analysis was performed using GraphPad 7.0 and SPSS 20.0 software. Results: ACE2 was up-regulated in non-ECRSwNP compared with ECRSwNP. Microarray analysis showed that ACE2 was positively correlated with IFN-γ while inversely correlated with IL-5, IL-13 and periostin significantly. Analysis of immune infiltration suggested that ACE2 expression correlated positively with the number of M1 macrophage while negatively with M2 macrophage. GSEA demonstrated that interferon-related signaling pathways were up-regulated in non-ECRSwNP, and miRNA-200B/miRNA-200C/miRNA-429 pathways targeting ACE2 were enriched in ECRSwNP. Results of pharmacological analysis indicated that ampicillin was able to promote the expression of ACE2 whereas acetaminophen could down regulated the expression of ACE2. Conclusion: Expression pattern of ACE2 is varied in non-ECRSwNP and ECRSwNP, which may be related to the different infiltration of indicated cytokines and different regulatory pathways of miRNA.

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.

Theta Oscillations at Subthalamic Region Predicts Hypomania State After Deep Brain Stimulation in Parkinson's Disease

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective treatment for the motor impairments of patients with advanced Parkinson's disease. However, mood or behavioral changes, such as mania, hypomania, and impulsive disorders, can occur postoperatively. It has been suggested that these symptoms are associated with the stimulation of the limbic subregion of the STN. Electrophysiological studies demonstrate that the low-frequency activities in ventral STN are modulated during emotional processing. In this study, we report 22 patients with Parkinson's disease who underwent STN DBS for treatment of motor impairment and presented stimulation-induced mood elevation during initial postoperative programming. The contact at which a euphoric state was elicited by stimulation was termed as the hypomania-inducing contact (HIC) and was further correlated with intraoperative local field potential recorded during the descending of DBS electrodes. The power of four frequency bands, namely, θ (4–7 Hz), α (7–10 Hz), β (13–35 Hz), and γ (40–60 Hz), were determined by a non-linear variation of the spectrogram using the concentration of frequency of time (conceFT). The depth of maximum θ power is located approximately 2 mm below HIC on average and has significant correlation with the location of contacts (r = 0.676, p < 0.001), even after partializing the effect of α and β, respectively (r = 0.474, p = 0.022; r = 0.461, p = 0.027). The occurrence of HIC was not associated with patient-specific characteristics such as age, gender, disease duration, motor or non-motor symptoms before the operation, or improvement after stimulation. Taken together, these data suggest that the location of maximum θ power is associated with the stimulation-induced hypomania and the prediction of θ power is frequency specific. Our results provide further information to refine targeting intraoperatively and select stimulation contacts in programming.

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.

Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm

STUDY OBJECTIVES

Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and/or reach a consensus among multiple sleep centers is time-consuming, we developed an artificial intelligence system to efficiently evaluate the reliability and consistency of sleep scoring and hence the sleep center quality.

METHODS

An interpretable machine learning algorithm was used to evaluate the interrater reliability (IRR) of sleep stage annotation among sleep centers. The artificial intelligence system was trained to learn raters from 1 hospital and was applied to patients from the same or other hospitals. The results were compared with the experts' annotation to determine IRR. Intracenter and intercenter assessments were conducted on 679 patients without sleep apnea from 6 sleep centers in Taiwan. Centers with potential quality issues were identified by the estimated IRR.

RESULTS

In the intracenter assessment, the median accuracy ranged from 80.3%-83.3%, with the exception of 1 hospital, which had an accuracy of 72.3%. In the intercenter assessment, the median accuracy ranged from 75.7%-83.3% when the 1 hospital was excluded from testing and training. The performance of the proposed method was higher for the N2, awake, and REM sleep stages than for the N1 and N3 stages. The significant IRR discrepancy of the 1 hospital suggested a quality issue. This quality issue was confirmed by the physicians in charge of the 1 hospital.

CONCLUSIONS

The proposed artificial intelligence system proved effective in assessing IRR and hence the sleep center quality.

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.

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.

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.

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.

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.

Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?

Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. We apply a set of linear and nonlinear HRV measures-including phase rectified signal averaging-to segments of resting ECG data collected from school-age children with ASD, age-matched typically developing controls, and children with other psychiatric conditions characterized by altered HRV (conduct disorder, depression). We use machine learning to identify time, frequency, and geometric signal-analytical domains that are specific to ASD (receiver operating curve area = 0.89). This is the first study to differentiate children with ASD from other disorders characterized by altered HRV. Despite a small cohort and lack of external validation, results warrant larger prospective studies.

Pattern recognition algorithm to identify detrusor overactivity on urodynamics

AIMS

Detrusor overactivity (DO) of the bladder is a finding on urodynamic studies (UDS) that often correlates with lower urinary tract symptoms and drives management. However, UDS interpretation remains nonstandardized. We sought to develop a mathematical model to reliably identify DO in UDS.

METHODS

We utilized UDS archive files for studies performed at our institution between 2013 and 2019. Raw tracings of vesical pressure, abdominal pressure, detrusor pressure, infused volume, and all annotations during UDS were obtained. Patients less than 1 year old, studies with calibration issues, or those with significant artifacts were excluded. In the training set, five representative DO patterns were identified. Candidate Pdet signal segments were matched to representative DO patterns. Manifold learning and dynamic time warping algorithms were used. Five-fold cross validation (CV) was used to evaluate the performance.

RESULTS

A total of 799 UDS studies were included. The median age was 9 years (range, 1-33). There were 1,742 DO events that did not overlap with annotated artifacts (cough, cry, valsalva, movements). The AUC of the training sets from the five-fold CV was 0.84 ± 0.01. The five-fold CV leads to an overall accuracy 81.35%, and sensitivity and specificity of detecting DO events are 76.92% and 81.41%, respectively, in the testing set.

CONCLUSIONS

Our predictive model using machine learning algorithms provides promising performance to facilitate automated identification of DO in UDS. This would allow for standardization and potentially more reliable UDS interpretation. Signal processing and machine learning interpretation of the other components of UDS are forthcoming.

Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning

Eye movements are disrupted in many neurodegenerative diseases and are frequent and early features in conditions affecting the cerebellum. Characterizing eye movements is important for diagnosis and may be useful for tracking disease progression and response to therapies. Assessments are limited as they require an in-person evaluation by a neurology subspecialist or specialized and expensive equipment. We tested the hypothesis that important eye movement abnormalities in cerebellar disorders (i.e., ataxias) could be captured from iPhone video. Videos of the face were collected from individuals with ataxia (n = 102) and from a comparative population (Parkinson’s disease or healthy participants, n = 61). Computer vision algorithms were used to track the position of the eye which was transformed into high temporal resolution spectral features. Machine learning models trained on eye movement features were able to identify abnormalities in smooth pursuit (a key eye behavior) and accurately distinguish individuals with abnormal pursuit from controls (sensitivity = 0.84, specificity = 0.77). A novel machine learning approach generated severity estimates that correlated well with the clinician scores. We demonstrate the feasibility of capturing eye movement information using an inexpensive and widely accessible technology. This may be a useful approach for disease screening and for measuring severity in clinical trials.

Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning

BACKGROUND

Cardiovascular waveforms contain information for clinical diagnosis. By learning and organizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning helps delineate a high-dimensional structure and display it as a novel 3-dimensional (3D) image. We hypothesize that the shape of this structure conveys clinically relevant inner dynamics information.

METHODS

To validate this hypothesis, we investigate the electrocardiography (ECG) waveform for ischemic heart disease and arterial blood pressure (ABP) waveform in dynamic vasoactive episodes. We model each beat or pulse to be a point lying on a manifold-like a surface-and use the diffusion map (DMap) to establish the relationship among those pulses. The output of the DMap is converted to a 3D image for visualization. For ECG datasets, first we analyzed the non-ST-elevation ECG waveform distribution from unstable angina to healthy control in the 3D image, and we investigated intraoperative ST-elevation ECG waveforms to show the dynamic ECG waveform changes. For ABP datasets, we analyzed waveforms collected under endotracheal intubation and administration of vasodilator. To quantify the dynamic separation, we applied the support vector machine (SVM) analysis and reported the total accuracy and macro-F1 score. We further performed the trajectory analysis and derived the moving direction of successive beats (or pulses) as vectors in the high-dimensional space.

RESULTS

For the non-ST-elevation ECG, a hierarchical tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented in the 3D image (accuracy = 97.6%, macro-F1 = 96.1%). The DMap helps quantify and visualize the evolving direction of intraoperative ST-elevation myocardial episode in a 1-hour period (accuracy = 97.58%, macro-F1 = 96.06%). The ABP waveform analysis of Nicardipine administration shows interindividual difference (accuracy = 95.01%, macro-F1 = 96.9%) and their common directions from intraindividual moving trajectories. The dynamic change of the ABP waveform during endotracheal intubation shows a loop-like trajectory structure, which can be further divided using the manifold learning knowledge obtained from Nicardipine.

CONCLUSIONS

The DMap and the generated 3D image of ECG or ABP waveforms provides clinically relevant inner dynamics information. It provides clues of acute coronary syndrome diagnosis, shows clinical course in myocardial ischemic episode, and reveals underneath physiological mechanism under stress or vasodilators.

[Scenario-based study of medical resource requirement rapid assessment under the COVID-19 pandemic]

Objective: COVID-19 outbreak is still under global pandemic. China is facing the risks of importation and local rebound of COVID-19. Under the circumstances, preparations for medical resources are in urgently needed. Methods: Based on current understanding of the disease, we set up five scenarios and use the infectious disease transmission dynamic and pandemic theoretical static models to evaluate the demand for medical resources. Results: Different epidemic strength and strategies of disease control and prevention resulted in different levels of medical resource request, and active control strategy and effective measures could significantly decrease the requirement. During the epidemic rising phase, the cost of prevention and control measures and the requirement of professional response capacities would increase with potential high risk of medical resource demand sharply increasing. Conclusion: Regions with different economic level, population scale, and different prevention and control capabilities should all initiate scientific assessment of medical resource requirement under emergency response and prepare for possible future rebound and epidemic.

[Clinical factors of pathological complete response after neoadjuvant chemoradiotherapy in rectal cancer]

Objective: To explore the feasibility of clinical factors to predict the pathological complete response after neoadjuvant chemoradiotherapy in rectal cancer. Methods: A retrospective analysis was performed on clinical factors of 162 patients with rectal cancer, who underwent neoadjuvant chemoradiotherapy in the General Hospital of People's Liberation Army from January 2011 to December 2018.According to the postoperative pathological results, the patients were divided into pathological complete response (pCR) group and non-pathological complete response group (non-pCR group) to check the predictive clinical factors for pCR. Results: Twenty-eight cases achieved pCR after neoadjuvant chemoradiation (17.3%, 28/162). Univariate analysis showed that patients with higher differentiation (P=0.024), tumor occupation of the bowel lumen≤1/2 (P=0.006), earlier clinical T stage (P=0.013), earlier clinical N stage (P=0.009), the time interval between neoadjuvant chemoradiotherapy and surgery>49 days (P=0.006), and maximum tumor diameter≤5 cm (P=0.019) were more likely to obtain pCR, and the differences werestatistically significant. Multivariate analysis showed that tumor occupation of the bowel lumen≤1/2 (P=0.01), maximum tumor diameter≤5 cm (P=0.035), and the interval>49 days (P=0.009) were independent factors in predicting pCR after neoadjuvant therapy. Conclusion: Tumor occupation of the bowel lumen, maximum tumor diameter, and the time interval between neoadjuvant chemoradiotherapy and surgery can predict the pCR in rectal cancer.

Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages

Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.

[Changing trend in prognosis of primary hypopharyngeal carcinoma and reason analysis: date comparison of 2003-2007 and 2010-2014 in a single centre]

Objective: To explore changing trend in prognosis of primary hypopharyngeal carcinoma and to analyze the reasons at the Eye, Ear, Nose and Throat Hospital of Fudan University. Methods: We retrospectively analyzed the clinical data of 461 patients with primary hypopharyngeal carcinoma treated at the Eye, Ear, Nose and Throat Hospital of Fudan University from 2003 to 2007 (Group 1) and 2010 to 2014 (Group 2) according to the inclusion criteria. 142 from Group 1, including 133 males and 9 females, rangedfrom 38 to 82 years old and 319 from Group 2, including 313 males and 6 females, ranged from 39 to 81 years old, were included in this work. The laryngeal function preservation rate, survival outcome, application and effect of pre-and post-operative adjuvant therapy were compared. SPSS 24.0 was used for statistical analyses. Results: There were 62 patients with early disease (T1-2N0) including 18 in Group 1 and 44 in Group 2, in whom 3 (16.7%) underwent surgical procedures with laryngeal function preservation in Group 1, while, 30 (68.2%) underwent laryngeal function preservation surgery in Group 2. The laryngeal function preservation rate showed an obviously upward trend in recent years (χ(2)=13.617, P<0.001), whereas, the recurrence-free survival rate (RFS) and overall survival rate (OS) showed no significant differences between two groups (P=0.469 and 0.808, respectively). Among the 399 patients with advanced disease, 124 were in Group 1 and 275 in Group 2. After propensity score matching (PSM) was used, the OS rate was significantly higher for Group 2 than Group 1 (P=0.017), while the application of laryngeal function preservation surgery was significantly higher in Group 2 (χ(2)=4.686, P=0.030). The application rates of preoperative adjuvant therapy and postoperative adjuvant chemotherapy were significantly higher in group 2 than in group 1 (χ(2)=5.687, P=0.017; χ(2)=19.407, P<0.001). Conclusion: The application of laryngeal function preserving surgery significantly increases the retention rate of laryngeal functions in patients with early-stage hypopharyngeal carcinoma, with similar long-term survival outcomes. The application of comprehensive treatment including preoperative adjuvant therapy and postoperative adjuvant treatments, especially postoperative chemoradiotherapy, shows an obvious increase in the function preservation rate and long-term survival in patients with advanced hypopharyngeal carcinoma.

Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière's disease

Background: Fluctuating hearing loss is characteristic of Ménière's disease (MD) during acute episodes. However, no reliable audiometric hallmarks are available for counselling the hearing recovery possibility.Aims/objectives: To find parameters for predicting MD hearing outcomes.Material and methods: We applied machine learning techniques to analyse transient-evoked otoacoustic emission (TEOAE) signals recorded from patients with MD. Thirty unilateral MD patients were recruited prospectively after onset of acute cochleo-vestibular symptoms. Serial TEOAE and pure-tone audiogram (PTA) data were recorded longitudinally. Denoised TEOAE signals were projected onto the three most prominent principal directions through a linear transformation. Binary classification was performed using a support vector machine (SVM). TEOAE signal parameters, including signal energy and group delay, were compared between improved (PTA improvement: ≥15 dB) and nonimproved groups using Welch's t-test.Results: Signal energy did not differ (p = .64) but a significant difference in 1-kHz (p = .045) group delay was recorded between improved and nonimproved groups. The SVM achieved a cross-validated accuracy of >80% in predicting hearing outcomes.Conclusions and significance: This study revealed that baseline TEOAE parameters obtained during acute MD episodes, when processed through machine learning technology, may provide information on outer hair cell function to predict hearing recovery.

Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage

OBJECTIVE

We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels.

APPROACH

We design an algorithm based on the optimal-shrinkage under the wave-shape manifold model. For the fetal heart rate analysis, the algorithm is evaluated on publicly available database, 2013 PhyioNet/Computing in Cardiology Challenge, set A (CinC2013). For the morphological analysis, we analyze CinC2013 and another publicly available database, non-invasive fetal ECG arrhythmia database (nifeadb), and propose to simulate semi-real databases by mixing the MIT-BIH normal sinus rhythm database and MITDB arrhythmia database.

MAIN RESULTS

For the fetal R peak detection, the proposed algorithm outperforms all algorithms under comparison. For the morphological analysis, the algorithm provides an encouraging result in recovery of the fetal ECG waveform, including PR, QT and ST intervals, even when the fetus has arrhythmia, both in real and simulated databases.

SIGNIFICANCE

To the best of our knowledge, this is the first work focusing on recovering the fetal ECG for morphological analysis from two or three channels with an algorithm potentially applicable for continuous fetal electrocardiographic monitoring, which creates the potential for long term monitoring purpose.

Fetal heart rate variability responsiveness to maternal stress, non-invasively detected from maternal transabdominal ECG

PURPOSE

Prenatal stress (PS) during pregnancy affects in utero- and postnatal child brain-development. Key systems affected are the hypothalamic-pituitary-adrenal axis and the autonomic nervous system (ANS). Maternal- and fetal ANS activity can be gauged non-invasively from transabdominal electrocardiogram (taECG). We propose a novel approach to assess couplings between maternal (mHR) and fetal heart rate (fHR) as a new biomarker for PS based on bivariate phase-rectified signal averaging (BPRSA). We hypothesized that PS exerts lasting impact on fHR.

METHODS

Prospective case-control study matched for maternal age, parity, and gestational age during the third trimester using the Cohen Perceived Stress Scale (PSS-10) questionnaire with PSS-10 over or equal 19 classified as stress group (SG). Women with PSS-10 < 19 served as control group (CG). Fetal electrocardiograms were recorded by a taECG. Coupling between mHR and fHR was analyzed by BPRSA resulting in fetal stress index (FSI). Maternal hair cortisol, a memory of chronic stress exposure for 2-3 months, was measured at birth.

RESULTS

538/1500 pregnant women returned the questionnaire, 55/538 (10.2%) mother-child pairs formed SG and were matched with 55/449 (12.2%) consecutive patients as CG. Maternal hair cortisol was 86.6 (48.0-169.2) versus 53.0 (34.4-105.9) pg/mg (p = 0.029). At 36 + 5 weeks, FSI was significantly higher in fetuses of stressed mothers when compared to controls [0.43 (0.18-0.85) versus 0.00 (- 0.49-0.18), p < 0.001].

CONCLUSION

Prenatal maternal stress affects the coupling between maternal and fetal heart rate detectable non-invasively a month prior to birth. Lasting effects on neurodevelopment of affected offspring should be studied.

TRIAL REGISTRATION

Clinical trial registration: NCT03389178.

A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems

Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.

Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data

Object

It is increasingly popular to collect as much data as possible in the hospital setting from clinical monitors for research purposes. However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose. We hypothesize that this calibration issue for a secondary analysis may become an important source of artifacts in patient monitor data. We test an off-the-shelf integrated photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a reliable pulse transit time (PTT) signal.

Approach

This is a retrospective clinical study using two databases: one containing 35 subjects who underwent laparoscopic cholecystectomy, another containing 22 subjects who underwent spontaneous breathing test in the intensive care unit. All data sets include recordings of PPG and ECG using a commonly deployed patient monitor. We calculated the PTT signal offline.

Main results

We report a novel constant oscillatory pattern in the PTT signal and identify this pattern as a sawtooth artifact. We apply an approach based on the de-shape method to visualize, quantify and validate this sawtooth artifact.

Significance

The PPG and ECG signals not designed for the PTT evaluation may contain unwanted artifacts. The PTT signal should be calibrated before analysis to avoid erroneous interpretation of its physiological meaning.

[Outcome of neck dissection for 154 cN0/cN1 patients with squamous cell carcinoma of hypopharynx and prognostic analysis]

Objective: To determine the relationship between nodal disease distribution patterns and oncological outcome and optimum management of cN0/cN1 patients with hypopharyngeal squamous cell carcinoma (HPSCC). Methods: The collected clinical data and the follow-up disease-free survival (DFS) information from 154 patients with HPSCC treated between 2001 and 2014 in Eye Ear Nose and Throat Hospital of Fudan University were retrospectively analyzed. Kaplan-Meier method was used to calculate DFS rate. Log-rank test and Cox regression model were performed to evaluate the survival prognostic factors for univariate and multivariate analyses. Results: Among 154 patients (63 cN0 patients, 91 cN1 patients) with HPSCC,in whom 148 were male, 6 were female, with an arerage age of (58.6±9.9), 63 cN0 patients received lateral neck dissection, of whom 9 were histologically positive nodes. Particularly, occult metastasis rate was 15.6%(7/45) for HPSCC in pyriform sinus and 18.2%(2/11) for HPSCC in posterior pharyngeal wall. Among 91 cN1 HPSCC, 34 underwent radical neck dissection and 54 underwent selective neck dissection. DFS for patients(T3-4N1) with radical neck dissection was lower than that for patient with selective neck dissection(15.2% vs.39.5%, χ(2)=5.153, P<0.05). The DFS of cN0 HPSCC was overestimated by the false-negative patients (cN0→pN+) for cN0 HPSCC and the DFS of cN1 HPSCC was underestimated by the false-positive(cN1→pN0) for cN1 HPSCC. Conclusions: For cN1 HPSCC, compared with radical neck dissection, selective neck dissection may be considered as a more adequate method for removal of metastatic lymph nodes. Postoperative combined therapy and follow-up strategy should be established according to pathological N staging.

Traditional Chinese medicine use is associated with lower end-stage renal disease and mortality rates among patients with diabetic nephropathy: a population-based cohort study

Background

Diabetic nephropathy (DN) is a common complication of diabetes mellitus (DM) that imposes an enormous burden on the healthcare system. Although some studies show that traditional Chinese medicine (TCM) treatments confer a protective effect on DN, the long-term impact remains unclear. This study aims to examine end-stage renal disease (ESRD) and mortality rates among TCM users with DN.

Methods

A total of 125,490 patients with incident DN patients from 2004 to 2006 were identified from the National Health Insurance Research Database in Taiwan and followed until 2012. The landmark method was applied to avoid immortal time bias, and propensity score matching was used to select 1:1 baseline characteristics-matched cohort. The Kaplan–Meier method and competing-risk analysis were used to assess mortality and ESRD rates separately.

Results

Among all eligible subjects, about 60% of patients were classified as TCM users (65,812 TCM users and 41,482 nonusers). After 1:1 matching, the outcomes of 68,882 patients were analyzed. For the ESRD rate, the 8-year cumulative incidence was 14.5% for TCM users [95% confidence interval (CI): 13.9–15.0] and 16.6% for nonusers (95% CI: 16.0–17.2). For the mortality rate, the 8-year cumulative incidence was 33.8% for TCM users (95% CI: 33.1–34.6) and 49.2% for nonusers (95% CI: 48.5–49.9). After adjusting for confounding covariates, the cause-specific hazard ratio of ESRD was 0.81 (95% CI: 0.78–0.84), and the hazard ratio of mortality for TCM users was 0.48 (95% CI: 0.47–0.50). The cumulative incidence of mortality increased rapidly among TCM users with ESRD (56.8, 95% CI: 54.6–59.1) when compared with TCM users without ESRD (30.1, 95% CI: 29.4–30.9). In addition, TCM users who used TCM longer or initiated TCM treatments after being diagnosed with DN were associated with a lower risk of mortality. These results were consistent across sensitivity tests with different definitions of TCM users and inverse probability weighting of subjects.

Conclusions

The lower ESRD and mortality rates among patients with incident DN correlates with the use of TCM treatments. Further studies about specific TCM modalities or medications for DN are still needed.

Electronic supplementary material

The online version of this article (10.1186/s12906-019-2491-y) contains supplementary material, which is available to authorized users.

[Prospective cohort study on the risks of pre-pregnancy overweight, excessive gestational weight gain on macrosomia]

Objective: To investigate the risks of pre-pregnancy overweight, excessive gestational weight gain on macrosomia. Methods: We conducted one hospital-based cohort study, focusing on pregnant women from January 2015. All pregnant women attending to this hospital for maternal check-ups, were included in our cohort and followed to the time of delivery. Data related to general demographic characteristics, pregnancy and health status of those pregnant women, was collected and maternal pre-pregnant BMI and maternal weight gain were calculated. Logistic regression was used to explore the risk difference of pre-pregnancy BMI, excessive gestational weight gain on macrosomia. Results: The overall incidence of macrosomia in our cohort appeared as 6.6% (149/2 243). After adjusting the confounding factors including age and histories on pregnancy, pre-pregnancy overweight/obesity was associated with higher risks of macrosomia (OR=3.12, 95%CI: 1.35-7.22, P=0.008; OR=2.99, 95%CI: 1.17-7.63, P=0.022) when comparing to those with normal pre-pregnancy weight. Cesarean delivery and sex of the offspring were associated with higher risk of macrosomia, while excessive gestational weight gain showed no significant difference (OR=1.41, 95%CI: 0.96-2.09, P=0.084). Our data showed that Macrosomia was statistically associated with gestational weight gain (P=0.002). After controlling parameters as age, history of pregnancy and related complications of the pregnant women, results from the logistic regression showed that women with gestational inadequate weight gain having reduced risks to deliver macrosomia, when compared to those pregnant women with adequate weight gain (OR=0.52, 95%CI: 0.30-0.90, P=0.019). Conclusion: Pre-pregnancy overweight and obesity were on higher risks to macrosomia.

Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography

OBJECTIVE

Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data.

APPROACH

Our algorithm, shape adaptive nonlocal artifact removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity.

MAIN RESULTS

SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain.

SIGNIFICANCE

This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.