Daytime napping is a common behavior especially in elderlies. There are conflict results regarding long-term health consequences and daytime napping. No prior studies have investigated how objective daytime napping changes longitudinally with aging, and how this change interacts with the progression of Alzheimer's.
We have studied this using a large cohort of elderlies with annual clinical assessment and annual assessment of actigraphy. Participants have been followed for 6 years on average, and up to 14 years. Results were presented in oral at the virtual SLEEP2020 meeting...
This is the first study in people living with HIV infection in the UK Biobank cohort. I presented our recent data that showed an exacerbated effect of HIV infection on the association between daytime sleep behavior and cognitive performance.
I have the honor to present oral in this year's SLEEP meeting again! The topic is about my most recent research interest -- daytime napping. Many results have been touched in the online AAIC oral presentation this year. In this SLEEP presentation, I've added some most recent updates on the...
BackgroundSpontaneous heart rate fluctuations contain rich information related to health and illness in terms of physiological complexity, an accepted indicator of plasticity and adaptability. However, it is challenging to make inferences on complexity from shorter, more practical epochs of data. Distribution entropy (DistEn) is a recently introduced complexity measure that is designed specifically for shorter duration heartbeat recordings. We hypothesized that reduced DistEn predicted increased mortality in a large population cohort.Method and ResultsThe prognostic value of DistEn was examined in 7631 middle‐older–aged UK Biobank participants who had 2‐minute resting ECGs conducted (mean age, 59.5 years; 60.4% women). During a median follow‐up period of 7.8 years, 451 (5.9%) participants died. In Cox proportional hazards models with adjustment for demographics, lifestyle factors, physical activity, cardiovascular risks, and comorbidities, for each 1‐SD decrease in DistEn, the risk increased by 36%, 56%, and 73% for all‐cause, cardiovascular, and respiratory disease–related mortality, respectively. These effect sizes were equivalent to the risk of death from being \textgreater5 years older, having been a former smoker, or having diabetes mellitus. Lower DistEn was most predictive of death in those \textless55 years with a prior myocardial infarction, representing an additional 56% risk for mortality compared with older participants without prior myocardial infarction. These observations remained after controlling for traditional mortality predictors, resting heart rate, and heart rate variability.ConclusionsResting heartbeat complexity from short, resting ECGs was independently associated with mortality in middle‐ to older‐aged adults. These risks appear most pronounced in middle‐aged participants with prior MI, and may uniquely contribute to mortality risk screening.
The Sasso Fratino Integral Natural Reserve (Central Italy), a rare example of climax Mediterranean forest, provides an extraordinary opportunity to create an important soundscape reference of old-growth forest. In this study, we describe the soundscape of three localities (Lama, Sasso 950, Sasso 1400) representative of a gradient of variety and complexity of habitats, recorded during the period 10 May to 9 June 2017. Our results reveal temporal partitioning into acoustically homogeneous periods across 24 h suggesting that soniferous species (mainly birds) adopt ecological routines in which their acoustic activity is organized according to specific transient physiological needs. We processed multi-temporal aggregates of 1, 5, 10, and 15 s recordings and calculated the Acoustic Signature (AS) with four new indices: Ecoacoustic Events (EE), Acoustic Signature Dissimilarity (ASD), and their fractal dimensions (DEE and DASD), derived from the Acoustic Complexity Index (ACI). The use of the EE and ASD greatly improved the AS interpretation, adding further details such as the emergence of a clear sequence of patterns consistent with the daily evolution of the overall soundscape. DEE and DASD confirm the patterns observed using the AS, but provide more clarity and detail about the great acoustic complexity that exists across temporal scales in this old-growth forest. The temporal turnover of different acoustic communities occurs as a result of a gradual shift of different homogenous acoustic properties. We conclude that soniferous species use distinct, species-specific temporal resolutions according to their physiological and ecological needs and that the fractal approach used here provides a novel tool to overcome the difficulties associated with describing multi-temporal acoustic patterns.
Objective: Coronary artery disease (CAD) is a common fatal disease. At present, an accurate method to screen CAD is urgently needed. This study aims to provide optimal detection models for suspected CAD detection according to the differences in medical conditions, so as to assist physicians to make accurate judgments on suspected CAD patients. Approach: Electrocardiogram (ECG) and phonocardiogram (PCG) signals of 32 CAD patients and 30 patients with chest pain and normal coronary angiograms (CPNCA) were simultaneously collected for this paper. For each subject, the ECG and PCG multi-domain features were extracted, and the results of Holter monitoring, echocardiography (ECHO), and biomarker levels (BIO) were obtained to construct a multi-modal feature set. Then, a hybrid feature selection (HFS) method was developed using mutual information, recursive feature elimination, random forest, and weight of support vector machine to obtain the optimal feature subset. A support vector machine with nested cross-validation was used for classification. Main results: Results showed that the Holter model achieved the best performance as a single-modal feature model with an accuracy of 82.67%. In terms of multi-modal feature models, PCG-Holter, PCG-Holter-ECHO, PCG-Holter-ECHO-BIO, and ECG-PCG-Holter-ECHO-BIO were the optimal bimodal, three-modal, four-modal, and five-modal models, with accuracies of 90.38%, 91.92%, 95.25%, and 96.67%, respectively. Among them, the ECG-PCG-Holter-ECHO-BIO model, which was constructed by combining ECG and PCG signals features with Holter, ECHO, and BIO examination results, achieved the best classification results with an average accuracy, sensitivity, specificity, and F1-measure of 96.67%, 96.67%, 96.67%, and 96.64%, respectively. Significance: The study indicated that multi-modal feature fusion and HFS can obtain more effective information for CAD detection and provide a reference for physicians to diagnose CAD patients.
The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (DistEn), in the context of HRV signal analysis. We thereby propose modified distribution entropy (mDistEn) to remove the physiological discrepancy involved in the computation of DistEn. The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes “mDistEn” a computationally inexpensive and physiologically more relevant complexity measure in comparison to DistEn.
QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.
There is a growing interest in automated diagnosis of coronary artery disease (CAD) with the application of machine learning (ML) methods to the body surface electrocardiograph (ECG). Although prior studies have documented associations of CAD with increased QT variability and ST-T segment abnormalities such as T-wave inversion and ST-segment elevation or depression, their efficacy in automated CAD detection has not been fully investigated. To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed. Based on this dataset, simultaneous analyses were then conducted in five scenarios, in which different ML algorithms were applied to classify the two groups with various features derived from the RR and QT interval time-series and ST-T segment waveforms. Compared with utilizing features obtained from the RR interval time-series, better classification results were achieved utilizing that obtained from the QT interval time-series. The classification results were elevated with combining utilization of features derived from both the RR and QT interval time-series. By further fusing features extracted from ST-T segment waveforms, the best performance was achieved with 96.16% accuracy, 95.75% sensitivity, and 96.40% specificity. Based the best performance, an automated CAD detection system was developed with extreme gradient boosting, an ensemble ML algorithm, and the residual neural network, namely, a deep learning method. The results of this study support the potential of information derived from the QT interval time-series and ST-T segment waveforms in ECG-based automated CAD detection.
Nonlinear physiological signal features that reveal information content and causal flow have recently been shown to be predictors of mortality after severe traumatic brain injury (TBI). The extent to which these features interact together, and with traditional measures to describe patients in a clinically meaningful way remains unclear. In this study, we incorporated basic demographics (age and initial Glasgow Coma Scale [GCS]) with linear and non-linear signal information based features (approximate entropy [ApEn], and multivariate conditional Granger causality [GC]) to evaluate their relative contributions to mortality using cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10 year period. Beyond linear modelling, we employed a decision tree analysis approach to define a predictive hierarchy of features. We found ApEn (p = 0.009) and GC (p = 0.004) based features to be independent predictors of mortality at a time when mean intracranial pressure (ICP) was not. Our combined model with both signal information-based features performed the strongest (area under curve = 0.86 vs. 0.77 for linear features only). Although low "intracranial" complexity (ApEn-ICP) outranked both age and GCS as crucial drivers of mortality (fivefold increase in mortality where ApEn-ICP \textless1.56, 36.2% vs. 7.8%), decision tree analysis revealed clear subsets of patient populations using all three predictors. Patients with lower ApEn-ICP who were \textgreater60 years of age died, whereas those with higher ApEn-ICP and GCS ≥5 all survived. Yet, even with low initial intracranial complexity, as long as patients maintained robust GC and "extracranial" complexity (ApEn of mean arterial pressure), they all survived. Incorporating traditional linear and novel, non-linear signal information features, particularly in a framework such as decision trees, may provide better insight into "health" status. However, caution is required when interpreting these results in a clinical setting prior to external validation.
Inflammation, nutrition, and coagulation play significant roles in cancer prognosis. Autonomic function is also actively involved in tumorigenesis. Previous studies have shown that an elevated C-reactive protein (CRP) level, a serum marker for inflammation, is associated with low heart rate variability (HRV), a common clinical tool for the assessment of autonomic function. It is yet to be investigated whether HRV links to these prognostic factors in cancer patients. Sixty-one patients who were first diagnosed with gastric cancer (GC) were enrolled in this study. Fasting blood samples were collected in the morning seven days before surgery. Blood CRP, prealbumin (PA), and fibrinogen (FIB) were used to assess the inflammation level, nutritional status, and coagulation function respectively. Five-minute resting electrocardiogram (ECG) signals were collected one day before surgical treatment. Short-term HRV time-series were extracted from ECG recordings and were analyzed using commonly-used time- and frequency-domain parameters including standard deviation of normal-to-normal intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), very-low-frequency power (VLF), low-frequency power (LF), high-frequency power (HF), total power (TP), LF power in normalized units (LF n.u.), HF power in normalized units (HF n.u.), and ratio of LF to HF (LF/HF). After adjusted for sex, age, body mass index, alcohol consumption, history of diabetes, left ventricular ejection fraction, and hemoglobin levels, our results demonstrated negative associations of HRV with levels of CRP and FIB, while positive associations between HRV and PA level, with effect sizes of as high as 35%–52% standard deviations (SD) changes in CRP, FIB, or PA per 1-SD change in HRV parameters. Therefore, decreased HRV in patients with GC predicts increased burdens of inflammation and coagulation and perturbed nutrition, suggesting that short-term HRV measurement can potentially be a noninvasive biomarker for GC prognosis.
My research interests span multidisciplinary fields from physiological measurement, signal processing, data mining, to human aging and aging-related degenerations. The goal of my work is to promote health and wellbeing by developing new tools for understanding and predicting disease progression, monitoring health status, and guiding self-interventions to actively manage individual’s health.
I am actively working on the following topics:
(1) Harnessing the power of ambulatory data
Digital technologies are empowering us to better track and monitor our own health. What is challenging is how we are going to take advantage of the wealth of data produced every single second. We are faced with a BIG challenge in dealing with the BIG data using traditional approaches. In recent years, I have been focusing on understanding the fluctuations in physiological recordings that show promise to reflect the intrinsic functioning status of the underlying regulation systems. The idea is that the spontaneous fluctuations in physiological outputs are inherently complex while functional degeneration is accompanied by a loss of such complexity.
(2) Sleep, life style, autonomic function, and diseases
Sleep and life style are increasingly recognized as modifiable risk factors of being diseased and disease progression. The autonomic function is weighed considerably as one of the potential pathways linking individual habits and pathogenesis. Benefits of interventions targeting these factors are yet to be determined.
(3) Proactive healthcare technology
To actively manage the health status now, today, or to wait and seek for treatments until diseases come, it is time to make a decision. It is the perspective of the ‘proactive healthcare’ that to invest more time and resources up front to prevent illness and to manage chronic diseases before they progress to cause complications. It is the future of healthcare, but the future is now here already. What needs to be worked out are the how and what questions. I am trying to bridge the gaps between basic or applied research and healthcare practices, and eventually to grow them to be critical components of the new healthcare schemes. The ultimate goal is to help individuals better understand and, further, proactively manage their health status and prevent from being diseased.