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...
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.
Predictive patterns Fractals are self-similar patterns that exist across different conditions; in medicine, changes in fractal fluctuations can indicate disease, such as degraded fractal movement fluctuations seen with dementia. Using wrist-worn activity monitors, Li et al. analyzed daily motor activity of a large cohort of elderly subjects. They found that more random fluctuations over two time scales (1 to 90 min and greater than 2 hours) predicted increased risk of frailty, disability, and death years later—independent of age, sex, chronic health conditions, and total motor activity. Results suggest that fractal analyses can help predict health outcomes in the absence of overt symptoms and support the utility of passive monitoring. Mobile healthcare increasingly relies on analytical tools that can extract meaningful information from ambulatory physiological recordings. We tested whether a nonlinear tool of fractal physiology could predict long-term health consequences in a large, elderly cohort. Fractal physiology is an emerging field that aims to study how fractal temporal structures in physiological fluctuations generated by complex physiological networks can provide important information about system adaptability. We assessed fractal temporal correlations in the spontaneous fluctuations of ambulatory motor activity of 1275 older participants at baseline, with a follow-up period of up to 13 years. We found that people with reduced temporal correlations (more random activity fluctuations) at baseline had increased risk of frailty, disability, and all-cause death during follow-up. Specifically, for 1-SD decrease in the temporal activity correlations of this studied cohort, the risk of frailty increased by 31%, the risk of disability increased by 15 to 25%, and the risk of death increased by 26%. These incidences occurred on average 4.7 years (frailty), 3 to 4.2 years (disability), and 5.8 years (death) after baseline. These observations were independent of age, sex, education, chronic health conditions, depressive symptoms, cognition, motor function, and total daily activity. The temporal structures in daily motor activity fluctuations may contain unique prognostic information regarding wellness and health in the elderly population. More random fluctuations in daily motor activity predict deteriorated quality of life and high death rate in elderly subjects. More random fluctuations in daily motor activity predict deteriorated quality of life and high death rate in elderly subjects.
Many outputs from healthy neurophysiological systems including motor activity display nonrandom fluctuations with fractal scaling behavior as characterized by similar temporal fluctuation patterns across a range of time scales. Degraded fractal regulation predicts adverse consequences including Alzheimer's dementia. We examined longitudinal changes in the scaling behavior of motor activity fluctuations during the progression of Alzheimer's disease (AD) in 1068 participants in the Rush Memory and Aging Project. Motor activity of up to 10 days was recorded annually for up to 13 years. Cognitive assessments and clinical diagnoses were administered annually in the same participants. We found that fractal regulation gradually degraded over time (p \textless 0.0001) even during the stage with no cognitive impairment. The degradation rate was more than doubled after the diagnosis of mild cognitive impairment and more than doubled further after the diagnosis of Alzheimer's dementia (p's ≤ 0.0005). Besides, the longitudinal degradation of fractal regulation significantly correlated with the decline in cognitive performance throughout the progression from no cognitive impairment to mild cognitive impairment, and to AD (p \textless 0.001). All effects remained the same in subsequent sensitivity analyses that included only 255 decedents with autopsy-confirmed Alzheimer's pathology. These results indicate that the progression of AD accelerates fractal degradation and that fractal degradation may be an integral part of the process of AD.
Identifying prognostic factors by affordable tools is crucial for guiding gastric cancer (GC) treatments especially at earlier stages for timing interventions. The autonomic function that is clinically assessed by heart rate variability (HRV) is involved in tumorigenesis. This pilot study was aimed to examine whether nonlinear indices of HRV can be biomarkers of GC severity. Sixty-one newly-diagnosed GC patients were enrolled. Presurgical serum fibrinogen (FIB), carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA199) were examined. Resting electrocardiogram (ECG) of 5-min was collected prior to surgical treatments to enable the HRV analysis. Twelve nonlinear HRV indices covering the irregularity, complexity, asymmetry, and temporal correlation of heartbeat fluctuations were obtained. Increased short-range temporal correlations, decreased asymmetry, and increased irregularity of heartbeat fluctuations were associated with higher FIB level. Increased irregularity and decreased complexity were also associated with higher CEA level. These associations were independent of age, sex, BMI, alcohol consumption, history of diabetes, left ventricular ejection fraction, and anemia. The results support the hypothesis that perturbations in nonlinear dynamical patterns of HRV predict increased GC severity. Replication in larger samples as well as the examination of longitudinal associations of HRV nonlinear features with cancer prognosis/survival are warranted.
Electrocardiogram (ECG) and phonocardiogram (PCG) signals reflect the electrical and mechanical activities of the heart, respectively. Although studies have documented that some abnormalities in ECG and PCG signals are associated with coronary artery disease (CAD), only few researches have combined the two signals for automatic CAD detection. This paper aims to differentiate between CAD and non-CAD groups using simultaneously collected ECG and PCG signals. To entirely exploit the underlying information in these signals, a novel dual-input neural network that integrates the feature extraction and deep learning methods is developed. First, the ECG and PCG features are extracted from multiple domains, and the information gain ratio is used to select important features. On the other hand, the ECG signal and the decomposed PCG signal (at four scales) are concatenated as a five-channel signal. Then, the selected features and the five-channel signal are fed into the proposed network composed of a fully connected model and a deep learning model. The results show that the classification performance of either feature extraction or deep learning is insufficient when using only ECG or PCG signal, and combining the two signals improves the performance. Further, when using the proposed network, the best result is obtained with accuracy, sensitivity, specificity, and G-mean of 95.62%, 98.48%, 89.17%, and 93.69%, respectively. Comparisons with existing studies demonstrate that the proposed network can effectively capture the combined information of ECG and PCG signals for the recognition of CAD.
Background The significant association of myocardial ischemia with elevated QT interval variability (QTV) has been reported in myocardial infarction (MI) patients. However, the influence of the time course of MI on QTV has not been investigated systematically. Method Short-term QT and RR interval time series were constructed from the 5 min electrocardiograms of 49 coronary patients without MI and 26 patients with old MI (OMI). The QTV, heart rate variability (HRV), and QT–RR coupling of the two groups were analyzed using various time series analysis tools in the time- and frequency-domains, as well as nonlinear dynamics. Results Nearly all of the tested QTV indices for coronary patients with OMI were higher than those for patients without MI. However, no significant differences were found between the two groups in any of the variables employed to assess the HRV and QT–RR coupling. All of the markers that showed statistical significances in univariate analyses still possessed the capabilities of distinguishing between the two groups even after adjusting for studied baseline characteristics, including the coronary atherosclerotic burden. Conclusions The results suggested that the QTV increased in coronary patients with OMI compared to those without MI, which might reflect the influence of post-MI remodeling on the beat-to-beat temporal variability of ventricular repolarization. The non-significant differences in the HRV and QT–RR couplings could indicate that there were no differences in the modulation of the autonomic nervous system and interaction of QT with the RR intervals between the two groups.
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.