Ping Huang Tsai
Bio: Ping Huang Tsai is an academic researcher from National Taiwan University. The author has contributed to research in topics: Medicine & Plasmon. The author has an hindex of 3, co-authored 3 publications receiving 88 citations. Previous affiliations of Ping Huang Tsai include National Chiao Tung University & National Yang-Ming University.
TL;DR: HHT is introduced to analyze the alpha waves of human's electroencephalography (EEG), which seemly oscillate regularly between 8 and 12 Hz in healthy subject but getting irregular or disappeared in different demented status, and the potential usages are demonstrated in characterizing the biological signals qualitatively and quantitatively.
Abstract: The analysis of biological fluctuations provides an excellent route to probe the underlying mechanisms in maintaining internal homeostasis of the body, especially under the challenges of the ever-changing environment or disease processes. However, the features of nonlinearity and nonstationarity in physiological time series limit the reliability of the conventional analysis. Hilbert–Huang transform (HHT), based on nonlinear theory, is an innovative approach to extract the dynamic information at different time scales, in particular, from nonstationary signals. In this paper, HHT is introduced to analyze the alpha waves of human's electroencephalography (EEG), which seemly oscillate regularly between 8 and 12 Hz in healthy subject but getting irregular or disappeared in different demented status. Furthermore, conventional time–frequency analyses are adopted to collate the results from those methods and HHT. Finally, the potential usages of HHT are demonstrated in characterizing the biological signals qualitatively and quantitatively, including stationarity analysis, instantaneous frequency and amplitude modulation or correlation analysis. Such applications on EEG have successively disclosed the differences of alpha rhythms between normal and demented brains and the nonlinear characteristics of the underlying mechanisms. Hopefully, in addition to empower the studies of EEG varied in diseased, aging, and physiological processes, these methods might find other applications in EEG analysis.
TL;DR: The dynamic complexity of EEG fluctuations is degraded by pathological degeneration, and EMD-based detrended SaEn provides an objective, non-invasive and non-expensive tool for evaluating and following AD patients.
TL;DR: In this paper, the authors used multiscale entropy (MSE) analysis, which can disclose the embedded information in different time scales, in electroencephalography (EEG), in an attempt to predict the efficacy of AChE inhibitors.
Abstract: Alzheimer's disease (AD) is the most common form of dementia. According to one hypothesis, AD is caused by the reduced synthesis of the neurotransmitter acetylcholine. Therefore, acetylcholinesterase (AChE) inhibitors are considered to be an effective therapy. For clinicians, however, AChE inhibitors are not a predictable treatment for individual patients. We aimed to disclose the difference by biosignal processing. In this study, we used multiscale entropy (MSE) analysis, which can disclose the embedded information in different time scales, in electroencephalography (EEG), in an attempt to predict the efficacy of AChE inhibitors. Seventeen newly diagnosed AD patients were enrolled, with an initial minimental state examination (MMSE) score of 18.8 ± 4.5. After 12 months of AChE inhibitor therapy, 7 patients were responsive and 10 patients were nonresponsive. The major difference between these two groups is Slope 2 (MSE6 to 20). The area below the receiver operating characteristic (ROC) curve of Slope 2 is 0.871 (95% CI = 0.69–1). The sensitivity is 85.7% and the specificity is 60%, whereas the cut-off value of Slope 2 is −0.024. Therefore, MSE analysis of EEG signals, especially Slope 2, provides a potential tool for predicting the efficacy of AChE inhibitors prior to therapy.
TL;DR: In this article , a proof-of-concept experiment based on biofunctionalized magnetoplasmonic nanoparticles (MPNs) and magneto-optical Faraday effect for in vitro Alzheimer's disease (AD) assay was conducted.
TL;DR: The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first renal flare in each cluster as discussed by the authors .
Abstract: Identifying candidates responsive to treatment is important in lupus nephritis (LN) at the renal flare (RF) because an effective treatment can lower the risk of progression to end-stage kidney disease. However, machine learning (ML)-based models that address this issue are lacking.Transcriptomic profiles based on DNA microarray data were extracted from the GSE32591 and GSE112943 datasets. Comprehensive bioinformatics analyses were performed to identify disease-defining genes (DDGs). Peripheral blood samples (GSE81622, GSE99967, and GSE72326) were used to evaluate the effect of DDGs. Single-sample gene set enrichment analysis (ssGSEA) scores of the DDGs were calculated and correlated with specific immunology genes listed in the nCounter panel. GSE60681 and GSE69438 were used to examine the ability of the DDGs to discriminate LN from other renal diseases. K-means clustering was used to obtain the separate gene sets. The clustering results were extended to data derived using the nCounter technique. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first RF in each cluster. LASSO models with tenfold validation were built in GSE200306 and assessed by receiver operating characteristic (ROC) analysis with area under curve (AUC). The models were validated by using an independent dataset (GSE113342).Forty-five hub genes specific to LN were identified. Eight optimal disease-defining clusters (DDCs) were identified in this study. Th1 and Th2 cell differentiation pathway was significantly enriched in DDC-6. LCK in DDC-6, whose expression positively correlated with various subsets of T cell infiltrations, was found to be differentially expressed between responders and non-responders and was ranked high in regulatory network analysis. Based on DDC-6, the prediction model had the best performance (AUC: 0.75; 95% confidence interval: 0.44-1 in the testing set) and high precision (0.83), recall (0.71), and F1 score (0.77) in the validation dataset.Our study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.
TL;DR: The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.
Abstract: A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...
21 Jun 2010
TL;DR: A meta-analysis of cohort studies revealed that lower HRV was associated with a higher risk of all-cause death and cardiovascular events in patients with CVD.
Abstract: Lower heart rate variability (HRV) is associated with a higher risk of cardiovascular events and mortality, although the extent of the association is uncertain. We performed a meta-analysis of cohort studies to elucidate the association between HRV and the risk of all-cause death or cardiovascular events in patients with cardiovascular disease (CVD) during a follow-up of at least 1 year. We searched four databases (PubMed, MEDLINE, Embase, and Cochrane Central Register of Controlled Trials) and extracted the adjusted hazard ratio (HR) from eligible studies. We included 28 cohort studies involving 3,094 participants in the meta-analysis. Results revealed that lower HRV was associated with a higher risk of all-cause death and cardiovascular events; the pooled HR was 2.27 (95% confidence interval [CI]: 1.72, 3.00) and 1.41 (95% CI: 1.16, 1.72), respectively. In subgroup analyses, the pooled HR of all-cause death was significant for patients with acute myocardial infarction (AMI) but not for those with heart failure. The pooled HR for cardiovascular events was significant for the subgroup of patients with AMI and acute coronary syndrome but not for those with coronary artery disease and heart failure. Additionally, both time and frequency domains of HRV were significantly associated with risk of all-cause death and cardiovascular events in patients with CVD.
TL;DR: The area under the MSE curve for scale 6 to 20 is not relevant to β-blockers and could further warrant independent risk stratification for the prognosis of CHF patients.
Abstract: Aims The influences of nonstationarity and nonlinearity on heart rate time series can be mathematically qualified or quantified by multiscale entropy (MSE) The aim of this study is to investigate the prognostic value of parameters derived from MSE in the patients with systolic heart failure Methods and Results Patients with systolic heart failure were enrolled in this study One month after clinical condition being stable, 24-hour Holter electrocardiogram was recording MSE as well as other standard parameters of heart rate variability (HRV) and detrended fluctuation analysis (DFA) were assessed A total of 40 heart failure patients with a mea age of 56±16 years were enrolled and followed-up for 684±441 days There were 25 patients receiving β-blockers treatment During follow-up period, 6 patients died or received urgent heart transplantation The short-term exponent of DFA and the slope of MSE between scale 1 to 5 were significantly different between patients with or without β-blockers (p = 0014 and p = 0028) Only the area under the MSE curve for scale 6 to 20 (Area6–20) showed the strongest predictive power between survival (n = 34) and mortality (n = 6) groups among all the parameters The value of Area6–20212 served as a significant predictor of mortality or heart transplant (p = 00014) Conclusion The area under the MSE curve for scale 6 to 20 is not relevant to β-blockers and could further warrant independent risk stratification for the prognosis of CHF patients
TL;DR: The result indicates that the proposed model has satisfactory forecasting performance in the big multi-step extremely strong simulating wind speed forecasting.