Publications by authors named "Cheng Yen Wang"

The "Mozart effect" in epilepsy was first identified by Hughes et al. in 1998. In their treatment of 29 (ages 3-47) patients with epilepsy, including children, the patients showed a significant reduction in epileptic activity on the EEG while listening to "Mozart's Sonata for Two Pianos K.

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A growing body of research suggests a link between Dark Triad personality traits and cyber aggression but inconsistencies exist. These inconsistencies may be due to limitations in past studies (e.g.

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Objective: To investigate whether dynamic cerebral autoregulation (CA) and neuroimaging characteristics are determinants of poststroke cognitive impairment (PSCI).

Methods: Eighty patients within 7 days of acute ischemic stroke and 35 age- and sex-matched controls were enrolled. In the patients with stroke, brain magnetic resonance imaging and dynamic CA were obtained at baseline, and dynamic CA was followed up at 3 months and 1 year.

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Article Synopsis
  • The study introduces a new method called the multimodal coupling analysis (MMCA) to quantify respiratory sinus arrhythmia (RSA) and assess parasympathetic function, addressing limitations of the commonly used Fourier spectral analysis.
  • Using data from 20 young and 20 elderly subjects, the MMCA method showed that elderly individuals had diminished RSA activity and nonlinearity in their heart rate-respiration dynamics compared to younger individuals.
  • The findings suggest that MMCA, along with a cycle-based analysis, offers a more effective way to evaluate aging effects on parasympathetic function and the complexity of RSA waveforms than traditional Fourier and wavelet methods.
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Temporal cardiac properties provide alternative information in analyzing heart rate variability (HRV), which may be disregarded by the standard HRV analyses. Patients with congestive heart failure (CHF) are known to have distinct temporal features from the healthy individuals. However, the underlying mechanism leading to the variation remains unclear.

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Monitoring fetal heart rate during pregnancy is essential to assist clinicians in making more timely decisions. Non-invasive monitoring of fetal heart activities using abdominal ECGs is useful for diagnosis of heart defects. However, the extracted fetal ECGs are usually too weak to be robustly detected.

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Background and Purpose- Cerebral autoregulation is impaired in patients with acute ischemic stroke. The purpose of this study was to investigate whether dynamic cerebral autoregulation (dCA) indices constitute an independent functional outcome predictor of acute ischemic stroke. Methods- In this study, 86 patients at days 3 to 7 after acute ischemic stroke and 40 age- and sex-matched controls were enrolled for assessing their dCA indices under spontaneous hemodynamic fluctuations.

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We compared the dynamic cerebral autoregulation (dCA) indices between 5- and 10-minute data lengths by analyzing 37 patients with ischemic stroke and 51 controls in this study. Correlation coefficient () and transfer function analysis were applied for dCA analysis. and phase shift in all frequency bands were not significantly different between 5- and 10-minute recordings [mean difference: = 0.

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Parameters derived from the goniometer measures in the Pendulum test are insufficient in describing the function of abnormal muscle activity in the spasticity. To explore a quantitative evaluation of muscle activation-movement interaction, we propose a novel index based on phase amplitude coupling (PAC) analysis with the consideration of the relations between movement and surface electromyography (SEMG) activity among 22 hemiplegic stroke patients. To take off trend and noise, we use the empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) of the angular velocity due to its superior decomposing ability in nonlinear oscillations.

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Transfer function analysis (TFA) is a frequently used method to assess dynamic cerebral autoregulation (CA) using spontaneous oscillations in blood pressure (BP) and cerebral blood flow velocity (CBFV). However, controversies and variations exist in how research groups utilise TFA, causing high variability in interpretation. The objective of this study was to evaluate between-centre variability in TFA outcome metrics.

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Objectives: The physiologic relationship between slow-wave activity (SWA) (0-4 Hz) on the electroencephalogram (EEG) and high-frequency (0.1-0.4 Hz) cardiopulmonary coupling (CPC) derived from electrocardiogram (ECG) sleep spectrograms is not known.

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Article Synopsis
  • The study explores the relationship between sleep EEG characteristics and heart rate variability (HRV) to better understand sleep states and diagnose sleep-related conditions.
  • Utilizing Hilbert Huang transform and detrended fluctuation analysis, researchers assessed the dynamics of slow and fast-wave oscillations in sleep EEG alongside the autonomic nervous system's activity.
  • Results indicate that specific EEG frequency and amplitude features are effective indicators for determining the depth of sleep and distinguishing between sleep stages, with new methods showing stronger correlations than traditional analysis.
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  • The study aimed to improve the analysis of ventricular fibrillation (VF) during CPR, as traditional methods may require stopping CPR, which can worsen patient outcomes.
  • Researchers developed an algorithm that uses empirical mode decomposition and least square mean fitting to isolate and remove CPR artifacts from ECG readings, allowing for better preservation of the VF waveform.
  • Analysis of 150 patients revealed that this new algorithm significantly improved the identification of useful VF signals while maintaining the predictive accuracy for successful defibrillation, compared to traditional corrupted ECG readings.
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