Jiann-Shing Shieh

COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOVEL NOISE ENHANCED DATA ANALYSIS METHOD

The phenomenon of mode-mixing caused by intermittence signals is an annoying problem in Empirical Mode Decomposition (EMD) method. The noise assisted method of Ensemble EMD (EEMD) has not only effectively resolved this problem but also generated a new one, which tolerates the residue noise in the signal reconstruction. Of course, the relative magnitude of the residue noise could be reduced with large enough ensemble, it would be too time consuming to implement. An improved algorithm of noise enhanced data analysis method is suggested in this paper. In this approach, the residue of added white noises can be extracted from the mixtures of data and white noises via pairs of complementary ensemble IMFs with positive and negative added white noises. Though this new approach yields IMF with the similar RMS noise as EEMD, it effectively eliminated residue noise in the IMFs. Numerical experiments were conducted to demonstrate the new approach and also illustrate the problems of mode splitting and translation.

Complexity of intracranial pressure correlates with outcome after traumatic brain injury

This study applied multiscale entropy analysis to investigate the correlation between the complexity of intracranial pressure waveform and outcome after traumatic brain injury. Intracranial pressure and arterial blood pressure waveforms were low-pass filtered to remove the respiratory and pulse components and then processed using a multiscale entropy algorithm to produce a complexity index. We identified significant differences across groups classified by the Glasgow Outcome Scale in intracranial pressure, pressure-reactivity index and complexity index of intracranial pressure (P < 0.0001; P = 0.001; P < 0.0001, respectively). Outcome was dichotomized as survival/death and also as favourable/unfavourable. The complexity index of intracranial pressure achieved the strongest statistical significance (F = 28.7; P < 0.0001 and F = 17.21; P < 0.0001, respectively) and was identified as a significant independent predictor of mortality and favourable outcome in a multivariable logistic regression model (P < 0.0001). The results of this study suggest that complexity of intracranial pressure assessed by multiscale entropy was significantly associated with outcome in patients with brain injury.

Genetic algorithms applied in online autotuning PID parameters of a liquid-level control system

In this paper, a simple genetic algorithm (GA) method has been applied in a real-time experiment on a liquid-level control system for online autotuning proportional-integral-derivative (PID) parameters. Our proposed method can automatically choose the best PID parameters for each generation. Then, using the reproduction, crossover and mutation to create the new population for other PID parameters, it can continuously control the liquid-level system until the preset iteration number is reached. Finally, the best PID parameters can be obtained. Furthermore, two selection methods, roulette wheel and tournament, have been compared in real-time experiments. Real-time experimental results are given to demonstrate the effectiveness and usefulness for online tuning PID parameters via this evolution process.

Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition.

How to quantify the complexity of a physiological signal is a crucial issue for verifying the underlying mechanism of a physiological system. The original algorithm of detrended fluctuation analysis (DFA) quantifies the complexity of signals using the DFA scaling exponent. However, the DFA scaling exponent is suitable only for an integrated time series but not the original signal. Moreover, the method of least squares line is a simple detrending operation. Thus, the analysis results of the original DFA are not sufficient to verify the underlying mechanism of physiological signals. In this study, we apply an innovative timescale-adaptive algorithm of empirical mode decomposition (EMD) as the detrending operation for the modified DFA algorithm. We also propose a two-parameter scale of randomness for DFA to replace the DFA scaling exponent. Finally, we apply this modified algorithm to the database of human heartbeat interval from Physiobank, and it performs well in identifying characteristics of heartbeat interval caused by the effects of aging and of illness.

Adaptive Computation of Multiscale Entropy and Its Application in EEG Signals for Monitoring Depth of Anesthesia During Surgery

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (S E ) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the S E from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of S E in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients.

Complexity of heart rate variability predicts outcome in intensive care unit admitted patients with acute stroke

Background Heart rate variability (HRV) has been proposed as a predictor of acute stroke outcome. This study aimed to evaluate the predictive value of a novel non-linear method for analysis of HRV, multiscale entropy (MSE) and outcome of patients with acute stroke who had been admitted to the intensive care unit (ICU). Methods The MSE of HRV was analysed from 1 h continuous ECG signals in ICU-admitted patients with acute stroke and controls. The complexity index was defined as the area under the MSE curve (scale 1–20). A favourable outcome was defined as modified Rankin scale 0–2 at 3 months after stroke. Results The trends of MSE curves in patients with atrial fibrillation (AF) (n=77) were apparently different from those in patients with non-AF stroke (n=150) and controls (n=60). In addition, the values of complexity index were significantly lower in the patients with non-AF stroke than in the controls (25.8±.3 vs 32.3±4.3, p<0.001). After adjustment for clinical variables, patients without AF who had a favourable outcome were significantly related to higher complexity index values (OR=1.15, 95% CI 1.07 to 1.25, p<0.001). Importantly, the area under the receiver operating characteristic curve for predicting a favourable outcome of patients with non-AF stroke from clinical parameters was 0.858 (95% CI 0.797 to 0.919) and significantly improved to 0.903 (95% CI 0.853 to 0.954) after adding on the parameter of complexity index values (p=0.020). Conclusions In ICU-admitted patients with acute stroke, early assessment of the complexity of HRV by MSE can help in predicting outcomes in patients without AF.

Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module). Multivariate empirical mode decomposition (MEMD) algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3) is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN) methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC) curve (AUC) of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.

Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy

In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measure its complexity, such as spectral entropy, approximate entropy (ApEn) and sample entropy (SampEn). However, as a weak physiological signal, EEG is easily subject to interference from external sources such as the electric power, electric knives and other electrophysiological signal sources, which lead to a reduction in the accuracy of DOA determination. In this study, we adopt the multivariate empirical mode decomposition (MEMD) to decompose and reconstruct the EEG recorded from clinical surgeries according to its best performance among the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complementary EEMD (CEEMD) and the MEMD. Moreover, according to the comparison between SampEn and ApEn in measuring DOA, the SampEn is a practical and efficient method to monitor the DOA during surgeries at real time.

Multivariate Multiscale Entropy Applied to Center of Pressure Signals Analysis: An Effect of Vibration Stimulation of Shoes

Falls are unpredictable accidents and resulting injuries can be serious to the elderly. A preventative solution can be the use of vibration stimulus of white noise to improve the sense of balance. In this work, a pair of vibration shoes were developed and controlled by a touch-type switch which can generate mechanical vibration noise to stimulate the patient’s feet while wearing the shoes. In order to evaluate the balance stability and treatment effect of vibrating insoles in these shoes, multivariate multiscale entropy (MMSE) algorithm is applied to calculate the relative complexity index of reconstructed center of pressure (COP) signals in antero-posterior and medio-lateral directions by the multivariate empirical mode decomposition (MEMD). The results show that the balance stability of 61.5% elderly subjects is improved after wearing the developed shoes, which is more than 30.8% using multiscale entropy. In conclusion, MEMD-enhanced MMSE is able to distinguish the smaller differences between before and after the use of vibration shoes in both two directions, which is more powerful than the empirical mode decomposition (EMD)-enhanced MSE in each individual direction.

Dynamic cerebral autoregulation in carotid stenosis before and after carotid stenting

BACKGROUND Impaired dynamic cerebral autoregulation (DCA) has been shown in patients with severe (> or =70%) internal carotid artery (ICA) stenosis, but DCA in moderate (50% to 69%) ICA stenosis, especially its response to carotid revascularization, has rarely been reported. Our study aimed to characterize DCA in severe and moderate ICA stenosis before and after carotid stenting. METHODS This study included 21 patients with ICA stenosis > or =50% who received carotid stenting. Data of arterial blood pressure and cerebral blood flow velocity of the middle cerebral artery, measured by transcranial Doppler, were collected for 10 minutes < or =24 hours before and after stenting. The DCA index, represented as aMx, was assessed by calculating the Pearson product-moment correlation coefficient of spontaneous arterial blood pressure and cerebral blood flow velocity fluctuations. The relationship between aMx and stenotic severity and also alternations of aMx before and after stenting were assessed. RESULTS Carotid stenting was effective to improve the DCA in the stenting side but not in the contralateral nonstenting side. In considering individual ICAs, the average aMx (mean +/- SD) increased significantly from ICA stenosis <50% (0.117 +/- 0.091) to 50% to 69% (0.349 +/- 0.144), 70% to 99% (0.456 +/- 0.147), and total occlusion (0.557 +/- 0.210; P < .05, P < .01, and P < .01, compared with 50% to 69%, 70% to 99%, or total occlusion with <50% stenosis). The correlation between the degree of ICA stenosis and the aMx was also significant (r = 0.693, P < .005). The aMx improved significantly in the stented side after carotid stenting in both moderate and severe ICA stenosis, and this finding was not affected by age, sex, risk factors, or clinical symptoms. CONCLUSIONS In addition to patients with severe carotid stenosis, patients with moderate carotid stenosis may also have impaired DCA that can be restored after carotid stenting.