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

Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection

Recently, tools developed for detecting human activities have been quite prominent in contributing to health issue prevention and long-term healthcare. For this occasion, the current study aimed to evaluate the performance of eye-movement complexity features (from multi-scale entropy analysis) compared to eye-movement conventional features (from basic statistical measurements) on detecting daily computer activities, comprising reading an English scientific paper, watching an English movie-trailer video, and typing English sentences. A total of 150 students participated in these computer activities. The participants’ eye movements were captured using a desktop eye-tracker (GP3 HD Gazepoint™ Canada) while performing the experimental tasks. The collected eye-movement data were then processed to obtain 56 conventional and 550 complexity features of eye movement. A statistic test, analysis of variance (ANOVA), was performed to screen these features, which resulted in 45 conventional and 379 complexity features. These eye-movement features with four combinations were used to build 12 AI models using Support Vector Machine, Decision Tree, and Random Forest (RF). The comparisons of the models showed the superiority of complexity features (85.34% of accuracy) compared to conventional features (66.98% of accuracy). Furthermore, screening eye-movement features using ANOVA enhances 2.29% of recognition accuracy. This study proves the superiority of eye-movement complexity features.

Altered complexity in resting-state fNIRS signal in autism: a multiscale entropy approach

Objective.Feature extraction and recognition in brain signal processing is of great significance for understanding the neurological mechanism of autism spectrum disorder (ASD). Resting-state (RS) functional near-infrared spectroscopy measurement provides a way to investigate the possible alteration in ASD-related complexity of resting-state (RS) functional near-infrared spectroscopy (fNIRS) signals and to explore the relationship between brain functional connectivity and complexity.Approach.Using the multiscale entropy (MSE) of fNIRS signals recorded from the bilateral temporal lobes (TLs) on 25 children with ASD and 22 typical development (TD) children, the pattern of brain complexity was assessed for both the ASD and TD groups.Main results.The quantitative analysis of MSE revealed the increased complexity in RS-fNIRS in children with ASD, particularly in the left temporal lobe. The complexity in the RS signal and resting state functional connectivity (RSFC) were also observed to exhibit negative correlation in the medium magnitude.Significance.These results indicated that the MSE might serve as a novel measure for RS-fNIRS signals in characterizing and understanding ASD.

A decomposition-ensemble approach for tourism forecasting

With the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.

Multiscale Entropy Analysis of Postural Stability for Estimating Fall Risk via Domain Knowledge of Timed-Up-And-Go Accelerometer Data for Elderly People Living in a Community

As people in developed countries live longer, assessing the fall risk becomes more important. A major contributor to the risk of elderly people falling is postural instability. This study aimed to use the multiscale entropy (MSE) analysis to evaluate postural stability during a timed-up-and-go (TUG) test. This test was deemed a promising method for evaluating fall risk among the elderly in a community. The MSE analysis of postural instability can identify the elderly prone to falling, whereupon early medical rehabilitation can prevent falls. Herein, an objective approach is developed for assessing the postural stability of 85 community-dwelling elderly people (aged 76.12 ± 6.99 years) using the short-form Berg balance scale. Signals were collected from the TUG test using a triaxial accelerometer. A segment-based TUG (sTUG) test was designed, which can be obtained according to domain knowledge, including “Sit-to-Walk (STW),” “Walk,” “Turning,” and “Walk-to-Sit (WTS)” segments. Employing the complexity index (CI) of sTUG can reveal information about the physiological dynamics’ signal for postural stability assessment. Logistic regression was used to assess the fall risk based on significant features of CI related to sTUG. MSE curves for subjects at risk of falling (n = 19) exhibited different trends from those not at risk of falling (n = 66). Additionally, the CI values were lower for subjects at risk of falling than those not at risk of falling. Results show that the area under the curve for predicting fall risk among the elderly subjects with complexity index features from the overall TUG test is 0.797, which improves to 0.853 with the sTUG test. For the elderly living in a community, early assessment of the CI for sTUG using MSE can help predict the fall risk.

Automatic anesthesia depth staging using entropy measures and relative power of electroencephalogram frequency bands

Many of the surgeries performed under general anesthesia are aided by electroencephalogram (EEG) monitoring. With increased focus on detecting the anesthesia states of patients in the course of surgery, more attention has been paid to analyzing the power spectra and entropy measures of EEG signal during anesthesia. In this paper, by using the relative power of EEG frequency bands and the EEG entropy measures, a new method for detecting the depth of anesthesia states has been presented based on the least squares support vector machine (LS-SVM) classifiers. EEG signals were recorded from 20 patients before, during and after general anesthesia in the operating room at a sampling rate of 200 Hz. Then, 12 features were extracted from each EEG segment, 10 s in length, which are used for anesthesia state monitoring. No significant difference was observed (p > 0.05) between these features and the bispectral index (BIS), which is the commonly used measure of anesthetic effect. The used LS-SVM classifier based method is able to identify the anesthesia states with an accuracy of 80% with reference to the BIS index. Since the underlying equation of the utilized LS-SVM is linear, the computational time of the algorithm is not significant and therefore it can be used for online application in operation rooms.

Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.

Analysis of Electroencephalogram of patients with specific low back pain with the massage treatment

Through the investigation of the difference of approximate entropy (ApEn) and Hilbert-Huang Transform Marginal spectrum entropy (HHTMSEn) of the Electroencephalogram (EEG) signals of specific low back pain (SLBP) patients before and after the massage, we wanted to reveal the impact of the massage in the brain, and provided the basis for the massage treatment of SLBP patients. We recruited twenty-six SLBP patients and collected their spontaneous EEG signals before and after the massage. Firstly, we analyzed the ApEn and HHTMSEn of fourteen channels before and after the massage, and results showed that values of ApEn and HHTMSEn after the massage were less than the values before the massage significantly. And then, we extracted δ, θ, α and β rhythms of the EEG signal, and analyzed the ApEn and HHTMSEn of the four rhythms before and after the massage. The results showed that the ApEn values of δ and α rhythm after the massage were significantly less than the values before the massage, and the HHTMSEn values of β rhythms were significantly less than the values before the massage. The results of this study suggested the complexity of EEG signals was reduced with the relief of the pain after the massage therapy, and the change of pain of the SLBP patients was closely related to the change of the rhythms of the brain in the massage therapy, and the ApEn and the HHTMSEn features could serve as a base for quantitative assessment of SLBP condition after the massage therapy.

Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks

Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques.

Comparison of FFT and marginal spectra of EEG using empirical mode decomposition to monitor anesthesia

BACKGROUND AND OBJECTIVE

Intraoperative awareness refers that patients can recall aspects of their surgery after being put under general anesthesia. This distressing complication causes affected patients to be conscious and probably feel pain, leading to emotional trauma or other sequelae. Monitoring and administrating the depth of anesthesia is necessary to prevent patients from awareness during a medical operation. In this paper, we analyzed the electroencephalograms (EEGs) of patients to characterize their anesthesia. The data set, "awareness" and "anesthesia" groups, each contained 558 samples, including patients who had undergone different types of surgeries.

METHODS

EEG signals acquired from patients in an aware state or under anesthesia were decomposed into a set of intrinsic mode functions (IMFs) through empirical mode decomposition (EMD). Fast Fourier transform (FFT) and Hilbert transform (HT) analyses were then performed on each IMF to determine the frequency spectra. The probability distributions of expected values of frequencies were generated for the same IMF in the two groups of patients. The corresponding statistical data, including analysis of variance tests, were also calculated. A receiver operating characteristic curve was used to identify optimal frequency value to discriminate between the two states of consciousness.

RESULTS

The frequencies of the IMFs for aware patients were found to be higher than those for anesthetized patients. The optimal frequency threshold by using FFT (or HT) for IMF 1 was 21.08 (or 25.00) Hz. IMF1 performed the highest with respect to the area under the curve (AUC) of 0.993 for FFT (or 0.989 for HT); hence it can be applied as a useful classifier to distinguish between fully anesthetized patients and aware patients.

CONCLUSIONS

This paper proposes a method for identifying whether patients' state of consciousness during a range of surgery types is "under anesthesia" or "aware." Our method involves using EEG to characterize the depth of anesthesia through two frequency analysis techniques. On the basis of our analyses, we conclude that the performance of IMF1 is satisfactory in distinguishing between patients' states of consciousness during surgery requiring general anesthesia.

Comparing Postural Stability Entropy Analyses to Differentiate Fallers and Non-fallers

The health and financial cost of falls has spurred research to differentiate the characteristics of fallers and non-fallers. Postural stability has received much of the attention with recent studies exploring various measures of entropy. This study compared the discriminatory ability of several entropy methods at differentiating two paradigms in the center-of-pressure of elderly individuals: (1) eyes open (EO) vs. eyes closed (EC) and (2) fallers (F) vs. non-fallers (NF). Methods were compared using the area under the curve (AUC) of the receiver-operating characteristic curves developed from logistic regression models. Overall, multiscale entropy (MSE) and composite multiscale entropy (CompMSE) performed the best with AUCs of 0.71 for EO/EC and 0.77 for F/NF. When methods were combined together to maximize the AUC, the entropy classifier had an AUC of for 0.91 the F/NF comparison. These results suggest researchers and clinicians attempting to create clinical tests to identify fallers should consider a combination of every entropy method when creating a classifying test. Additionally, MSE and CompMSE classifiers using polar coordinate data outperformed rectangular coordinate data, encouraging more research into the most appropriate time series for postural stability entropy analysis.

EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

A critical care monitoring system for depth of anaesthesia analysis based on entropy analysis and physiological information database

Diagnosis of depth of anaesthesia (DoA) plays an important role in treatment and drug usage in the operating theatre and intensive care unit. With the flourishing development of analysis methods and monitoring devices for DoA, a small amount of physiological data had been stored and shared for further researches. In this paper, a critical care monitoring (CCM) system for DoA monitoring and analysis was designed and developed, which includes two main components: a physiologic information database (PID) and a DoA analysis subsystem. The PID, including biologic data and clinical information was constructed through a browser and server model so as to provide a safe and open platform for storage, sharing and further study of clinical anaesthesia information. In the analysis of DoA, according to our previous studies on approximate entropy, sample entropy (SampEn) and multi-scale entropy (MSE), the SampEn and MSE were integrated into the subsystem for indicating the state of patients underwent surgeries in real time because of their stability. Therefore, this CCM system not only supplies the original biological data and information collected from the operating room, but also shares our studies for improvement and innovation in the research of DoA.

Measuring center of pressure signals to quantify human balance using multivariate multiscale entropy by designing a force platform

To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis. In order to prove that the system can estimate the different magnitude of different sways in comparison with the commercial Advanced Mechanical Technology Incorporation (AMTI) system, four sway tests have been developed (i.e., eyes open, eyes closed, eyes open with water pad, and eyes closed with water pad) to produce different sway displacements. Firstly, static and dynamic tests were conducted to investigate the feasibility of the system. Then, correlation tests of the CPCMS and AMTI systems have been compared with four sway tests. The results are within the acceptable range. Furthermore, multivariate empirical mode decomposition (MEMD) and enhanced multivariate multiscale entropy (MMSE) analysis methods have been used to analyze COP data reported by the CPCMS and compare it with the AMTI system. The improvements of the CPCMS are 35% to 70% (open eyes test) and 60% to 70% (eyes closed test) with and without water pad. The AMTI system has shown an improvement of 40% to 80% (open eyes test) and 65% to 75% (closed eyes test). The results indicate that the CPCMS system can achieve similar results to the commercial product so it can determine the balance.