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Site response analyses are crucial for estimating local effects on ground shaking during earthquakes. However, recent investigations utilizing KiK-net borehole array data have revealed a consistent underprediction of high-frequency ground motion by both equivalent linear and fully nonlinear methods, contrary to expectations. This study reassess the accuracy of 1D site response analysis methods, including equivalent linear, frequency-dependent equivalent linear model, and nonlinear analysis, by integrating depth-dependent stiffness and adjusted shear wave velocity. Nine instrumented vertical arrays, featuring a total of 132 recorded ground motions, are subjected to analysis. For the sites and ground motions considered, the results indicate that the nonlinear method performs without significant bias, whereas the equivalent linear and frequency-dependent equivalent linear methods tend to respectively underestimate and overestimate high-frequency results. To enhance accuracy, it is recommended to incorporate depth-dependent stiffness and adjusted shear wave velocity when predicting high-frequency ground motion in site response analyses. [ABSTRACT FROM AUTHOR]

Abstract Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single‐species alternative. We expect model‐free ecosystem‐based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species.

Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems.Here, we develop a management scheme that is based on predictive models driven by the observed dynamics.We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single‐species alternative.We expect model‐free ecosystem‐based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species. [ABSTRACT FROM AUTHOR]

AbstractThe current series of studies examine how local food prices are affected by domestic and international factors. This research advances the existing body of knowledge by examining this effect at different quantiles, frequencies, and times. We use research data from January 1999 to August 2022 using three local and six global variables as independent variables. Additionally, our study uses recent econometric methods, including Wavelet Coherence, Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ). Moreover, this research uses the Quantile Regression (QR) approach to determine how reliable the findings are. Based on the GCQ approach, the results demonstrate that the correlation persists at most of the quantiles. Moreover, the WC results demonstrate a substantial association between local prices of food and the independent factors across various frequencies and times. Additionally, QQR estimates demonstrate that the impact of exogenous variables on food prices vary among quantiles. These findings are also supported by the QR method. Last but not least, our study offers policy suggestions obtained based on the results of this study.

The dynamics of cardiac signals can be studied using methods for nonlinear analysis of heart rate variability (HRV). The methods that are used in the article to investigate the fractal, multifractal and informational characteristics of the intervals between heartbeats (RR time intervals) are: Rescaled Range, Detrended Fluctuation Analysis, Multifractal Detrended Fluctuation Analysis, Poincaré plot, Approximate Entropy and Sample Entropy. Two groups of people were studied: 25 healthy subjects (15 men, 10 women, mean age: 56.3 years) and 25 patients with arrhythmia (13 men, 12 women, mean age: 58.7 years). The results of the application of the methods for nonlinear analysis of HRV in the two groups of people studied are shown as mean ± std. The effectiveness of the methods was evaluated by t-test and the parameter Area Under the Curve (AUC) from the Receiver Operator Curve (ROC) characteristics. The studied 11 parameters have statistical significance (p < 0.05); therefore, they can be used to distinguish between healthy and unhealthy subjects. It was established by applying the ROC analysis that the parameters Hq=2(MFDFA), F(α)(MFDFA) and SD2(Poincaré plot) have a good diagnostic value; H(R/S), α1(DFA), SD1/SD2(Poincaré plot), ApEn and SampEn have a very good score; α2(DFA), αall(DFA) and SD1(Poincaré plot) have an excellent diagnostic score. In conclusion, the methods used for nonlinear analysis of HRV have been evaluated as effective, and with their help, new perspectives are opened in the diagnosis of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown that the predictive value of traditional linear (time domain and frequency domain) heart rate variability (HRV) for the survival of patients with advanced non-small cell lung cancer (NSCLC) is controversial. Nonlinear methods, based on the concept of complexity, have been used to evaluate HRV, providing a new means to reveal the physiological and pathological changes in HRV. This study aimed to assess the association between heartbeat complexity and overall survival in patients with advanced NSCLC. Methods: This study included 78 patients with advanced NSCLC (mean age: 62.0 ± 9.3 years). A 5-min resting electrocardiogram of advanced NSCLC patients was collected to analyze the following HRV parameters: time domain indicators, i.e., standard deviation of the normal-normal intervals (SDNN) and root mean square of successive interval differences (RMSSD); frequency domain indicators, i.e., total power (TP), low frequency power (LF), high frequency power (HF), and the ratio of LF to HF (LF/HF); nonlinear HRV indicators characterizing heartbeat complexity, i.e., approximate entropy (ApEn), sample entropy (SampEn), and recurrence quantification analysis (RQA) indexes: mean diagonal line length (Lmean), maximal diagonal line length (Lmax), recurrence rate (REC), determinism (DET), and shannon entropy (ShanEn). Results: Univariate analysis revealed that the linear frequency domain parameter HF and nonlinear RQA parameters Lmax, REC, and DET were significantly correlated with the survival of advanced NSCLC patients (all p < 0.05). After adjusting for confounders in the multivariate analysis, HF, REC, and DET were found to be independent prognostic factors for the survival of patients with advanced NSCLC (all p < 0.05). Conclusion: There was an independent association between heartbeat complexity and survival in advanced NSCLC patients. The nonlinear analysis method based on RQA may provide valuable additional information for the prognostic stratification of patients with advanced NSCLC and may supplement the traditional time domain and frequency domain analysis methods. [ABSTRACT FROM AUTHOR]

Nowadays, nonlinear vibration methods are increasingly used for the detection of damage mechanisms in polymer matrix composite (PMC) materials, which are anisotropic and heterogeneous. The originality of this study was the use of two nonlinear vibration methods to detect different types of damage within PMC through an in situ embedded polyvinylidene fluoride (PVDF) piezoelectric sensor. The two used methods are nonlinear resonance (NLR) and single frequency excitation (SFE). They were first tested on damage introduced during the manufacturing of the smart PMC plates, and second, on the damage that occurred after the manufacturing. The results show that both techniques are interesting, and probably a combination of them will be the best choice for SHM purposes. During the experimentation, an accelerometer was used, in order to validate the effectiveness of the integrated PVDF sensor. [ABSTRACT FROM AUTHOR]

The current series of studies examine how local food prices are affected by domestic and international factors. This research advances the existing body of knowledge by examining this effect at different quantiles, frequencies, and times. We use research data from January 1999 to August 2022 using three local and six global variables as independent variables. Additionally, our study uses recent econometric methods, including Wavelet Coherence, Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ). Moreover, this research uses the Quantile Regression (QR) approach to determine how reliable the findings are. Based on the GCQ approach, the results demonstrate that the correlation persists at most of the quantiles. Moreover, the WC results demonstrate a substantial association between local prices of food and the independent factors across various frequencies and times. Additionally, QQR estimates demonstrate that the impact of exogenous variables on food prices vary among quantiles. These findings are also supported by the QR method. Last but not least, our study offers policy suggestions obtained based on the results of this study. [ABSTRACT FROM AUTHOR]

The analysis and design of pipe networks is usually a very complex task and depending of many factors. The paper here presented explores issues related to the accuracy and robustness of three iterative nonlinear methods used for designing pipe networks: Hardy Cross method, Gradient method and Newton-Raphson method. The last two methods are implemented in EPANET and Matlab, through the fsolve function. In order to evaluate the performance of each numerical method, three pipe networks, were assessed. No differences in the results were found by using fsolve (of MatLab) and Hardy-Cross methods. However, the EPANET results show differences up to 115% regarding other two methods. This behavior can be explained by the magnitude of head loss and the flow rate in some tubes of the network with errors close to the errors set up and by the Nodal Newton-Raphson method used at EPANET's code. The Hardy Cross method proved to be robust on three evaluated cases. [ABSTRACT FROM AUTHOR]

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.

AimIn many cases, the dynamics of psychotherapeutic change processes is characterized by sudden and critical transitions. In theoretical terms, these transitions may be “phase transitions” of self-organizing nonlinear systems. Meanwhile, a variety of methods is available to identify phase transitions even in short time series. However, it is still an open question if different methods for timeseries analysis reveal convergent results indicating the moments of critical transitions and related precursors.Methods and ProceduresSeven concepts which are commonly used in nonlinear time series analysis were investigated in terms of their ability to identify changes in psychological time series: Recurrence Plots, Change Point Analysis, Dynamic Complexity, Permutation Entropy, Time Frequency Distributions, Instantaneous Frequency, and Synchronization Pattern Analysis, i.e., the dynamic inter-correlation of the system’s variables. Phase transitions were simulated by shifting control parameters in the Hénon map dynamics, in a simulation model of psychotherapy processes (one by an external shift of the control parameter and one created by a simulated control parameter shift), and three sets of empirical time series generated by daily self-ratings of patients during the treatment.ResultsThe applied methods showed converging results indicating the moments of dynamic transitions within an acceptable tolerance. The convergence of change points was confirmed statistically by a comparison to random surrogates. In the three simulated dynamics with known phase transitions, these could be identified, and in the empirical cases, the methods converged indicating one and the same transition (possibly the phase transitions of the cases). Moreover, changes that did not manifest in a shift of mean or variance could be detected.ConclusionChanges can occur in many different ways in the psychotherapeutic process. For instance, there can be very slow and small transitions or very high and sudden ones. The results show the validity and stability of different measures indicating pattern transitions and/or early warning signals of those transitions. This has profound implications for real-time monitoring in psychotherapy, especially in cases where a transition is not obvious to the eye. Reliably identifying points of change is mandatory also for research on precursors, which in turn can help improving treatment.

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one. [ABSTRACT FROM AUTHOR]

Aim: In many cases, the dynamics of psychotherapeutic change processes is characterized by sudden and critical transitions. In theoretical terms, these transitions may be "phase transitions" of self-organizing nonlinear systems. Meanwhile, a variety of methods is available to identify phase transitions even in short time series. However, it is still an open question if different methods for timeseries analysis reveal convergent results indicating the moments of critical transitions and related precursors. Methods and Procedures: Seven concepts which are commonly used in nonlinear time series analysis were investigated in terms of their ability to identify changes in psychological time series: Recurrence Plots, Change Point Analysis, Dynamic Complexity, Permutation Entropy, Time Frequency Distributions, Instantaneous Frequency, and Synchronization Pattern Analysis, i.e., the dynamic inter-correlation of the system's variables. Phase transitions were simulated by shifting control parameters in the Hénon map dynamics, in a simulation model of psychotherapy processes (one by an external shift of the control parameter and one created by a simulated control parameter shift), and three sets of empirical time series generated by daily self-ratings of patients during the treatment. Results: The applied methods showed converging results indicating the moments of dynamic transitions within an acceptable tolerance. The convergence of change points was confirmed statistically by a comparison to random surrogates. In the three simulated dynamics with known phase transitions, these could be identified, and in the empirical cases, the methods converged indicating one and the same transition (possibly the phase transitions of the cases). Moreover, changes that did not manifest in a shift of mean or variance could be detected. Conclusion: Changes can occur in many different ways in the psychotherapeutic process. For instance, there can be very slow and small transitions or very high and sudden ones. The results show the validity and stability of different measures indicating pattern transitions and/or early warning signals of those transitions. This has profound implications for real-time monitoring in psychotherapy, especially in cases where a transition is not obvious to the eye. Reliably identifying points of change is mandatory also for research on precursors, which in turn can help improving treatment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple approach to the construction of a general class of L -stable explicit second-order one-step methods for solving stiff problems. These methods are nonlinear and derive from a novel approximation for the right-hand side functions of differential equations inspired by the nonstandard finite difference methodology introduced by Mickens. Through rigorous mathematical analysis, it is proved that the proposed numerical methods are not only explicit and L -stable, but also convergent of order two. Therefore, they are suitable and efficient to solve stiff problems. The proposed numerical methods generalize and improve a nonstandard explicit integration scheme for initial value problems formulated by Ramos (2007). Moreover, the present approach can be extended to construct A -stable and L -stable high-order explicit one-step methods for differential equations. Finally, the theoretical findings and advantages of the developed numerical methods are supported and illustrated by a series of numerical experiments in which stiff problems are considered. [ABSTRACT FROM AUTHOR]

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

در این مقاله قابلیت پیش‌بینی بازده روزانه قیمت جهانی طلا از تاریخ 25/07/2011 تا 17/12/2012 مورد بررسی قرار گرفته است. بدین منظور ابتدا با استفاده از آزمون براک- دیکرت- شاینکمن (BDS) به بررسی خطی، غیرخطی و آشوبناک بودن سری مورد مطالعه پرداخته شده است. نتایج تحقیق فرض تصادفی بودن سری مورد مطالعه را رد می‌کند که شاهدی بر پیش‌بینی پذیر بودن بازده روزانه قیمت طلاست. همچنین فرضیه عدم وجود رابطه غیرخطی در جملات پسماند مدل خطی رد می‌شود که نشان از وجود رفتار غیرخطی در سری مورد بررسی است. برای پیش‌بینی بازده روزانه قیمت طلا یک مدل عصبی فازی ANFIS طراحی گردیده و نتایج آن با استفاده از معیارهای مختلف مورد ارزیابی قرار گرفته است. همچنین نتایج با نتایج دو مدل خطی ARMA و غیرخطی GARCH مقایسه شد که مطابق انتظار، مدل غیرخطی ANFIS پیش‌بینی بهتری از سایر مدل­های رقیب داشت. در نهایت با استفاده از آماره مورگان- گرنجر- نیبولد (MGN) معنی‌داری اختلاف پیش‌بینی مدل‌ها مورد بررسی قرار گرفت. نتایج حاکی از معنی­دار بودن اختلاف پیش­بینی مدل­های غیرخطی نسبت به مدل خطی ARMA است.

The article discusses a calculation technique for a reinforced concrete building with a braced frame on an earthquake. It allows to produce an adequate assessment of a of seismic stability level. A direct simulation of concrete, reinforcement and their binding with the help of volumetric and core finite elements is fulfilled. The paper presents the concrete work chart and considers mathematical models for the material. The authors propose calculation technique of reinforced concrete buildings with the actual reinforcement taking into account. The results of the numerical calculations of structural elements (crossbar) are compared to the field experiments conducted by US Department of Transportation Federal Highway Administration. The article examines the level of seismic stability of buildings with a braced frame, the actual reinforcement taking into account when calculating on a seismic action. [ABSTRACT FROM AUTHOR]

In our previous work, we have developed a sudden cardiac death index (SCDI) using electrocardiogram (ECG) signals that could effectively predict the occurrence of SCD four minutes before the onset. Thus, the prediction of SCD before its onset by using heart rate variability (HRV) signals is a worthwhile task for further investigation. Therefore, in this paper, a new novel methodology to automatically classify the HRV signals of normal and subjects at risk of SCD by using nonlinear techniques has been presented. In this study, we have predicted SCD by analyzing four-minutes of HRV signals (separately for each one-minute interval) prior to SCD occurrence by using nonlinear features such as Renyi entropy (REnt), fuzzy entropy (FE), Hjorth's parameters (activity, mobility and complexity), Tsallis entropy (TEnt), and energy features of discrete wavelet transform (DWT) coefficients. All the clinically significant features obtained are ranked using their t -value and fed to classifiers such as K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). In this work, we have achieved an accuracy of 97.3%, 89.4%, 89.4%, and 94.7% for prediction of SCD one, two, three, and four minutes prior to the SCD onset respectively using SVM classifier. Furthermore, we have also developed a novel SCD Index (SCDI) by using nonlinear HRV signal features to classify the normal and SCD prone HRV signals. Our proposed technique is able to identify the person at risk of developing SCD four minutes earlier, thereby providing sufficient time for the clinicians to respond with treatment in Intensive Care Units (ICU). Thus, this proposed technique can thus serve as a valuable tool for increasing the survival rate of many cardiac patients. [ABSTRACT FROM AUTHOR]

The pairing of dynamical systems theory and complexity science brings novel concepts and methods to the study of infant motor development. Accordingly, this longitudinal case study presents a new approach to characterizing the dynamics of infant limb and vocalization behaviors. A single infant’s vocalizations and limb movements were recorded from 51-days to 305-days of age. On each recording day, accelerometers were placed on all four of the infant’s limbs and an audio recorder was worn on the child’s chest. Using nonlinear time series analysis methods, such as recurrence quantification analysis and Allan factor, we quantified changes in the stability and multiscale properties of the infant’s behaviors across age as well as how these dynamics relate across modalities and effectors. We observed that particular changes in these dynamics preceded or coincided with the onset of various developmental milestones. For example, the largest changes in vocalization dynamics preceded the onset of canonical babbling. The results show that nonlinear analyses can help to understand the functional co-development of different aspects of infant behavior.

The pairing of dynamical systems theory and complexity science brings novel concepts and methods to the study of infant motor development. Accordingly, this longitudinal case study presents a new approach to characterizing the dynamics of infant limb and vocalization behaviors. A single infant's vocalizations and limb movements were recorded from 51-days to 305-days of age. On each recording day, accelerometers were placed on all four of the infant's limbs and an audio recorder was worn on the child's chest. Using nonlinear time series analysis methods, such as recurrence quantification analysis and Allan factor, we quantified changes in the stability and multiscale properties of the infant's behaviors across age as well as how these dynamics relate across modalities and effectors. We observed that particular changes in these dynamics preceded or coincided with the onset of various developmental milestones. For example, the largest changes in vocalization dynamics preceded the onset of canonical babbling. The results show that nonlinear analyses can help to understand the functional co-development of different aspects of infant behavior. [ABSTRACT FROM AUTHOR]

The time domain measures and power spectral analysis of heart rate variability (HRV) are classic conventional methods to assess the complex regulatory system between autonomic nervous system and heart rate and are most widely used. There are abundant scientific data about the prognostic significance of the conventional measurements of HRV in patients with various conditions, particularly with myocardial infarction. Some studies have suggested that some newer measures describing nonlinear dynamics of heart rate, such as fractal measures, may reaveal prognostic information beyond that obtained by the conventional measures of HRV. An ideal risk indicator could specifically predict sudden arrhythmic death as the implantable cardioverter-defibrillator (ICD) therapy can prevent such events. In postinfarction patients, numerically the highest number of sudden deaths occur in patients with better preserved left ventricular function than in those with severe left ventricular dysfunction. Recent data support the concept that HRV measurements, when analyzed several weeks after acute myocardial infarction, predict life-threatening ventricular tachyarrhythmias in patients with moderately depressed left ventricular function. However, well-designed prospective randomized studies are needed to evaluate whether the ICD therapy based on the assessment of HRV alone or with other risk indicators improves the patients´ prognosis. Several issues, such as the optimal target population, optimal timing of HRV measurements, optimal methods of HRV analysis and optimal cutpoints for different HRV parameters, need clarification before the HRV analysis can be a widespread clinical tool in risk stratification.

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The current financial and economic crisis reignited the interest in the output-unemployment relationship. The unemployment rate in Romanian economy reacted strongly to the fall in economic activity, by more than doubling in less than a year raising, and thus raising questions on the validity of a symmetric relationship between unemployment and output. In this paper I investigate the existence of asymmetries in the Okun coefficient in Romanian economy. The asymmetric approach is justified based on both practical reasons, given the last results in the literature, as well as from a theoretic point of view. The econometric framework used is the Markov Switching approach. The data used is at monthly frequency and consists in the monthly industrial production index and in the unemployment rate between January 1991 and December 2009. The Regime switching approach allows for the testing whether the Okun relation switches between different phases. I find that there are asymmetries in the Okun coefficient. A state characterized by a weaker Okun coefficient of -0.09 was found, and a state with a stronger Okun coefficient of -0.32. After 1999, the switches are associated with the two phases of the business cycles, with a weaker Okun coefficient during the expansion and a stronger Okun relationship during the recession, as in the literature, underlining that the Romanian economy became a full market economy. The findings here reconcile the previous findings based on symmetric approaches. Some policy implications as well as the peril of a jobless recovery are discussed. This paper thus contributes to the ongoing refinement and understanding of the output - unemployment relationship [ABSTRACT FROM AUTHOR]

Highlights: • We analyzed fetal heart rate of normal and acidemic fetuses. • We used conventional and nonlinear features for the signal analysis. • Addition of nonlinear features improves accuracy of classification. • The best nonlinear features are: Lempel Ziv complexity and Sample entropy. • Combination of conventional and nonlinear features provides the best accuracy. Abstract: Fetal heart rate (FHR) is used to evaluate fetal well-being and enables clinicians to detect ongoing hypoxia during delivery. Routine clinical evaluation of intrapartum FHR is based on macroscopic morphological features visible to the naked eye. In this paper we evaluated conventional features and compared them to the nonlinear ones in the task of intrapartum FHR classification. The experiments were performed using a database of 217 FHR records with objective annotations, i.e. pH measurement. We have proven that the addition of nonlinear features improves accuracy of classification. The best classification results were achieved using a combination of conventional and nonlinear features with sensitivity of 73.4%, specificity of 76.3%, and F-measure of 71.9%. The best selected nonlinear features were: Lempel Ziv complexity, Sample entropy, and fractal dimension estimated by Higuchi method. Since the results of automatic signal evaluation are easily reproducible, the process of FHR evaluation can become more objective and may enable clinicians to focus on additional non-cardiotocography parameters influencing the fetus during delivery. [Copyright &y& Elsevier]

Deep brain stimulation (DBS) is effective in reducing motor symptoms in Parkinson’s disease (PD). However, objective methods for quantifying its efficacy are lacking. We present a principal component (PC)-based tracking method for quantifying the effects of DBS in PD by using electromyography (EMG) and acceleration measurements. Ten parameters capturing PD characteristic signal features were initially extracted from isometric EMG and acceleration recordings. Using a PC approach, the original parameters were transformed into a smaller number of PCs. Finally, the effects of DBS were quantified by examining the PCs in a low-dimensional feature space. The EMG and acceleration data from 13 PD patients with DBS ON and OFF, and 13 healthy age-matched controls were used for analysis. Clinical evaluation of patients showed that their motor symptoms were effectively reduced with DBS. The analysis results showed that the signal characteristics of 12 patients were more similar to those of the healthy controls with DBS ON than with DBS OFF. These observations indicate that the PC-based tracking method can be used to objectively quantify the effects of DBS on the neuromuscular function of PD patients. Further studies are suggested to estimate the clinical sensitivity of the method to different types of PD. [ABSTRACT FROM PUBLISHER]