Detrended Fluctuation Analysis Research Articles

Determination of local fractal dimension of the catalyst support MgAl2O4 surface

In this paper, a procedure for estimating local fractal dimensionality values for greyscale images was developed. This procedure was tested on synthesised fractal and multifractal Brownian surfaces. The use of the method of detrended fluctuation analysis to determine the Hurst index for samples with a size not exceeding 2000 points was analysed. The optimal values of the sample size and the scale parameter have been obtained, allowing the Hurst index to be calculated with an error of less than 20%. The procedure was also tested on greyscale images, which are 8-bit representations of a synthesised fractal surface. The peculiarities of the application of the procedure for calculating the local fractal dimension for images obtained by scanning electron microscopy have been analysed. The values of the local fractal dimension of the oxide support MgAl2O4 are calculated using SEM images at different magnifications. It has been shown that, taking into account the fractal dimensionality calculation, different morphologies of the particle surface are observed at different scales of image magnification. At the highest magnification, the particle surface is found to consist of embedded smooth micron particles. The surface morphology of the MgAl2O4 particle depends on the micro- and mesopores between the embedded particles, this surface can be characterised by a fractal surface with FD2 = 2.3–2.4.

Multifractal and chaotic characteristics of geomagnetic inter-storm interval over eight solar cycles

The structure and characteristics of geomagnetic inter-storm intervals, the sequence of time differences between two successive storm events, can provide information about the origin and drivers of the storms compared to analyzing the entire data set. In this study, the inter-storm event was investigated using the Kp index over 8 solar cycles (17–24). Three nonlinear approaches were considered: burstiness, multifractal detrended fluctuation analysis, and chaos analysis. The burstiness analysis reveals that the inter-storm intervals are heterogeneous events with irregular time intervals. The multifractal strength of inter-storm intervals ranges from 0.23 to 0.65 for each of the solar cycles, compared to 0.38 for the entire period. Long-range correlations largely drove the multifractality across most solar cycles. Chaos analysis confirmed the low predictability of inter-storm intervals across each solar cycle. The results will be relevant to the prediction and understanding of drivers of storm events across different solar cycles.

Monitoring autonomic responses in Parkinson’s disease individuals: non-linear and chaotic global metrics of heart rate variability

Aim To examine and compare the autonomic responses, as assessed through the non-linear and chaotic global metrics of heart rate variability in two groups: the Parkinson’s Disease Group (PDG) and the Control Group (CG), both at rest and during an active tilt test. Methods The study encompassed 46 participants (PDG: n = 23; 73.73 ± 7.28 years old; CG: n = 23; 70.17 ± 8.20 years old). Initial data collection involved the acquisition of participant’s characteristics. The autonomic modulation was estimated both at rest and during the active tilt test. For this assessment, we computed non-linear indices derived from five entropies (Approximate, Sample, Shannon, Renyi, Tsallis), Detrended Fluctuation Analysis and the seven chaotic global metrics (hsCFP1-hsCFP7). Results At rest, the PDG exhibited lower values of hsCFP3 (0.818 ± 0.116 vs. 0.904 ± 0.065; p < 0.05) and Sample Entropy (0.720 ± 0.149 vs. 0.799 ± 0.171; p < 0.05). During the test, the PDG demonstrated lower values of ApEn, while the CG presented lower values of SampEn, hsCFP1, hsCFP3, hsCFP7, and higher values of hsCFP5. An interaction was observed, indicating that hsCFP1 and hsCFP3 exhibit differential behavior for the CG and PDG in response to the test. Conclusion subjects with PD exhibited reduced complexity of the RR interval series at rest, and a diminished autonomic response to the active tilt test when compared with the CG. The test, together with non-linear indices, may serve for assessing the Autonomic Nervous System in individuals with PD in a clinical setting. The interpretation of these data should be approached with caution, given the possible influences of pharmacotherapies and the inclusion of diabetic participants.

Segmented multifractal detrended fluctuation analysis for assessing inefficiency in North African stock markets

This study employs segmented multifractal analysis to evaluate the efficiency of key financial markets in North Africa. The proposed method, an adapted version of Multifractal Detrended Fluctuation Analysis (MF-DFA), integrates a wavelet-based change-point detection to identify and separate the two most dynamically changing phases. Subsequent multifractal measurements are then conducted for each of these identified intervals. Focusing on three equity indices—the Egyptian Exchange Index (EGX30), Moroccan All Shares Index (MASI), and Tunisian Stock Index (Tunindex)–that collectively represent the North African financial and economic landscape, empirical results reveal significant asymmetric multifractality, especially notable in the two Maghreban indices. These findings prompt inquiries into the influence of major events on financial market efficiency. Segmented multifractal analysis introduces a novel approach to explore the dynamics and resilience of these sectors, contributing to a more profound understanding of their complex behaviors and responses to various stimuli.

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques.

The Complexity of Center of Pressure Positions During Quiet Stance and Its Relationship to Cognition, Aging, and Falls.

In a secondary analysis of data taken from a publicly available database, we examined cognitive performance, postural sway, and relations between them for 4 groups: younger and older individuals with versus without a recent history of falls. Our objective was to compare linear versus nonlinear measures of postural activity as post hoc predictors of cognitive performance and falling. We evaluated standing body sway in 147 participants (18-85-years old) over 60 seconds, separately with eyes-open and with eyes-closed. We evaluated cognitive performance using portions of the Trail Making Test. We evaluated postural activity in terms of standard deviation, velocity, and amplitude of the Center of Pressure (CoP). Separately, we used detrended fluctuation analysis (DFA) to examine the complexity of CoP displacements. Using analysis of variance, we conducted separate analyses of cognitive performance and postural activity comparing Younger and Older Adults and Non-fallers and Fallers, taking into account Vision (eyes-closed vs open) and the direction of postural movements (Anteroposterior vs Mediolateral) while also controlling for participants' characteristics. We used moderation analyses to evaluate whether relationships between Trail Making Test scores and the linear and nonlinear outcomes were moderated by Age group or Fall status. For postural activity, only DFA differed between Non-fallers and Fallers. Older adults exhibited increased complexity associated with better processing speed function, while fallers show an opposite association, relying on processing speed to increase postural rigidity instead of facilitating adaptive control of balance. We conclude that DFA can provide information regarding postural activity and cognitive performance that cannot be obtained from more traditional, linear measures of postural activity and that DFA may be a valuable tool for assessing fall risk.

Detection of long-range correlation causing multifractality in H time series of geomagnetic field over the Northern Hemisphere during quiet geomagnetic conditions

The generation of geomagnetic time series frequently involves intricate spatio-temporal dynamics, wherein nonlinearity and scaling emerge as pivotal mechanisms. The present work investigates the fractal properties of geomagnetic field, and its sources during quiet geomagnetic conditions, by analyzing the horizontal component data of 1 min resolution (H time series) from different latitude regions. Analysis using Auto Correlation Function (ACF), Power spectrum analysis (PSA), Rescaled range analysis (R/S) and Detrended Fluctuation Analysis (DFA) gave indications of the presence of long-range correlations, fractal properties and scaling behaviour of quiet period H time series data. The strong q dependence of generalized Hurst exponent h(q) and scaling exponent Ʈ(q), obtained from the Multifractal Detrended Fluctuation Analysis (MFDFA) method, confirmed the multifractal behaviour of quiet period H time series. The multifractal source analysis of the quiet period H time series with its shuffled and surrogate time series data revealed that, the multifractal characteristics are caused almost entirely by the long-range correlations between small and large fluctuations. The analysis of the latitudinal variations in the multifractal characteristics is also performed as a part of the work, by considering H time series belonging to various latitudes under almost same longitude. The observed decrease in the generalized Hurst exponent values with increase in latitude indicates the change in the multifractal characteristics of the time series with latitude. Also the work revealed that, the H time series from high latitude region shows anti-persistent behaviour, even when the H time series from observatories located at the low and mid latitude regions exhibiting persistent behaviour. The change in the persistence, anti-persistence behaviour can be related to the strength of Earth’s magnetic field.

Harnessing Multistability: A Novel Approach to Optical Logic Gate Construction Using Erbium-Doped Fiber Lasers

We present an innovative method harnessing multistability within a diode-pumped erbium-doped fiber laser to construct logic gates. Our approach involves manipulating the intensity of external noise to regulate the probability of transitioning among four concurrent attractors. In this manner, we facilitate the realization of OR, AND, NOR, and NAND logic operations, aligning with the coexisting period-1, period-3, period-4, and period-5 orbits. Employing detrended fluctuation analysis, we establish equilibrium in the probability distributions of these states. The obtained results denote a substantial advancement in the field of optical logic gate development, representing a pivotal stride toward the seamless integration of an all-optical logic gate within laser oscillator-based systems.

Fractal based feature extraction technique for classifying EEG signal for color visualization tasks

Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy (SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features.

Surge prediction in a gas-liquid mixed-flow pump based on multifractal detrended fluctuation analysis

Surge causes a sudden drop in pressure increase and possibly results in severe vibration in a multiphase pump once inlet gas volume fraction (IGVF) exceeds a critical value. In this study, a synchronous test system including a high-speed camera, a laser Doppler vibrometer and high-frequency pressure fluctuation sensors is constructed to measure shaft vibration and pressure fluctuation in a gas–liquid mixed-flow pump at different IGVFs. The multifractal characteristics of the collected signals obtained by using the multifractal detrended fluctuation analysis method have been analysed and compared in detail. Results show that pressure surge occurs once the flow pattern transforms from bubbly flow to gas pocket flow in the pump. The multifractal characteristics of the vibration and pressure fluctuations depend strongly on the IGVF. Before the surge, the multifractal strength of vibration and pressure fluctuation increase with the increment of IGVF. After the surge, they decrease rapidly as the bubble coalescence into a mass of gas pockets. Furthermore, the local fluctuation of the vibration is insensitive to the variation of the IGVF and the multifractal spectrum has a right tail at each IGVF. The local fluctuation of the pressure fluctuation become severe with the coalescence of bubbles and the multifractal spectrum has a left tail after the surge. The incipient surge state can be effectively predicted by characteristics parameters extracted from the multifractal spectrum of the shaft vibration and the pressure fluctuation in the pump. This study provides an effective method for surge state prediction in multiphase pumps.

Analysis and Warning Prediction of Tunnel Deformation Based on Multifractal Theory

To better analyze the fluctuation characteristics and development law of tunnel deformation data, multifractal theory is applied to tunnel deformation analysis. That is, the multifractal detrended fluctuation analysis (MF-DFA) model is first utilized to carry out the multifractal characterization of tunnel deformation data. Further, Mann–Kendall (M–K) analysis is utilized to construct the dual criterion (∆α indicator criterion and ∆f(α) indicator criterion) for the tunnel deformation early warning study. In addition, the particle swarm optimization long-short-term memory (PSO-LSTM) prediction model is used for predicting tunnel settlement. The results show that, in reference to the tunnel warning level criteria and based on the Z-value results of the indicator criterion, the warning level of all four sections is class II. At the same time, through the analysis of tunnel settlement predictions, the PSO-LSTM model has a better prediction effect and stability for tunnel settlement. The predicted results show a slow increase in tunnel settlement over the next 5 days. Finally, the tunnel warning level and the predicted results of tunnel settlement are analyzed in a comprehensive manner. The deformation will increase slowly in the future. Therefore, monitoring and measurement should be strengthened, and disaster preparedness plans should be prepared.

Blood pressure fragmentation as a new measure of blood pressure variability: association with predictors of cardiac surgery outcomes.

Background: Fluctuations in beat-to-beat blood pressure variability (BPV) encode untapped information of clinical utility. A need exists for developing new methods to quantify the dynamical properties of these fluctuations beyond their mean and variance. Objectives: Introduction of a new beat-to-beat BPV measure, termed blood pressure fragmentation (BPF), and testing of whether increased preoperative BPF is associated with (i) older age; (ii) higher cardiac surgical risk, assessed using the Society of Thoracic Surgeons' (STS) Risk of Morbidity and Mortality index and the European System for Cardiac Operative Risk Evaluation Score (EuroSCORE II); and (iii) longer ICU length of stay (LOS) following cardiac surgery. The secondary objective was to use standard BPV measures, specifically, mean, SD, coefficient of variation (CV), average real variability (ARV), as well a short-term scaling index, the detrended fluctuation analysis (DFA) ⍺1 exponent, in the same type of analyses to compare the results with those obtained using BPF. Methods: Consecutive sample of 497 adult patients (72% male; age, median [inter-quartile range]: 67 [59-75] years) undergoing cardiac surgery with cardiopulmonary bypass. Fragmentation, standard BPV and DFA ⍺1 measures were derived from preoperative systolic blood pressure (SBP) time series obtained from radial artery recordings. Results: Increased preoperative systolic BPF was associated with older age, higher STS Risk of Morbidity and Mortality and EuroSCORE II values, and longer ICU LOS in all models. Specifically, a one-SD increase in systolic BPF (9%) was associated with a 26% (13%-40%) higher likelihood of longer ICU LOS (>2 days). Among the other measures, only ARV and DFA ⍺1 tended to be associated with longer ICU LOS. However, the associations did not reach significance in the most adjusted models. Conclusion: Preoperative BPF was significantly associated with preoperative predictors of cardiac surgical outcomes as well as with ICU LOS. Our findings encourage future studies of preoperative BPF for assessment of health status and risk stratification of surgical and non-surgical patients.

Multifractal detrended fluctuation analysis of soil radon in the Kachchh Region of Gujarat, India: A case study of earthquake precursors

In this paper, the scaling properties of earthquake-induced soil radon emissions in the Kachchh region of Gujarat, India, were demonstrated through the application of multifractal detrended fluctuation analysis (MFDFA). The analysis considered soil radon data recorded at four stations, namely Rampar, Rapar, Pragpar and Paddhar, over a period of one year (January 1, 2020 to December 31, 2020). At these stations, soil radon along with soil pressure and temperature are recorded continuously at 10-minute intervals. In the first step, periodic oscillations such as diurnal and semi-diurnal periodicities of the raw soil radon data were removed by applying empirical mode decomposition (EMD). The aperiodic soil radon at all measuring stations was used as input for the MFDFA analysis. In order to obtain the exact source of the multifractality, the MFDFA is also applied to the shuffled and surrogate data of the aperiodic soil radon time series at all measuring stations. It can be observed that the fluctuation function (Log (Fq(s))) for all measuring stations increases linearly with increasing scale value(s). In contrast, the generalized Hurst exponent (H(q)) of the shuffled radon values is about 0.5, the H(q) value of the surrogate and observed radon series is greater than 0.5 at all measuring stations. The scaling exponent τqof shuffled Radon shows an approximately linear trend, while surrogate and soil observed radon show a non-linear trend. At all stations, the shuffled series shows a narrower spectrum compared to the surrogate and observed soil radon time series. This shows that observed soil radon has a long-range correlation. Three local earthquakes of June 14, 2020 (M=5.3), August 28, 2020 (M=4.1) and November 1, 2020 (M=4.1) near monitoring stations are considered for analysis in this study. The multifractal spectrum of soil radon is also examined during the seismically quiet and disturbed period. A broader spectrum is obtained during the disturbed period compared to the quiet period. This may be due to heterogeneity characterized by anomalous emission of soil radon gas before the occurrence of earthquakes during the monitoring period.

Correlation of detrended fluctuation analysis alpha 1 with blood lactate concentration in field standardised exercise tests in sport horses

Abstract Methods to determine exercise intensity thresholds, such as the aerobic and anaerobic threshold, that separate different exercise intensities in exercising horses in the field setting or on the treadmill often require equipment, standardised conditions and/or blood sampling. Detrended fluctuation analysis (DFA) is a heart rate variability variable proposed as a non-invasive tool for exercise intensity monitoring and with a potential association with exercise intensity thresholds and fatigue. The objectives of this study were to (1) describe the first component of DFA (DFA-α1) during standardised field incremental exercise tests in sport horses in active Eventing competition and in sport horses ridden without the goal of participating in competitions, and (2) to determine if DFA-α1 is associated with blood lactate concentration. Null hypotheses of the study were that (1) DFA-α1 is not correlated with blood lactate concentration measurements and (2) the correlation between DFA-α1 and blood lactate concentration is not stronger than the one between blood lactate concentration and heart rate or blood lactate concentration and speed. An in vivo observational study was performed analysing exercising electrocardiograms obtained in 59 standardised exercise tests in the field. DFA-α1 was significantly and moderately correlated with blood lactate concentration (, ). A strong correlation was observed between blood lactate concentration and speed (, ), as well as between blood lactate concentration and heart rate (, ). DFA-α1 correlation with blood lactate concentration was moderate but weaker than the correlation of blood lactate concentration and heart rate or blood lactate concentration and speed. DFA-α1 was not a useful surrogate of blood lactate concentration in field incremental standardised tests used for sport horses in this setting.

Lower Limb Cross-Over Effects on Postural Control: Impact of Proximal and Distal Muscle Fatigue

The purpose of this study was to examine the cross-over influence of lower limb fatigue on postural control. Using two experiments, cross-over fatigue was investigated using a proximal (Experiment 1—single-leg squat) and distal (Experiment 2—calf raise) muscle group. In Experiment 1, 15 healthy young participants underwent a single-leg standing task on both the right and left leg, with variations of having eyes open or closed and on stable or unstable surfaces, performing each task for 30 s. For Experiment 2, 20 individuals performed single-leg balance testing for the right and left leg and stable and unstable surfaces. Center-of-pressure data were collected during the balance tasks and were analyzed with linear (standard deviation) and nonlinear (detrended fluctuation analysis) metrics. The results lacked significant differences (p &gt; 0.05) for cross-over fatigue effects on the non-exercised limb, which exhibited similar levels of postural sway between the pre- and post-fatigue balance tests. These tasks may have lacked an appropriate level of duration or intensity to cause a significant effect of central fatigue on the nervous system. The findings underscore the need to better understand how a specific fatiguing task during unilateral rehabilitation may alter postural control.

General spectral characteristics of human activity and its inherent scale-free fluctuations

The scale-free nature of daily human activity has been observed in different aspects; however, the description of its spectral characteristics is incomplete. General findings are complicated by the fact that—although actigraphy is commonly used in many research areas—the activity calculation methods are not standardized; therefore, activity signals can be different. The presence of 1/f noise in activity or acceleration signals was mostly analysed for short time windows, and the complete spectral characteristic has only been examined in the case of certain types of them. To explore the general spectral nature of human activity in greater detail, we have performed Power Spectral Density (PSD) based examination and Detrended Fluctuation Analysis (DFA) on several-day-long, triaxial actigraphic acceleration signals of 42 healthy, free-living individuals. We generated different types of activity signals from these, using different acceleration preprocessing techniques and activity metrics. We revealed that the spectra of different types of activity signals generally follow a universal characteristic including 1/f noise over frequencies above the circadian rhythmicity. Moreover, we discovered that the PSD of the raw acceleration signal has the same characteristic. Our findings prove that the spectral scale-free nature is generally inherent to the motor activity of healthy, free-living humans, and is not limited to any particular activity calculation method.

Identifying the Occurrence Time of the Destructive Kahramanmaraş-Gazientep Earthquake of Magnitude M7.8 in Turkey on 6 February 2023

Here, we employ natural time analysis of seismicity together with non-extensive statistical mechanics aiming at shortening the occurrence time window of the Kahramanmaraş-Gazientep M7.8 earthquake. The results obtained are in the positive direction pointing to the fact that after 3 February 2023 at 11:05:58 UTC, a strong earthquake was imminent. Natural time analysis also reveals a minimum fluctuation of the order parameter of seismicity almost three and a half months before the M7.8 earthquake, pointing to the initiation of seismic electrical activity. Moreover, before this earthquake occurrence, the detrended fluctuation analysis of the earthquake magnitude time-series reveals random behavior. Finally, when applying earthquake nowcasting, we find average earthquake potential score values which are compatible with those previously observed before strong (M≥7.1) earthquakes. The results obtained may improve our understanding of the physics of crustal phenomena that lead to strong earthquakes.

Analysis of the structural complexity of Crab Nebula observed at radio frequency using a multifractal approach

The Crab Nebula is an astrophysical system that exhibits complex morphological patterns at different observing frequencies. We carry out a systematic investigation of the structural complexity of the nebula using publicly available imaging data at radio frequency. For the analysis, we use the well-known multifractal detrended fluctuation analysis in two dimensions. We find that radio data exhibit long-range correlations, as expected from the underlying physics of the supernova explosion and evolution. The correlations follow a power-law scaling with length scales. The structural complexity is found to be multifractal in nature, as evidenced by the dependence of the generalized Hurst exponent on the order of the moments of the detrended fluctuation function. By repeating the analysis on shuffled data, we further probe the origin of the multifractality in the radio imaging data. For the radio data, we find that the probability density function is close to a Gaussian form. Hence, the multifractal behavior is due to the differing nature of long-range correlations of the large and small detrended fluctuation field values. We investigate the multifractal parameters across different partitions of the radio image and find that the structures across the image are highly heterogeneous, making the Crab Nebula a structurally complex astrophysical system. Our analysis thus provides a fresh perspective on the morphology of the Crab Nebula from a complexity science viewpoint.

Unraveling the Seismic Signal Anomaly at Mount Rinjani Station: In-Depth Exploration with the Detrended Fluctuation (DFA) Analysis Method in Connection with the 2018 Big Earthquake on Lombok Island, West Nusa Tenggara

We analyzed the seismic signals of the Mount Rinjani station to identify possible seismic anomalies before a series of major earthquakes on Lombok Island in 2018. We observe anomalies before earthquake events Mw &gt;= 5.90. This research applies the detrended fluctuation analysis (DFA) method to investigate possible earthquake precursors associated with the analyzed earthquake. The results showed a relationship between scaling seismic signals (α) that exceeds the threshold of α values during the analysis period, and there is a deviation of the root mean square fluctuations in the corresponding scaling that should be rising but constant. The value of α with constant root mean square fluctuations occurs one to three days before the preliminary earthquake Mw 6.40 (28/07/2018) UTC and eight to 10 days before the main earthquake Mw 6.90 (05/08/2018). The Mw 6.90 earthquake had the most magnitude and could significantly contribute to the appearance of seismic anomalies.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel–Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.