Permutation Entropy Research Articles

A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer

In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power.

Eigen-entropy based time series signatures to support multivariate time series classification

Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset. To manage dataset’s dynamic nature, we employ preprocessing with dense multi scale entropy. Consequently, the proposed framework, Eigen-entropy-based Time Series Signatures, captures correlations among multivariate time series without losing its temporal and dynamic aspects. The efficacy of our algorithm is assessed using six binary datasets sourced from the University of East Anglia, in addition to a publicly available gait dataset and an institutional sepsis dataset from the Mayo Clinic. We use recall as the evaluation metric to compare our approach against baseline algorithms, including dependent dynamic time warping with 1 nearest neighbor and multivariate multi-scale permutation entropy. Our method demonstrates superior performance in terms of recall for seven out of the eight datasets.

Fingerprint liveness detection based on contourlet, various entropy algorithms and multiobjective genetic algorithm-based ensemble classifier

ABSTRACT Biometric devices have been indicated to be vulnerable to various threats. In the present study, a novel liveness identification technique is introduced according to the noise analysis of fingerprint images. It has been shown that the textural patterns of real fingertips are different from those of spoof fingertips. In this work, Real images were successfully distinguished from fake ones with an accuracy of approximately 99.50%. The database consists of 200 fingerprints, half of which is the real database (i.e. 100 fingerprints), and the other half is the fake database. Textural features are analyzed using pyramidal mean and PDFB analysis techniques that decompose images into directional sub-bands at several scales. The Shannon entropy, approximate entropy, sample entropy, permutation entropy, fuzzy entropy, multiscale entropy, Log energy entropy, and Sure entropy were estimated for all these sub-bands and were arranged into an array. In the classification stage, an ensemble classifier based on a multiobjective genetic algorithm was employed. The results obtained revealed that the suggested technique could successfully differentiate live fingertips from fake ones. The best accuracy was obtained for the proposed ensemble technique, with an average accuracy of 99.52% compared to other classifiers. After the ensemble method, quadratic SVM achieved the best classification performance with a mean accuracy of 97.34% in discriminating real live fingerprints from fake fingerprints. In comparison to other prior techniques, the suggested method produced good classification performance in real-time fingerprint identification while remaining straightforward. Therefore, the system can be a good option for use in real biometric systems.

Quantitative Fault Diagnosis of Planetary Gearboxes Based on Improved Symbolic Dynamic Entropy

To realize a quantitative diagnosis of faults in the planetary gearboxes of wind turbines by processing the complex frequency signals of the planetary gear boxes and avoiding the aliasing problem of the resulting frequencies, this paper proposes a diagnosis method based on improved variational mode decomposition (IVMD) and average multi-scale double symbolic dynamic entropy (AMDSDE). Moreover, an IVMD algorithm based on multi-scale permutation entropy is introduced to reduce noise interference and realize signal demodulation. Considering the effects of complex transfer paths and the correlation between current and adjacent state modes, AMDSDE is proposed. Each fault size is obtained based on the entropy curve, and the AMDSDE of unknown faults is calculated. To verify the accuracy of the proposed method, simulations and experimental signals are processed. The quantitative diagnosis of the planetary gearboxes of wind turbines is realized, providing a reliable basis for evaluating the health status of planetary gearboxes.

Complexity analysis of polarization components of chaotic fiber laser

We experimentally study the complexity of polarization components of chaotic fiber lasers by permutation entropy, skewness and kurtosis. The experimental setup for complexity analysis is composed of the chaotic fiber laser, polarization beam splitter and data-processing systems. The effects of the different polarization states and pump currents on complexity, skewness and kurtosis are experimentally discussed in detail. The result shows chaotic laser with high-complexity can be obtained by analyzing the polarization component of chaotic laser with different polarization. The random characteristics of polarization component of the chaotic fiber laser are analyzed by the autocorrelation function. The spatial resolution is analyzed by combining characteristics of the autocorrelation curve of chaotic fiber laser. The study of the polarization component dynamic characteristics of the chaotic fiber laser is beneficial to obtain chaotic signal with high-complexity and further effectively improve the detection precision of a sensing system based on the chaotic fiber laser.

Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches

Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

Refined composite multiscale slope entropy and its application in rolling bearing fault diagnosis

Rolling bearing is the key component of rotating machinery, and its vibration signal usually exhibits nonlinear and nonstationary characteristics when failure occurs. Multiscale permutation entropy (MPE) is an effective nonlinear dynamics analysis tool, which has been successfully applied to rolling bearing fault diagnosis in recent years. However, MPE ignores the deep amplitude information when measuring the complexity of the time series and the original multiscale coarse-graining is insufficient, which requires further research and improvement. In order to protect the integrity of information structure, a novel nonlinear dynamic analysis method termed refined composite multiscale slope entropy (RCMSlE) is proposed in this paper, which introduced the concept of refined composite to further boost the performance of MPE in nonlinear dynamical complexity analysis. Furthermore, RCMSlE utilizes a novel symbolic representation that takes full account of mode and amplitude information, which overcomes the weaknesses in describing the complexity and regularity of bearing signals. Based on this, a GWO-SVM multi-classifier is introduced to fulfill mode recognition, and then a new intelligent fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. The experimental results show that the proposed method can not only accurately identify different fault types and degrees of rolling bearing, but also has a short computation time and better performance than other comparative methods.

Significant improvement of chaotic synchronization quality of two polarization components emitted by an optically pumped spin- VCSEL using wavelet decomposition in photonic reservoir computers

Based on Daubechies (db) wavelet decomposition method, we propose to use the double photonic reservoir computers to enhance the chaotic synchronization quality of the two polarization components (X-PC and Y-PC) emitted by an optically pumped spin-VCSEL (the drive-VCSEL). The chaotic time series of two polarization components (PCs) from the drive-VCSEL, which serve as predictive targets, are decomposed into high-frequency and low-frequency components (multi-scale time series). This decomposition is performed using the first-type of Daubechies wavelet (db1-wavelet) decomposition. Our findings demonstrate that contrasted with a direct-prediction approach , the predictive errors in various intricate chaotic dynamics of the targets, outlined by permutation entropy, can be substantially minimized through db1-wavelet decomposition. As the layer number of the db1-wavelet decomposition increases, it noteworthily reduces the error in their predictive outcomes. Furthermore, under different parameter spaces, db1-wavelet decomposition considerably reduces the prediction errors for the targets and greatly enhances the quality of chaotic synchronization between pairs of X-PCs or Y-PCs, which are from the drive-VCSEL and reservoir-VCSEL. The further increase of the layer number of the db1-wavelet decomposition can trigger a more significant reduction in the prediction errors and improves the quality of chaotic synchronization. Interestingly, the use of different types of Daubechies wavelet decompositions does not substantially impact the prediction errors or the quality of chaotic synchronization. Our proposed model effectively addresses the issue of multi-scale optical time series prediction. The findings of this study can potentially be applied in enhancing the quality of optical chaotic synchronization in a photonic reservoir computing system.

Detection of congestive heart failure based on Gramian angular field and two-dimensional symbolic phase permutation entropy

Congestive heart failure (CHF) is a serious threat to human health. Electrocardiogram (ECG) signals have been proven to be useful in the detection of CHF. However, the low amplitude and short duration of the ECG signals, as well as the superimposed noise during the real-time acquisition of the signal, seriously affect the CHF detection. To improve the detection rate of CHF, this paper proposes a congestive heart failure detection method based on Gramian angular field (GAF) and two-dimensional symbolic phase permutation entropy (SPPE2D). The significant advantage of this method is that it reduces the sensitivity to noise, and good performance can be obtained without denoising using raw ECG signals. We segment the original ECG signals into 2 s non-overlapping segments and convert them into images using the GAF method. Then, the SPPE2D algorithm is proposed to measure the complexity between normal sinus rhythm (NSR) and CHF, and analyze the anti-noise performance of the algorithm. Finally, the SPPE2D features of GAF images are computed and input into a support vector machine (SVM) for CHF detection. Classification accuracy on the Massachusetts Institute of Technology − Beth Israel Hospital Normal Sinus Rhythm Database and Beth Israel Deaconess Medical Center Congestive Heart Failure Database is 99.59%, sensitivity is 99.42%, specificity is 99.80%, and F1-score is 99.62%. The accuracy of detecting CHF reach more than 97.75% in the other five CHF databases. The experimental results show that the method based on GAF and SPPE2D can effectively detect CHF by images of ECG signals and has good robustness. CHF can be detected using the 2 s sample lengths of ECG signals recording with high sensitivity, giving clinicians ample time to treat patients with CHF.

MHTFPE2D: two-dimensional multi-scale hierarchical time–frequency permutation entropy for complexity measurement

MHTFPE2D: two-dimensional multi-scale hierarchical time–frequency permutation entropy for complexity measurement

Remaining useful life prediction of rolling bearing via composite multiscale permutation entropy and Elman neural network

Accurate prediction of remaining useful life (RUL) is very important for the maintenance of mechanical equipment. With the latest development in sensor technology and artificial intelligence, the data-driven prediction method of mechanical equipment RUL has been widely concerned. However, in the past, the research on data mining was not sufficient, and the method of extracting degradation features was single. In order to solve this problem, this paper proposes a new method to extract the degradation features of bearings. This method creatively combines image with entropy methods and uses the Symmetrized Dot Pattern (SDP) method and Composite Multiscale Permutation Entropy (CMPE) to extract degradation features. Through five evaluation indexes, the degradation feature extracted by the SDP-CMPE method is compared with five common degradation features in time domain. Finally, a simple Elman neural network is used to predict RUL. Through the case study of bearing data set, it is verified that the degradation features extracted by the SDP-CMPE method are superior in prediction accuracy and conservatism.

A multi-factor combined traffic flow prediction model with secondary decomposition and improved entropy weight method

Traffic flow (TF) prediction is vital in traffic systems. Aiming at the effect of noise on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an improved CEEMDAN based on amplitude-aware permutation entropy (AAPE), named ICEEMDAN, is proposed. Aiming at the parameter selection problems on variational mode decomposition (VMD), VMD improved by pelican optimization algorithm (POA) is proposed, called PVMD. Aiming at the parameter selection problems of bidirectional long short-term memory (BiLSTM) and kernel extreme learning machine (KELM), BiLSTM improved by sea-horse optimizer (SHO), named SHOBiLSTM, and KELM improved by nutcracker optimizer algorithm (NOA), named NOAKELM are proposed, respectively. Since the chaotic characteristics of TF, multi-factor combined TF prediction model with ICEEMDAN, PVMD, SHOBiLSTM, NOAKELM, improved entropy weight method is proposed. First, use ICEEMDAN to decompose TF and get intrinsic mode functions (IMFs). Second, refined composite multiscale dispersion entropy (RCMDE) is used to divide IMFs into the fuzzy and detail components, fuzzy components are secondarily decomposed by PVMD, IMFs and factors are multi-factor predicted by SHOBiLSTM and NOAKELM. Finally, improved entropy weight method is proposed to weight SHOBiLSTM and NOAKELM results. TF of the UK highway proves the superiority. Experiments indicate that the proposed model is better than other nine models with RMSE, MAE, MAPE and R2 of 29.5868, 22.0431, 0.0701 and 0.9947 for TF I. The outcomes suggest this proposed model precedes nine models at 99% confidence level, providing data basis and theoretical for travel plans and traffic system.

Tsallis Entropy-Based Complexity-IPE Casualty Plane: A Novel Method for Complex Time Series Analysis.

Due to its capacity to unveil the dynamic characteristics of time series data, entropy has attracted growing interest. However, traditional entropy feature extraction methods, such as permutation entropy, fall short in concurrently considering both the absolute amplitude information of signals and the temporal correlation between sample points. Consequently, this limitation leads to inadequate differentiation among different time series and susceptibility to noise interference. In order to augment the discriminative power and noise robustness of entropy features in time series analysis, this paper introduces a novel method called Tsallis entropy-based complexity-improved permutation entropy casualty plane (TC-IPE-CP). TC-IPE-CP adopts a novel symbolization approach that preserves both absolute amplitude information and inter-point correlations within sequences, thereby enhancing feature separability and noise resilience. Additionally, by incorporating Tsallis entropy and weighting the probability distribution with parameter q, it integrates with statistical complexity to establish a feature plane of complexity and entropy, further enriching signal features. Through the integration of multiscale algorithms, a multiscale Tsallis-improved permutation entropy algorithm is also developed. The simulation results indicate that TC-IPE-CP requires a small amount of data, exhibits strong noise resistance, and possesses high separability for signals. When applied to the analysis of heart rate signals, fault diagnosis, and underwater acoustic signal recognition, experimental findings demonstrate that TC-IPE-CP can accurately differentiate between electrocardiographic signals of elderly and young subjects, achieve precise bearing fault diagnosis, and identify four types of underwater targets. Particularly in underwater acoustic signal recognition experiments, TC-IPE-CP achieves a recognition rate of 96.67%, surpassing the well-known multi-scale dispersion entropy and multi-scale permutation entropy by 7.34% and 19.17%, respectively. This suggests that TC-IPE-CP is highly suitable for the analysis of complex time series.

Numerical investigation on nonlinear dynamics of two-state quantum dot laser subject to optical injection

Based on the cascade relaxation model of the quantum dot (QD) lasers, the nonlinear dynamics of a two-state QD laser subject to optical injection is numerically studied. The simulation results show that, for a two-state QD laser operating at the ground-state (GS) and excited-state (ES) emission simultaneously, through adjusting the injection parameters, the two emission states can display the stable (S), injection locking (IL), suppression (SP), period-one (P1), period-two (P2), multi-period (MP), and chaotic pulse (CP) states when the frequency of the injection light is close to the GS lasing frequency. Further mapping of these dynamic states in the parameter space of frequency detuning and injection strength, it is found that the complex dynamic behaviors are mainly distributed in the negative frequency detuning region. In addition, the complexity of the chaotic signal is qualified by the permutation entropy (PE) calculating, and the high complexity signal with PE value over 0.95 is obtained. Moreover, within a certain frequency detuning range, the ES emission can be effectively suppressed by increasing the injection strength.

Exploring the interplay of intrinsic fluctuation and complexity in intracellular calcium dynamics

The concentration of intracellular calcium ion (Ca2＋) exhibits complex oscillations, including bursting and chaos, as observed experimentally. These dynamics are influenced by inherent fluctuations within cells, which serve as crucial determinants in cellular decision-making processes and fate determination. In this study, we systematically explore the interplay between intrinsic fluctuation and the complexity of various dynamic states of intracellular cytosolic Ca2＋. To investigate this interplay, we employ complexity measures such as permutation entropy and statistical complexity. Using a stochastic chemical Langevin equation, we simulate the dynamics of cytosolic Ca2＋. Our findings reveal that permutation entropy and statistical complexity effectively characterize the diverse, dynamic states of cytosolic Ca2＋ and illustrate their interactions with intrinsic fluctuation. Permutation entropy analysis elucidates that the chaotic state is more sensitive to intrinsic fluctuation than the other periodic states. Furthermore, we identify distinct states of cytosolic Ca2＋ occupying specific locations within the theoretical bounds of the complexity–entropy causality plane. These locations indicate varying complexity and information content as intrinsic fluctuation varies. When adjusting the permutation order, the statistical complexity measure for the different periodic and chaotic states exhibits peaks in an intermediate range of intrinsic fluctuation values. Additionally, we identify scale-free or fractal patterns in this intermediate range, which are further corroborated by multifractal detrended fluctuation analysis. These high-complexity states likely correspond to optimal Ca2＋ dynamics with biological significance, revealing rich and complex dynamics shaped by the interplay of intrinsic fluctuation and complexity. Our investigation enhances our understanding of the intricate regulatory mechanisms governing intracellular Ca2＋ dynamics and how intrinsic fluctuation modulates the complexity of the various dynamics that play crucial roles in biological cells.

A Multiscale Hybrid Wind Power Prediction Model Based on Least Squares Support Vector Regression–Regularized Extreme Learning Machine–Multi-Head Attention–Bidirectional Gated Recurrent Unit and Data Decomposition

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), permutation entropy (PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine (RELM), multi-head attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components as follows: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high-, mid-, and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Beetle Optimizer (DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. The empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM, and MHA-BiGRU. In comparison with other benchmark models, the proposed model exhibits a reduction in Root Mean Squared Error (RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Development of a video encryption algorithm for critical areas using 2D extended Schaffer function map and neural networks

This paper proposes an encryption algorithm for crucial areas of a video based on chaos and a neural network, which SVEA (Selective Video Encryption Algorithm). The critical areas of each frame in a video are extracted by deep learning to the encryption system. A one-step encryption algorithm is used to encrypt these critical areas based on chaos, where scrambling and diffusion are simultaneously performed. A new chaotic system 2D extended Schaffer function map (2D-ESFM) is utilized in the encryption system, inspired by the Schaffer function. The system has demonstrated excellent performance through Lyapunov exponents (LEs), permutation entropy (PE), the 0-1 test, and other methods. Additionally, to resist chosen plaintext attacks, the secret key is generated by a neural network, with the critical areas of the video as inputs to the neural network. The chaotic system generates the biases and weights for the neural network. We evaluate SVEA on our dataset (Gymnastics at the Olympic Games) and public datasets. SVEA exhibits strong security characteristics compared to state-of-the-art algorithms and reduces time complexity by approximately 51.3%.

Characterizing Complex Spatiotemporal Patterns from Entropy Measures.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method for classifying spatiotemporal processes based on entropy measurements. To test and validate the method, we selected five classes of similar processes related to the evolution of random patterns: (i) white noise; (ii) red noise; (iii) weak turbulence from reaction to diffusion; (iv) hydrodynamic fully developed turbulence; and (v) plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon permutation entropy (SHp) and a new approach based on Tsallis Spectral Permutation Entropy (Sqs). Notably, our observations reveal the segregation of reaction terms in this SHp×Sqs space, a result that identifies specific sectors for each class of dynamic process, and it can be used to train machine learning models for the automatic classification of complex spatiotemporal patterns.

Generalized weighted permutation entropy analysis of SPEI index in Serbia as a proxy of corn production

AbstractIn this work we analyse yearly standardized precipitation evapotranspiration index for accumulation timescale of 6 months (SPEI‐6) for August which has been identified as a strong proxy for corn production in Serbia. By applying recently proposed method generalized weighted permutation entropy (GWPE), which provides the information about predictability of large/small fluctuations in nonstationary time series, on SPEI index calculated from high‐resolution gridded dataset for the period 1951–2022, we found that the small fluctuation sequences of consecutive 4‐year SPEI values are the most predictable (indicated by lowest entropy values). The order of large fluctuation sequences is less predictable, while the order of sequences considering all magnitudes of fluctuations are the least predictable (indicated by highest entropy values). We also analysed 4‐month SPEI‐6 sequences for May, June, July and August (during the growing season of corn) along the years under study and found that they display lower GWPE values and thus are more predictable than yearly SPEI‐6 August sequences (small fluctuations displaying lower entropy values than large fluctuations). Regarding spatial distributions, in both cases SPEI‐6 sequences show similar pattern for large fluctuations: higher predictability (lower entropy values) in northern and eastern part of Serbia and lower predictability (higher entropy values) in southern and western part. For small fluctuations spatial distribution of GWPE values indicates that yearly SPEI‐6 August series are more predictable in the western part of the country while May to August monthly SPEI‐6 sequences are more predictable in the eastern part.

Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model.

There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance. We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach. We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology. The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics. Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.