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Predator hunting strategies, such as stalking versus coursing behaviors, are hypothesized to influence antipredator behaviors of prey and can describe the movement behaviors of predators themselves. Predators and prey may alter their movement in relation to predator hunting modes, yet few studies have evaluated how these strategies influence movement behaviors of free‐ranging animals in a multiple‐predator, multiple‐prey system. We fit hidden Markov models (HMM) with movement data derived from >400 GPS‐collared ungulates and large predators in eastern Washington, USA. We used these models to test our hypotheses that stalking (cougars [Puma concolor]) and coursing (gray wolves [Canis lupus]) predators would exhibit different broad‐scale movement behaviors consistent with their respective hunting strategies in areas that increased the likelihood of encountering or capturing ungulate prey (e.g., habitats selected by deer [Odocoileus spp.]). Similarly, we expected that broadscale movement behaviors of prey would change in response to background levels of predation risk associated with each predator's hunting strategy. We found that predators and ungulate prey adjusted their broadscale movements in response to one another's long‐term patterns of habitat selection but not based on differences in predator‐hunting strategies. Predators changed their movement behaviors based on the type of prey, whereas ungulates generally reduced movement in areas associated with large predators, regardless of the predator's hunting strategy. Both predator and prey movements varied in response to landscape features but not necessarily based on habitat that would facilitate specific hunting behaviors. Our results suggest that predators and prey adjust their movements at broad temporal scales in relation to long‐term patterns of risk and resource distributions, potentially influencing their encounter rates with one another at finer spatiotemporal scales. Habitat features further influenced changes in movement, resulting in a complex combination of movement behaviors in multiple‐predator, multiple‐prey systems. [ABSTRACT FROM AUTHOR]

The problem of asynchronous fault detection filtering for nonhomogeneous Markov jumping systems with dynamic quantization and hybrid cyber attacks is addressed in this paper. The introduction of polytopic-structure-based transition probabilities is employed to describe the nonhomogeneous Markov process. An asynchronous fault detection filter is proposed, which utilizes the hidden Markov model to achieve comprehensive access to the plant mode information. Prior to transmission to the filter, the measurement output of the system undergoes quantization using a dynamic quantizer. The novel hybrid cyber attacks model being discussed involves four types of attacks: deception attacks, denial-of-service attacks, no attack, and hybrid attacks with both deception and denial-of-service attacks. By constructing Lyapunov functional, sufficient conditions are presented for achieving the stochastic stability with H ∞ performance. Under the complex network environment, the industrial application of the presented asynchronous fault detection filtering model is demonstrated on a non-isothermal continuous stirred tank reactor. The simulation results confirm the practicality of the proposed design method. • The dynamic quantizer is adopted to quantify measurement output signals to reduce information exchange frequency and communication burden. • A convex polyhedron is used to describe the time-varying transition probability matrix. • Due to the mode information between the filter and the plant cannot be fully obtained in the networked control system, a HMM-based asynchronous mechanism is proposed. • In order to improve the system performance, a novel hybrid network attack model is proposed, which includes four attack modes. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for analyzing temporal directional data with scatter and heavy tails. A hidden Markov model with contaminated von Mises-Fisher emission distribution is developed. The model is implemented using forward and backward selection approach that provides additional flexibility for contaminated as well as non-contaminated data. The utility of the method for finding homogeneous time blocks (regimes) is demonstrated on several experimental settings and two real-life text data sets containing presidential addresses and corporate financial statements respectively. [ABSTRACT FROM AUTHOR]

This paper deals with fault-tolerant asynchronous control for Fornasini-Marchesini second model-based two-dimensional Markov jump systems under actuator failures and mode mismatches. The actuator failures are modeled as norm-bounded uncertainties and the mode mismatches between the plant and the designed controller are characterized by a hidden Markov model. Employing the Lyapunov direct method, a criterion is established to ensure the asymptotic mean square stability and H∞ noise suppression performance of the closedloop system. Then, two design methods for the fault-tolerant asynchronous controller are proposed for the cases where the actuator fault matrix is known and unknown, respectively. The required controller gains can be determined through feasible solutions to the linear matrix inequalities. Finally, the effectiveness of these design methods is demonstrated by the Darboux equation. [ABSTRACT FROM AUTHOR]

In recent years, automatic map matching has received great technical progress. However, when it comes to vague matching situations such as improper vocabulary use, there still lack reliable solutions. To handle the current gap, this paper proposes a novel automatic map matching method based on the hybrid computing framework of hidden Markov model (HMM) and conditional random field. First, the data filtering is completed by performing second-order transformation towards automatic matching conditions of HMM. Then, the data classification is completed using automatic data classification based on the conditional random field. After that, a hybrid computing framework with spatial elements and layer selection is built to generate map matching results. Finally, some simulation experiments are conducted for evaluation. For one thing, the trend of matching accuracy changes under specified conditions is basically the same as that of nonspecified conditions. The maximum difference in matching calculation values is about 3 times. However, once the vocabulary continues to increase, the difference in matching results between the two narrows to 10–20%. For the other thing, the matching accuracy of a specified state is higher than that of sending a specified state. While nonspecified fuzzy matching accuracy is about 3 times higher and nonspecified precision matching accuracy is about 50% higher. [ABSTRACT FROM AUTHOR]

The temporal dynamics of resting‐state networks may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan. However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi‐stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole‐brain level using hidden Markov models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macroscale of cortical networks. The present study used an HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4–6 years old). We used high‐density EEG to better capture the fast reconfiguration patterns of the HMM‐derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM‐derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default‐mode brain states, predicted differences on parental‐report questionnaire scores. Overall, these findings support the importance of resting‐state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers. [ABSTRACT FROM AUTHOR]

With the integration of the manufacturing process in the Internet, cybersecurity becomes even more important in the process of factory operations. Because of the complexity of data traffic in the manufacturing industry, the identification and classification of anomalous behavior is an important direction of current research. System calls are made at the operating system level. Therefore, the use of system call sequences can detect potential threats much earlier. So, this paper chooses system call information as the research object. System call orderliness is an ideal property for analysis of using hidden Markov model. In terms of methodology, the SVH2M model improves the performance and efficiency of attack detection in manufacturing systems. The SVH2M model combines pSVM with mHMM. The pSVM and mHMM models use SVMPSA and PATA. pSVM is first used to initially categorize the system call sequences into normal and abnormal categories. The classification of pSVM can reduce the amount of data. This reduces the error rate of mHMM processing. Next, mHMM is built for different types of known anomalies. The SVH2M model in the false positive rate is lower than that of hidden Markov model. The experimental results show that the AUC of the improved model is increased by 17%. The average Mismatch Rate is reduced by 16%. The performance and efficiency of detecting anomalous information are improved in manufacturing systems. [ABSTRACT FROM AUTHOR]

Background: In prey, patterns of individual habitat selection and movement can be a consequence of an individuals' anti-predator behavior. Adjustments of anti-predator behavior are important for prey to increase their survival. Hunters may alter the anti-predator behavior of prey. In long-lived animals, experience may cause behavioral changes during individuals' lifetime, which may result in altered habitat selection and movement. Our knowledge of which specific events related to hunting activity induce behavioral changes in solitary living species is still limited. Methods: We used offspring loss in a solitary and long-lived ungulate species, moose (Alces alces), as our model system. We investigated whether offspring loss to hunters induces behavioral changes in a species subjected to heavy human harvest but free from natural predation. To test for behavioral change in relation to two proxies for experience (calf fate and age), we combined movement data from 51 adult female moose with data on their offspring survival and female age. We tested for adjustments in females' habitat selection and movement following calf harvest using Hidden Markov Models and integrated Step Selection Analysis to obtain behavioral state specific habitat selection coefficients. Results: We found that females with a harvested calf modified habitat selection and movement during the following hunting season. Female moose selected for shorter distance to roads during the night, selected for shorter distance to forests and greater distance to human settlements following calf harvest than females who had not lost a calf. The survival of twins in a given hunting season was related to female age. Older females we more likely to have twins survive the hunting season. Conclusions: Our findings suggest that losing offspring to human harvest imposes behavioral changes in a long-lived ungulate species, leading to adjustments in females' habitat selection and movement behavior, which may lower the risk of encountering hunters. In our study, female moose that experienced calf loss selected for lower distance to forest and selected for greater distance to human settlements during periods of high hunting pressure compared to females without the experience of calf loss during the previous hunting season. We interpret this as potential learning effects. [ABSTRACT FROM AUTHOR]

Automating performance improvement in 4G cellular networks is a challenging research area due to existing limitations in artificial intelligence and machine learning applications. This study addresses these challenges by developing a data analysis model using Hidden Markov Models (HMM) to predict Key Performance Indicators (KPIs) and automate performance assessments. Data was analyzed from 1600 new sites of a mobile operator in Indonesia, collected from July 2023 to January 2024. The methodology follows Knowledge Discovery in Database (KDD) for data mining and applying HMMs to forecast KPIs such as eRAB Drop Rate and Setup Success Rate. The model achieved a Mean Absolute Error (MAE) of 0.005 and a Root Mean Square Error (RMSE) of 0.069 for eRAB Drop Rate, with an F1 Score reaching up to 99.76%. The performance of the model improves with an increasing number of observation states, particularly for Inter Frequency Handover Success Rate (HOSR) and RRC Connection Setup Success Rate. Despite strong performance, there is potential for further enhancement, especially for KPIs with high variability like Intra Frequency HOSR. This research demonstrates that HMMs are effective in predicting KPIs with high accuracy, rather than traditional time-series models. The results align with recent studies and suggest that combining HMMs with techniques such as LSTM or Random Forests could improve predictive accuracy. These methods are also applicable to another technology, especially 5G networks, offering valuable insights for more effective network management and performance optimization. [ABSTRACT FROM AUTHOR]

Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing recognition rates. In this paper, we have proposed the Hidden Markov Model (HMM) and convolutional Neural Network (CNN) models for FR by using ORL and Yale datasets. Facial images from the given data sets are divided into 3 portions, 4 portions, 5 portions, and 6 portions corresponding to their respective HMM hidden states being used in the HMM model. Quantized levels of eigenvalues and eigenvector coefficients of overlapping blocks of facial images define the observation states of the HMM model. For image selection and rejection, a threshold is calculated using singular value decomposition (SVD). After training HMM on 3 states HMM, 4 states HMM, 5 states HMM, and 6 states HMM, the recognition accuracies are 96.5%, 98.5%, 98.5%, and 99.5%, respectively, on the ORL database and 90.6667%, 94.6667%, 94.6667%, and 94.6667% on the Yale database. The CNN model uses convolutional layers, a max-pooling layer, a flattening layer, a dense layer, and a dropout layer. Relu is used as the activation function in all layers except in the last layer, where softmax is used as the activation function. Cross entropy is used as a loss function, and we have used the Adam optimizer in our proposed algorithm. The proposed CNN model has given 100% training and testing accuracy on the ORL data set. While using the Yale data set, the CNN model has a training accuracy of 100% and a testing accuracy of 85.71%. In this paper, our proposed model showed that the HMM model is cost-effective with lesser accuracy, while the CNN model is more accurate as compared to HMM but has a higher computational cost. [ABSTRACT FROM AUTHOR]

During spring, migratory birds are required to optimally balance energetic costs of migration across heterogeneous landscapes and weather conditions to survive and reproduce successfully. Therefore, an individual's migratory performance may influence reproductive outcomes. Given large‐scale changes in land use, climate, and potential carry‐over effects, understanding how individuals migrate in relation to breeding outcomes is critical to predicting how future scenarios may affect populations. We used GPS tracking devices on 56 Greater White‐fronted Geese (Anser albifrons) during four spring migrations to examine whether migration characteristics influenced breeding propensity and breeding outcome. We found a strong longitudinal difference in arrival to the breeding areas (18 days earlier), pre‐nesting duration (90.9% longer), and incubation initiation dates (9 days earlier) between western‐ and eastern‐Arctic breeding regions, with contrasting effects on breeding outcomes, but no migration characteristic strongly influenced breeding outcome. We found that breeding region influenced whether an individual likely pursued a capital or income breeding strategy. Where individuals fell along the capital‐income breeding continuum was influenced by longitude, revealing geographic effects of life‐history strategy among conspecifics. Factors that govern breeding outcomes likely occur primarily upon arrival to breeding areas or are related to individual quality and previous breeding outcome, and may not be directly tied to migratory decision‐making across broad scales. [ABSTRACT FROM AUTHOR]

In statistical genetics, the sequentially Markov coalescent (SMC) is an important family of models for approximating the distribution of genetic variation data under complex evolutionary models. Methods based on SMC are widely used in genetics and evolutionary biology, with significant applications to genotype phasing and imputation, recombination rate estimation, and inferring population history. SMC allows for likelihood-based inference using hidden Markov models (HMMs), where the latent variable represents a genealogy. Because genealogies are continuous, while HMMs are discrete, SMC requires discretizing the space of trees in a way that is awkward and creates bias. In this work, we propose a method that circumvents this requirement, enabling SMC-based inference to be performed in the natural setting of a continuous state space. We derive fast, exact procedures for frequentist and Bayesian inference using SMC. Compared to existing methods, ours requires minimal user intervention or parameter tuning, no numerical optimization or E-M, and is faster and more accurate. for this article are available online. [ABSTRACT FROM AUTHOR]

The characterisation of resting‐state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high‐density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium‐density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high‐density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG‐derived RSNs to those from MEG, including their ability to capture age‐related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject‐specific structural MRI images. [ABSTRACT FROM AUTHOR]

In plant heredity, the phenotype is the result of observation that can be directly observed, while the genotype is the underlying hidden factor that underlies the expression of the phenotype. The genotype is an important aspect that needs to be understood to explain the pattern of trait inheritance and predict trait inheritance in subsequent generations. The discrete hidden Markov model is a model generated by pair of an unobserved Markov chain and an observation process. This model can be applied to tetraploid plant crosses by modeling genotypes as hidden state and phenotypes as the obeservation process. The probability of dominant phenotype in monohybrid, dihybrid and trihybrid crosses occurring over ten generations during that period is as follows 61,305%, 37,583%, and 23,041%. Furthermore, as more traits are crossed, the probability of dominant phenotype appearing within ten generations decreases. When the dominant phenotype occurs over ten generations, the same genotype can be obtained in monohybrid, dihybrid, and trihybrid crosses, which is heterozygous in the first and second generations, while from the third to the tenth generation it is homozygous dominant.

This paper introduces a subclass of Markov renewal processes (MRPs) and presents a solution to the optimal filtering problem in a stochastic observation system, where the state is modeled by an MRP and observed indirectly through noisy measurements. The MRPs considered here can be interpreted as continuous-time Markov chains (CTMCs) with a finite set of abstract states representing distributions of random vectors. The paper outlines the probabilistic properties of MRPs, emphasizing the ability to express any arbitrary function of the MRP as the solution to a linear stochastic differential system (SDS) with a martingale on the right-hand side (RHS). Using these properties, an optimal filtering problem is formulated in stochastic observation systems, where the hidden state belongs to the class of MRPs, and the observations consist of both diffusion and counting components. The drift terms in all observations depend on the system state. An optimal filtering estimate for a scalar function of the MRP is provided through the solution of an SDS with innovation processes on the RHS. Additionally, the paper presents a version of the Kushner-Stratonovich equation, describing the evolution of the conditional probability density function (PDF). To demonstrate the practical application of the estimation method, the paper presents a communications-related example, focusing on monitoring the qualitative state and numerical characteristics of a network channel using noisy observations of round-trip time (RTT) and packet loss flow. The paper also highlights the robustness of the filtering algorithm in scenarios where the MRP distribution is uncertain.

Abstract Background In prey, patterns of individual habitat selection and movement can be a consequence of an individuals’ anti-predator behavior. Adjustments of anti-predator behavior are important for prey to increase their survival. Hunters may alter the anti-predator behavior of prey. In long-lived animals, experience may cause behavioral changes during individuals’ lifetime, which may result in altered habitat selection and movement. Our knowledge of which specific events related to hunting activity induce behavioral changes in solitary living species is still limited. Methods We used offspring loss in a solitary and long-lived ungulate species, moose (Alces alces), as our model system. We investigated whether offspring loss to hunters induces behavioral changes in a species subjected to heavy human harvest but free from natural predation. To test for behavioral change in relation to two proxies for experience (calf fate and age), we combined movement data from 51 adult female moose with data on their offspring survival and female age. We tested for adjustments in females’ habitat selection and movement following calf harvest using Hidden Markov Models and integrated Step Selection Analysis to obtain behavioral state specific habitat selection coefficients. Results We found that females with a harvested calf modified habitat selection and movement during the following hunting season. Female moose selected for shorter distance to roads during the night, selected for shorter distance to forests and greater distance to human settlements following calf harvest than females who had not lost a calf. The survival of twins in a given hunting season was related to female age. Older females we more likely to have twins survive the hunting season. Conclusions Our findings suggest that losing offspring to human harvest imposes behavioral changes in a long-lived ungulate species, leading to adjustments in females' habitat selection and movement behavior, which may lower the risk of encountering hunters. In our study, female moose that experienced calf loss selected for lower distance to forest and selected for greater distance to human settlements during periods of high hunting pressure compared to females without the experience of calf loss during the previous hunting season. We interpret this as potential learning effects.

Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing recognition rates. In this paper, we have proposed the Hidden Markov Model (HMM) and convolutional Neural Network (CNN) models for FR by using ORL and Yale datasets. Facial images from the given data sets are divided into 3 portions, 4 portions, 5 portions, and 6 portions corresponding to their respective HMM hidden states being used in the HMM model. Quantized levels of eigenvalues and eigenvector coefficients of overlapping blocks of facial images define the observation states of the HMM model. For image selection and rejection, a threshold is calculated using singular value decomposition (SVD). After training HMM on 3 states HMM, 4 states HMM, 5 states HMM, and 6 states HMM, the recognition accuracies are 96.5%, 98.5%, 98.5%, and 99.5%, respectively, on the ORL database and 90.6667%, 94.6667%, 94.6667%, and 94.6667% on the Yale database. The CNN model uses convolutional layers, a max-pooling layer, a flattening layer, a dense layer, and a dropout layer. Relu is used as the activation function in all layers except in the last layer, where softmax is used as the activation function. Cross entropy is used as a loss function, and we have used the Adam optimizer in our proposed algorithm. The proposed CNN model has given 100% training and testing accuracy on the ORL data set. While using the Yale data set, the CNN model has a training accuracy of 100% and a testing accuracy of 85.71%. In this paper, our proposed model showed that the HMM model is cost-effective with lesser accuracy, while the CNN model is more accurate as compared to HMM but has a higher computational cost.

Trajectory mapping onto a road network is a complex yet important task. This is because, in the presence of circular sections, Y-shaped intersections, and sections with elevated overlaps with the ground, the conditions of road networks become complicated. Consequently, trajectory mapping becomes challenging owing to the complexities of road networks and the noise generated by high positioning errors. In this study, in response to the difficulty in handling redundant noisy trajectory data in complex road network environments, a complex road network map-matching method based on trajectory structure extraction is proposed. The features of the structure are extracted from the original trajectory data to reduce the effects of redundancy and noise on matching. An adaptive screening candidate method is proposed using driver behavior to estimate the road density and reduce the matching time by selecting effective candidates. A spatiotemporal analysis function is redefined using speed and distance features, and a directional analysis function is proposed for use in combination with directional features to improve the matching accuracy of complex road networks. An experimental evaluation based on real-ground trajectory data collected using in-vehicle sensing devices is conducted to verify the effectiveness of the algorithm. Moreover, extensive experiments are performed on challenging real datasets to evaluate the proposed method, and its accuracy and efficiency are compared with those of two state-of-the-art map-matching algorithms. The experimental results confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract Backgroud Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE. Methods Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database. Results Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p

Abstract Movement models are frequently fit to animal location data to understand how individuals respond to and interact with local environmental features. Several open‐source software packages are available for analysing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness of fit. We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness of fit of integrated step‐selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models and demonstrate the approach using both an integrated step‐selection analysis and a hidden Markov model applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate statistical significance using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g. to explore variability in model behaviour across stochastic simulations or to identify areas where the model could be improved). We provide coded examples and vignettes to demonstrate the flexibility of the approach. We encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness of fit.

Backgroud: Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database. Results: Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function. Conclusions: This study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE. [ABSTRACT FROM AUTHOR]

In movement analysis, correlated random walk (CRW) models often use so‐called turning angles, which are measured relative to the previous movement direction. To segregate between different movement modes, hidden Markov models (HMMs) describe movements as piecewise stationary CRWs in which the distributions of turning angles and step sizes depend on the underlying state. This typically allows for the segregation of movement modes that show different movement speeds. We show that in some cases, it may be interesting to investigate absolute angles, that is, biased random walks (BRWs) instead of turning angles. In particular, while discrimination between states in the turning angle setting can only rely on movement speed, models with absolute angles can be used to discriminate between sections of different movement directions. A preprocessing algorithm is provided that enables the analysis of absolute angles in the existing R package moveHMM. In a data set of movements of cell organelles, models using not the turning angle but the absolute angle could capture interesting additional properties. Goodness‐of‐fit was increased for HMMs with absolute angles, and HMMs with absolute angles tended to choose a higher number of states, suggesting the existence and relevance of prominent directional changes in the present data set. These results suggest that models with absolute angles can provide important information in the analysis of movement patterns if the existence and frequency of directional changes is of biological importance. [ABSTRACT FROM AUTHOR]

Speech assistive system for people with neuro disorders is a highly challenging task till date. Any kind of neuro cognitive disability affects the speech production mechanism that leads to speech impairment. Representation learning methods have recently emerged to improve the outcome of machine learning algorithms. In case of complex recognition tasks such as disordered speech recognition, learning compact and efficient representations for disordered speech utterances is important. Recent deep learning-based architectures need sufficiently large amount of impaired speech samples which are tedious with respect to neurologically disabled people. In this work, we focus on proposing a representation learning approach that uses traditional sequential model such as Hidden Markov Model (HMM) which works moderately well with small amount of impaired speech data. We propose a novel sequence to vector-based HMM State Sequence (HMM-SS) approach which is very compact and has proved to be an efficient representation for disordered speech utterances. The efficiency of the proposed HMM-SS approach is assessed using four datasets, namely 50 words of TORGO, 100-common words dataset of the UA-SPEECH, 50 help-seeking words and 100-common words of Impaired speech corpus in Tamil corpus. The proposed approach outperforms the baseline HMM, DNN-HMM and a recent state-of-the-art approach for all four datasets. The discriminative ability and the compactness of the proposed representation proved effective for disordered speech recognition. [ABSTRACT FROM AUTHOR]

Many wildlife species vary habitat selection across space, time, and behavior to maximize rewards and minimize risk. Multi‐scale research approaches that identify variation in wildlife habitat selection can highlight not only habitat preferences and risk tolerance but also movement strategies that afford coexistence or cause conflict with humans. Here, we examined how anthropogenic and natural features influenced coyote (Canis latrans) habitat selection in a mixed‐use, agricultural landscape in Mendocino County, California, USA. We used resource selection functions and hidden Markov models to test whether coyote selection for anthropogenic and natural features varied by time of day or by behavioral state (resting, foraging, and traveling). We found that coyotes avoided development, but, contrary to our expectations, coyotes selected for roads, agriculture, and areas with risk of human encounter and rifle use regardless of diel period or behavioral state. While traveling, coyotes increased selection for roads and avoided ruggedness, indicating that unpaved roads may enhance connectivity for coyotes in mixed‐use landscapes. Finally, we found that coyotes selected for mountain lion habitat when resting and at night, signifying that risk from natural predators was not a factor in habitat selection at coarse scales. Coyote habitat selection for places and times associated with human activity, without variation across scales, signals a potential for conflict if coyotes are perceived by people as a nuisance. [ABSTRACT FROM AUTHOR]

The fight against bacterial antibiotic resistance must be given critical attention to avert the current and emerging crisis of treating bacterial infections due to the inefficacy of clinically relevant antibiotics. Intrinsic genetic mutations and transferrable antibiotic resistance genes (ARGs) are at the core of the development of antibiotic resistance. However, traditional alignment methods for detecting ARGs have limitations. Artificial intelligence (AI) methods and approaches can potentially augment the detection of ARGs and identify antibiotic targets and antagonistic bactericidal and bacteriostatic molecules that are or can be developed as antibiotics. This review delves into the literature regarding the various AI methods and approaches for identifying and annotating ARGs, highlighting their potential and limitations. Specifically, we discuss methods for (1) direct identification and classification of ARGs from genome DNA sequences, (2) direct identification and classification from plasmid sequences, and (3) identification of putative ARGs from feature selection. [ABSTRACT FROM AUTHOR]

The human brain exhibits spatio‐temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography‐functional magnetic resonance imaging (EEG‐fMRI) eyes open (EO) and eyes closed (EC) resting‐state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG‐defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window‐based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting‐state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha‐BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in‐depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics. [ABSTRACT FROM AUTHOR]

Railway track bed defects affect the normal operation of trains and pose great safety risks. In order to detect such issues early, we developed a railway track bed defect detection method which uses optical fibre sensors and an improved HMM (hidden Markov model) to detect the signals collected by a DAS (distributed acoustic sensing) system. First, by analysing the physical process of train operation and determining the number of hidden states, a waveform segmentation method based on average amplitude was used to solve the problem of unequal signal lengths. Second, an adaptive power spectrum energy ratio calculation method was employed to extract track fault features, a set of which was constructed by combining various quantity features. Then, normal and abnormal models were trained according to the sensor measurement area. Finally, the probability of detecting the signal with each model was compared to determine whether the signal was abnormal. Experiments were conducted to compare the applicability of the waveform segmentation method and the feature extraction method. The results show that the HMM based on both waveform segmentation and track bed defect feature sets had the highest recognition rate, the lowest number of false detection areas, and a greater impact on the signal in the early development stage of track bed defects. The proposed method, therefore, has strong recognition ability, which makes it suitable for track bed defect detection. [ABSTRACT FROM AUTHOR]

Admixture between populations and species is common in nature. Since the influx of new genetic material might be either facilitated or hindered by selection, variation in mixture proportions along the genome is expected in organisms undergoing recombination. Various graph-based models have been developed to better understand these evolutionary dynamics of population splits and mixtures. However, current models assume a single mixture rate for the entire genome and do not explicitly account for linkage. Here, we introduce TreeSwirl , a novel method for inferring branch lengths and locus-specific mixture proportions by using genome-wide allele frequency data, assuming that the admixture graph is known or has been inferred. TreeSwirl builds upon TreeMix that uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast to TreeMix , our model infers locus-specific mixture proportions employing a hidden Markov model that accounts for linkage. Through simulated data, we demonstrate that TreeSwirl can accurately estimate locus-specific mixture proportions and handle complex demographic scenarios. It also outperforms related D - and f -statistics in terms of accuracy and sensitivity to detect introgressed loci. [ABSTRACT FROM AUTHOR]

The traditional blockchain deployment process is too complicated and has high technical requirements for blockchain deployers. Deploying a blockchain requires building a complex software-dependent environment, being able to use Linux commands for cumbersome parameter configurations, as well as the need to consider whether the hardware meets the requirements for running a blockchain. To address these current challenges in blockchain deployment both domestically and internationally, a web-based automatic deployment system with an interactive front-end and back-end has been developed. This system streamlines the process by automatically configuring and deploying blockchains while providing deployers with a graphical interface to monitor the entire deployment procedure. Meanwhile, in order to improve the efficiency of blockchain deployment, a Hidden Markov Model has been designed for blockchain deployment, which can predict the best deployment method for blockchain deployment under the current software environment. As one of the excellent blockchain platforms in China, Chainmaker has the outstanding features of independent control, flexible assembly, software and hardware integration, open source, and openness. The system takes Chainmaker as an experimental object and after a lot of tests, it can easily build a blockchain network on the server. [ABSTRACT FROM AUTHOR]

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Objectives: The presence of occult nodal metastases in patients with oral tongue squamous cell carcinomas (OTSCCs) has implications for treatment. More than 30% of patients will have occult nodal metastases, yet a considerable number of patients undergo unnecessary invasive neck dissection to confirm nodal status. In this work, we propose a probabilistic model for lymphatic metastatic spread that can quantify the risk of microscopic involvement at the lymph node level (LNL) given the location of macroscopic metastases and the tumor stage using the MRI method. Materials and methods: A total of 108 patients of OTSCCs were included in the study. A hidden Markov model (HMM) was used to compute the probabilities of transitions between states over time based on MRI. Learning of the transition probabilities was performed via Markov chain Monte Carlo sampling and was based on a dataset of OTSCC patients for whom involvement of individual LNLs was reported. Results: Our model found that the most common involvement was that of level I and level II, corresponding to a high probability of ?b1 = 0.39 ± 0.05, ?b2 = 0.53 ± 0.09; lymph node level I had metastasis, and the probability of metastasis in lymph node II was high (93.79%); lymph node level II had metastasis, and the probability of metastasis in lymph node III was small (7.88%). Lymph nodes progress faster in the early stage and slower in the late stage. Conclusion: An HMM can produce an algorithm that is able to predict nodal metastasis evolution in patients with OTSCCs by analyzing the macroscopic metastases observed in the upstream levels, and tumor category. [ABSTRACT FROM AUTHOR]

The paper investigates the problem of the time arrow in terms of artistic creation. The statistical model of artwork signal processing is established in order to identify a duality relation between psychological and intrinsic time. The intrinsic time is linked to the time operator of wavelets, and the psychological one is metaphorically related to the spatial domain of an artwork. The increase of irreducible randomness along the timeline is formulated by the second law of thermodynamics. The dual statement concerns an increase in statistical complexity, which is the definition of self-organization. In that manner, two arrows of time which are opposed and dual to each other are recognized. The authors have indicated a link to the theory of musical forms, the originality issue, and the perspective problem. Some repercussions for art theory, neuroaesthetics, and psychophysics have also been implied. [ABSTRACT FROM AUTHOR]

Autonomous driving systems for unmanned ground vehicles (UGV) operating in enclosed environments strongly rely on LiDAR localization with a prior map. Precise initial pose estimation is critical during system startup or when tracking is lost, ensuring safe UGV operation. Existing LiDAR-based place recognition methods often suffer from reduced accuracy due to only matching descriptors from individual LiDAR keyframes. This paper proposes a multi-frame descriptor-matching approach based on the hidden Markov model (HMM) to address this issue. This method enhances the place recognition accuracy and robustness by leveraging information from multiple frames. Experimental results from the KITTI dataset demonstrate that the proposed method significantly enhances the place recognition performance compared with the scan context-based single-frame descriptor-matching approach, with an average performance improvement of 5.8% and with a maximum improvement of 15.3%. [ABSTRACT FROM AUTHOR]

Purpose: To investigate dynamic functional connectivity (dFC) within the cerebellar‐whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients. Methods: Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum‐whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed. Results: Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep. Conclusion: Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high‐order and low‐level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA. [ABSTRACT FROM AUTHOR]

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data. • It studies a generalized HMM where observation depends on two-step states. • Two-filter smoothing formula and its generalization are established. • Auxiliary variable sampling technique is applied to draw samples. • It proposes two particle smoothing algorithms by using sequential Monte Carlo. [ABSTRACT FROM AUTHOR]

Search operations performed by adaptive autonomous maritime vehicles have been a topic of considerable interest for many years. Such operations require carefully scheduled coordination of multiple vehicles performing search tasks across the region of interest. Due to the inherent uncertainty of the maritime environment, however, an initially planned search schedule may not be maintained if the vehicles have significant capability to adapt their tasks to match the environment they detect in real time. We propose a multi-vehicle adaptive algorithm for dynamic evaluation and elastic re-planning of variable-length tasks commonly found in the maritime environments. In adaptive evaluation and re-planning problems, a set of tasks are initially planned for execution by adaptive, autonomous search vehicles. Tasks are allocated to search vehicles under a pre-defined schedule based on prior knowledge and desired outcome. Because of the vehicles’ autonomy and reactivity to in situ conditions such as environment or target pose, the precise duration and actions required by each task are unknown a priori. We develop a hidden Markov model (HMM) for propagating task estimates, coupled with a quadratic-programming-based elastic re-scheduler. The result is an integrated estimate-and-schedule adaptation scheme that quickly and efficiently re-plans the vehicles’ schedules based on in situ observations. The numerical simulation results show that this novel HMM approach decreases avoidable schedule variation by over a factor of two compared to existing methods. [ABSTRACT FROM AUTHOR]

Movement models are frequently fit to animal location data to understand how individuals respond to and interact with local environmental features. Several open‐source software packages are available for analysing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness of fit.We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness of fit of integrated step‐selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models and demonstrate the approach using both an integrated step‐selection analysis and a hidden Markov model applied to fisher (Pekania pennanti) data.A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate statistical significance using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g. to explore variability in model behaviour across stochastic simulations or to identify areas where the model could be improved).We provide coded examples and vignettes to demonstrate the flexibility of the approach. We encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness of fit. [ABSTRACT FROM AUTHOR]

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16 hr fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Abstract During spring, migratory birds are required to optimally balance energetic costs of migration across heterogeneous landscapes and weather conditions to survive and reproduce successfully. Therefore, an individual's migratory performance may influence reproductive outcomes. Given large‐scale changes in land use, climate, and potential carry‐over effects, understanding how individuals migrate in relation to breeding outcomes is critical to predicting how future scenarios may affect populations. We used GPS tracking devices on 56 Greater White‐fronted Geese (Anser albifrons) during four spring migrations to examine whether migration characteristics influenced breeding propensity and breeding outcome. We found a strong longitudinal difference in arrival to the breeding areas (18 days earlier), pre‐nesting duration (90.9% longer), and incubation initiation dates (9 days earlier) between western‐ and eastern‐Arctic breeding regions, with contrasting effects on breeding outcomes, but no migration characteristic strongly influenced breeding outcome. We found that breeding region influenced whether an individual likely pursued a capital or income breeding strategy. Where individuals fell along the capital‐income breeding continuum was influenced by longitude, revealing geographic effects of life‐history strategy among conspecifics. Factors that govern breeding outcomes likely occur primarily upon arrival to breeding areas or are related to individual quality and previous breeding outcome, and may not be directly tied to migratory decision‐making across broad scales.

Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging resting-state dataset. Results: Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion: Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models recover connectivity from intensity-based hidden states, or from connectivity summed across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.

Background: Gait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15–30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies. Objective: We aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait. Methods: We studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues. Results: The occurrence of low and high β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues. Conclusions: We show that STN oscillatory activity in the α and β frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.

Abstract Background Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred. Results We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations. Conclusions Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Childrens Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data-driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision-making for patients with intractable epilepsy.

Introduction: Visual scoring of sleep electroencephalography (EEG) has long been considered the gold standard for sleep staging. However, it has several drawbacks, including high cost, time-intensiveness, vulnerability to human variability, discomfort to patients, lack of visualization to validate the hypnogram, and no acknowledgment of differences between delta and slow oscillation deep sleep. This report highlights a spectral scoring approach that addresses all these shortcomings of visual scoring. Past algorithms have used spectral information to help classify traditional visual stages. The current method used the clearly visible spectral patterns to develop new spectral stages, which are similar to but not the same as visual stages. Importantly, spectral scoring delivers both a hypnogram and a whole-night spectrogram, which can be visually inspected to ensure accurate scoring. Methods: This study compared traditional visual scoring of 32-channel polysomnography with forehead-only spectral scoring from an EEG patch worn concurrently. The PSG was visually scored by trained technicians and the forehead patch was scored spectrally. Because non-rapid eyemovement (NREM) stage divisions in spectral scoring are not based on visual NREM stages, the agreements are not expected to be as high as other automated sleep scoring algorithms. Rather, they are a guide to understanding spectral stages as they relate to the more widely understood visual stages and to emphasize reasons for the differences. Results: The results showed that visual REM was highly recognized as spectral REM (89%). Visual wake was only scored as spectral Wake 47% of the time, partly because of excessive visual scoring of wake during Light and REM sleep. The majority of spectral Light (predominance of spindle power) was scored as N2 (74%), while less N2 was scored as Light (65%), mostly because of incorrect visual staging of Lo Deep sleep due to high-pass filtering. N3 was scored as both Hi Deep (13 Hz power, 42%) and Lo Deep (0-1 Hz power, 39%), constituting a total of 81% of N3. Discussion: The results show that spectral scoring better identifies clinically relevant physiology at a substantially lower cost and in a more reproducible fashion than visual scoring, supporting further work exploring its use in clinical and research settings. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD. [ABSTRACT FROM AUTHOR]