Hidden Markov Model Research Articles

Single-Channel Sleep EEG Classification Method Based on LSTM and Hidden Markov Model

Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications.

Random Forest Spatial-Temporal and State Space Models to Assess the Impact of Bushfire-Induced Aerosol Events on Ozone Depletion in Australia

Serious concerns exist that the increasing frequency of fires may delay the recovery of ozone given increasing temperatures due to climate change. Australian bushfires from September 2019 to February 2020 were catastrophic. A random forest spatial-temporal (RF sp) analysis using satellite data to detect an association between Australian bushfires and stratosphere ozone on the local depletion of ozone in the vicinity of fires in three regions of Australia (Pacific Ocean, Victoria, NSW) has shown a significant reduction in ozone attributable to aerosols from fires. By intervention analysis, increases in aerosols in all three regions were shown to have a significant and ongoing impact 1–5 days later on reducing ozone (p < 0.0001). Intervention analysis also gave similar periods of aerosol exceedance to those found by Hidden Markov models (HMMs). HMMs established a significant and quantifiable decline in ozone due to bushfire-induced aerosols, with significant lags of 10–25 days between times of aerosol exceedance and subsequent ozone level decline in all three regions.

Wind effects on individual male and female Bactrocera jarvisi (Diptera: Tephritidae) tracked using harmonic radar.

Wind affects the movement of most volant insects. While the effects of wind on dispersal are relatively well understood at the population level, how wind influences the movement parameters of individual insects in the wild is less clear. Tephritid fruit flies, such as Bactrocera jarvisi, are major horticultural pests worldwide and while most tephritids are nondispersive when host plants are plentiful, records exist for potentially wind-assisted movements up to 200 km. In this study, harmonic radar (HR) was used to track the movements of both male and female lab-reared B. jarvisi in a papaya field. Overall flight directions were found to be correlated with wind direction, as were the subset of between-tree movements, while within-tree movements were not. Furthermore, the effect of wind direction on fly trajectories varied by step-distance but not strongly with wind speed. Mean path distance, step distance, flight direction, turning angle, and flight propensity did not vary by sex. Both male and female movements are well fit by 2-state hidden Markov models further supporting the observation that B. jarvisi move differently within (short steps with random direction) and between (longer more directional steps) trees. Data on flight directionality and step-distances determined in this study provide parameters for models that may help enhance current surveillance, control, and eradication methods, such as optimizing trap placements and pesticide applications, determining release sites for parasitoids, and setting quarantine boundaries after incursions.

Hardware Area Efficiently and Real-Time FPGA Implementation of PHMMRGB

For encryption applications on embedded systems, operating in real-time while using minimal system resources is essential. It is expected that efficient and rapid encryption of high-resolution images is to be accomplished with limited hardware resources. Therefore, in order to ensure the desired efficiency of encryption of high-resolution images, the system must possess a digital architecture capable of achieving high speeds with minimal hardware resources. This study considered the limitations of a hardware architecture for an image encryption algorithm based on the Profile Hidden Markov Model called PHMMRGB. The proposed architecture is relatively simple in comparison to its alternatives. The hardware architecture, designed with the objective of using minimal resources, has been implemented on an FPGA. Based on the results of the proposed architecture, it is believed that the implementation of the specified method on an FPGA would yield high efficiency. Experiments conducted using large-sized satellite images confirmed this expectation.

Prior exposure to racial discrimination and patterns of acute parasympathetic nervous system responses to a race-related stress task among Black adults.

Black adults' prior exposure to racial discrimination may be associated with their acute parasympathetic reactivity to and recovery from a new race-related stressor. Existing analytical approaches to investigating this link obscure nuances in the timing, magnitude, and patterns of these acute parasympathetic nervous system (PNS) responses. In a re-analysis of a prior study, we utilize an hidden Markov model (HMM) approach to examine how prior experiences of racial discrimination are associated with intraindividual patterns of (1) physiological states of PNS activity and (2) patterns of and variability in transitions between these physiological states. Participants (N = 118) were Black young adults (range 18-29 years; Mage = 19.67, SDage = 2.04) who completed an online survey to index prior racial discrimination exposure, followed by an in-person lab visit during which their PNS activity in response to a race-related stress task was measured via electrocardiogram and converted into respiratory sinus arrhythmia. HMMs indicated evidence for two states: baseline and a second state representing a significant reduction in respiratory sinus arrhythmia. Most participants (93.22%) demonstrated a blunted response to the task, indicating that they did not transition from baseline during the procedure. Prior racial discrimination was not associated with HMM states or state transition parameters. Blunted physiological responses may be an important area of future investigation that could inform early life course mental and physical health screenings.

KiNext: a portable and scalable workflow for the identification and classification of protein kinases

BackgroundProtein kinases are a diverse superfamily of proteins common to organisms across the tree of life that are typically involved in signal transduction, allowing organisms to sense and respond to biotic or abiotic environmental factors. They have important roles in organismal physiology, including development, reproduction, acclimation to environmental stress, while their dysregulation can lead to disease, including several forms of cancer. Identifying the complement of protein kinases (the kinome) of any organism is useful for understanding its physiological capabilities, limitations and adaptations to environmental stress. The increasing availability of genomes makes it now possible to examine and compare the kinomes across a broad diversity of organisms. Here we present a pipeline respecting the FAIR principles (findable, accessible, interoperable and reusable) that facilitates the search and identification of protein kinases from a predicted proteome, and classifies them according to group of serine/threonine/tyrosine protein kinases present in eukaryotes.ResultsKiNext is a Nextflow pipeline that regroups a number of existing bioinformatic tools to search for and classify the protein kinases of an organism in a reproducible manner, starting from a set of amino acid sequences. Conventional eukaryotic protein kinases (ePKs) and atypical protein kinases (aPKs) are identified by using Hidden Markov Models (HMMs) generated from the catalytic domains of kinases. Furthermore, KiNext categorizes ePKs into the eight kinase groups by employing dedicated Hidden Markov Models (HMMs) tailored for each group. The performance of the KiNext pipeline was validated against previously identified kinomes obtained with other tools that were already published for two marine species, the Pacific oyster Crassostrea gigas and the unicellular green alga Ostreoccocus tauri. KiNext outperformed previous results by finding previously unidentified kinases and by attributing a large proportion of previously unclassified kinases to a group in both species. These results demonstrate improvements in kinase identification and classification, all while providing traceability and reproducibility of results in a FAIR pipeline. The default HMM models provided with KiNext are most suitable for eukaryotes, but the pipeline can be easily modified to include HMM models for other taxa of interest.ConclusionThe KiNext pipeline enables efficient and reproducible identification of kinomes based on predicted amino acid sequences (i.e. proteomes). KiNext was designed to be easy to use, automated, portable and scalable.

Asynchronous Sliding Mode Control of Networked Markov Jump Systems via an Asynchronous Observer Approach Based on a Dynamic Event Trigger

This paper explores the utilization of sliding mode control, which relies on an asynchronous observer, for Markov jump systems subject to external disturbances. Firstly, given that the system’s mode is not directly measurable and could potentially differ from the observer’s and controller’s mode, the paper constructs an asynchronous observer employing a hidden Markov model. Secondly, a sliding surface is designed to correspond with the asynchronous observer. Moreover, a multi-parameter event-triggered mechanism is incorporated into the observer design to alleviate bandwidth strain. Thirdly, by applying the integrated sliding mode control law, we ensure that the system state trajectories will reach the sliding surface within a finite time. Fourthly, the achievement of H∞ stability is realized by making use of the Lyapunov function. Lastly, a practical-oriented example is presented to illustrate the efficiency of the established method.

A Systematic Literature Review of Insurance Claims Risk Measurement Using the Hidden Markov Model

In the rapidly evolving field of insurance, accurate risk measurement is crucial for effective claims management and financial stability. Therefore, this research presented a systematic literature review (SLR) on insurance claims risk measurement using the Hidden Markov Model (HMM). Bibliometric analysis was conducted using VOSviewer 1.6.20 and ResearchRabbit software to map research trends and collaboration networks in this topic. This review explored the implementation of the HMM in predicting the frequency and severity of insurance claims, with a focus on the statistical distribution methods used. In addition, the research emphasized the influence of the number of hidden states in the HMM on claims behavior, both in terms of frequency and magnitude, and provided interpretations of these hidden dynamics. Data sources for this review comprised three databases, namely, Scopus, ScienceDirect, and Dimensions, and additional papers from a website. The article selection process followed updated PRISMA 2020 guidelines, resulting in twelve key papers relevant to the topic. The results offered insights into the application of the HMM for forecasting the frequency and severity of insurance claims and opened avenues for further investigation on distribution models and hidden state modeling.

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential

IntroductionAs emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of “brain state,” typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics.MethodsHidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity.ResultsA hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity.DiscussionOur results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

Multiphasic movement and step-selection patterns of dispersed tigers in the central Indian landscape.

Large carnivores play a crucial role in the ecosystem, though their conservation needs a landscape-level approach due to their wide range of habitats and dispersal events. The study of tigers in a human-dominated landscape matrix and their adaptation and adjustment of movement behaviours during the dispersal phase is essential for long-term conservation planning and management policy. We studied the dispersal event of five VHF/GPS collared individuals during 2009-2020. We investigated movement parameters (step length), and the effects of anthropogenic pressures (distance from village), distance from water and vegetation cover, on behavioural phase under a Hidden Markov Model framework. We also tested the effects of distance from village, vegetation cover, and habitat types on animal movement using an integrated Step Selection Function framework. The mean step length (405.44±10.63 m/hr) varied widely by different time of day. Displacement was high during the night (665.28±21.36 m/hr) compared to day (434.16±17.37 m/hr). Tigers moved fast (872.7m; 95% CI 839.1-906.3m) with longer step length and a directional turning angle in non-forested areas (i.e. the human-dominated landscape), likely to avoid conflict with humans. Individuals distinctly exhibited two behavioural states: encamping (~32% of the time) and travelling (~68% of the time). Further, they avoided the human-dominated landscape and mostly remained in and forested areas, especially during nighttime. Our study is the first attempt to understand behavioural transition of dispersal tigers and their habitat selection. Lesser anthropogenic disturbance and high vegetation cover positively influenced the tiger dispersal, while water availability did not affect their state transitional probability. Additionally, dispersers showed high affinity towards forested land during nighttime for travelling.The findings of this study show the importance of functional corridors and stepping stones (mostly encamping areas), and also provide baseline knowledge for integrated landscape management planning and policymaking for the long-term survival of tigers in metapopulation framework.

Model selection for mixture hidden Markov models: an application to clickstream data

AbstractIn a clickstream analysis setting, Mixture Hidden Markov Models (MHMMs) can be used to examine categorical sequences assuming they evolve according to a mixture of latent Markov processes, each related to a different subpopulation. These models involve identifying both the number of subpopulations and hidden states. This study proposes a model selection criterion based on an integrated completed likelihood approach that accounts for the two latent classes in the model. We implemented a Monte Carlo simulation study to compare selection criteria performance. In scenarios characterised by categorical short length sequences, our proposed measure outperforms the most commonly used model selection criteria in identifying components and states. The paper presents a case study on clickstream data collected from the website of a company operating in the hospitality industry and modelled by an MHMM selected by the proposed score.

Dynamic evolution of high-speed railway driver fatigue across scheduling shifts – a simulator study in China

ABSTRACT Scheduling significantly contributes to fatigue among high-speed rail (HSR) drivers, impacting both driving performance and safety. This study aims to analyse distinctiveness and evolutionary patterns of driver fatigue across different scheduling shifts, leveraging an HSR simulator and wearable devices. Through an extensive statistical examination, differences in most heart rate variability features were identified across shifts. The evolution of self-reported fatigue and physiological fatigue was investigated using a Markov chain and a hidden Markov model (HMM) that is extended using softmax regression, respectively. Results indicated a similarity in the initial fatigue state distribution and evolution trends across shifts, albeit with varying degrees of fluctuation. Morning shifts exhibited a higher likelihood of fatigue accumulation. The study revealed that even adequate pre-rest did not entirely counterbalance the impact of circadian rhythms. These findings may contribute to comprehending the temporal dynamics of driver fatigue evolution and offer insights for optimizing driver scheduling practices.

A Rigid–Flexible Supernumerary Robotic Arm/Leg: Design, Modeling, and Control

As humans’ additional arms or legs, supernumerary robotic limbs (SRLs) have gained great application prospects in many fields. However, current SRLs lack both rigidity/flexibility adaptability and arm/leg function conversion. Inspired by the muscular hydrostat characteristics of octopus tentacles, fiber-reinforced actuators (FRAs) were employed to develop SRLs simultaneously realizing flexible operation and stable support. In this paper, an SRL with FRAs was designed and implemented. The analytic model of the FRA was established to formulate the movement trajectory and stiffness profile of the SRL. A hierarchical hidden Markov model (HHMM) was proposed to recognize the wearer’s motion intention and control the SRL to complete the specific working mode and motion type. Experiments were conducted to exhibit the feasibility and superiority of the proposed robot.

Artificial Intelligence and the Future of Communication Sciences and Disorders: A Bibliometric and Visualization Analysis.

As artificial intelligence (AI) takes an increasingly prominent role in health care, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study aims to provide a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research. We conducted a bibliometric analysis of AI-based research in the discipline of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. Based on the bibliometric data, we examined publication trends and patterns, characteristics of research activities, and research hotspot tendencies. From 1985 onwards, there has been a consistent annual increase in publications, averaging 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, aphasia, dysarthria, Parkinson's disease, and Alzheimer's disease. Noteworthy AI models instantiated in CSD research encompass support vector machine, convolutional neural network, and hidden Markov model, among others. Compared to AI applications in other fields, the adoption of AI in CSD has lagged slightly behind. While CSD studies primarily use classical machine learning techniques, there is a growing trend toward the integration of deep learning methods. AI technology offers significant benefits for both research and clinical practice in CSD, but it also presents certain challenges. Moving forward, collaboration among technological, research, and clinical domains is essential to empower researchers and speech-language pathologists to effectively leverage AI technology for the study, diagnosis, assessment, and rehabilitation of CSD. https://doi.org/10.23641/asha.27162564.

Generative dynamical models for classification of rsfMRI data

ABSTRACT The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data. By fitting separate hidden Markov models to the classes in our training data and assigning class labels to test data based on their likelihood under those models, we are able to take advantage of dynamical patterns in the data without confronting the statistical limitations of some other dynamical approaches. Moreover, we demonstrate that hidden Markov models are able to successfully perform within-subject classification on the MyConnectome dataset solely on the basis of transition probabilities among their hidden states. On the other hand, individual Human Connectome Project subjects cannot be identified on the basis of hidden state transition probabilities alone—although a vector autoregressive model does achieve high performance. These results demonstrate a dynamical classification approach for rsfMRI data that shows promising performance, particularly for within-subject classification, and has the potential to afford greater interpretability than other approaches.

Are information criteria good enough to choose the right the number of regimes in hidden Markov models?

Selecting the number of regimes in Hidden Markov models is an important problem. Many criteria are used to do so, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), integrated completed likelihood (ICL), deviance information criterion (DIC), and Watanabe-Akaike information criterion (WAIC), to name a few. In this article, we introduced goodness-of-fit tests for general Hidden Markov models with covariates, where the distribution of the observations is arbitrary, i.e. continuous, discrete, or a mixture of both. A selection procedure is proposed based on this goodness-of-fit test. The main aim of this article is to compare the classical information criterion with the new criterion when the outcome is either continuous, discrete or zero-inflated. Numerical experiments assess the finite sample performance of the goodness-of-fit tests, and comparisons between the different criteria are made.

Hidden-Markov models for ordinal time series

AbstractA common approach for modeling categorical time series is Hidden-Markov models (HMMs), where the actual observations are assumed to depend on hidden states in their behavior and transitions. Such categorical HMMs are even applicable to nominal data but suffer from a large number of model parameters. In the ordinal case, however, the natural order among the categorical outcomes offers the potential to reduce the number of parameters while improving their interpretability at the same time. The class of ordinal HMMs proposed in this article link a latent-variable approach with categorical HMMs. They are characterized by parametric parsimony and allow the easy calculation of relevant stochastic properties, such as marginal and bivariate probabilities. These points are illustrated by numerical examples and simulation experiments, where the performance of maximum likelihood estimation is analyzed in finite samples. The developed methodology is applied to real-world data from a health application.

Single-vesicle imaging reveals actin-dependent spatial restriction of vesicles at the active zone, essential for sustained transmission

Synaptic-vesicle (SV) recruitment is thought to maintain reliable neurotransmitter release during high-frequency signaling. However, the mechanism underlying the SV reloading for sustained neurotransmission at central synapses remains unknown. To elucidate this, we performed direct observations of SV reloading and mobility at a single-vesicle level near the plasma membrane in cerebellar mossy fiber terminals using total internal reflection fluorescence microscopy, together with simultaneous recordings of membrane fusion by capacitance measurements. We found that actin disruption abolished the rapid SV recruitment and reduced sustained release. In contrast, induction of actin polymerization and stabilization did not affect vesicle recruitment and release, suggesting that the presence of actin filaments, rather than actin dynamics, was required for the rapid recruitment. Single-particle tracking experiments of quantum dot-labeled vesicles, which allows nanoscale resolution of vesicle mobility, revealed that actin disruption caused vesicles to diffuse more rapidly. Hidden Markov modeling with Bayesian inference revealed that SVs had two diffusion states under normal conditions: free-diffusing and trapped. After disruption of the actin filament, vesicles tended to have only the free-diffusing state. F-actin staining showed that actin filaments were localized outside the active zones (AZs) and surrounded some SV trajectories. Perturbation of SV mobility, possibly through interference with biomolecular condensates, also suggested that the restricted diffusion state determined the rate of SV recruitment. We propose that actin filaments confined SVs near the AZ to achieve rapid and efficient recruitment followed by priming and sustained synaptic transmission.

Mathematics behind the identifying CpG islands

The major objective of the paper is to review the theory for an hidden Markov model, a very general type of probabilistic model for sequences of symbols. In order for the hidden Markov model to be applicable to real-world applications, three key problems about the model must be addressed, and to do this, first we go over how to choose the best state sequence to explain an observation sequence, then we go over how to calculate the probability of an observation sequence, and finally we go over how to compute the maximization of the probability of the observation sequence. From these three angles, we review the mathematical concept behind the identification of CpG islands. The entire process and study of the outcomes have been tackled by examining both hypothetical and real DNA sequences side by side. We use well-known biological sequence analysis servers to carry out the experiment. Analytical and algorithmic approaches are compared while taking the hypothetical DNA sequence example into consideration.

Accurate identification of locally aneuploid cells by incorporating cytogenetic information in single cell data analysis

Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. PartCNVH further improves partCNV by integrating a hidden Markov model for feature selection. We have thoroughly evaluated the performance of partCNV and partCNVH through simulation studies and real data analysis using three scRNA-seq datasets from blood cancer patients. Our results show that partCNV and partCNVH have favorable accuracy and provide more interpretable results compared to existing methods. In the real data analysis, we have identified multiple biological processes involved in the oncogenesis of myelodysplastic syndromes and acute myeloid leukemia.