scholarly journals A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque LFP and human EEG

2020 ◽  
Author(s):  
Indie C. Garwood ◽  
Sourish Chakravarty ◽  
Jacob Donoghue ◽  
Pegah Kahali ◽  
Shubham Chamadia ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Subjects ◽  
Slow Oscillation ◽  
State Transitions ◽  
State Process ◽  
The Mean ◽  
Eeg Recordings ◽  
Gamma Burst

AbstractKetamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes bursts of 30-50 Hz oscillations alternating with 0.1 to 10 Hz oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in 10 Hz frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma burst and slow oscillation activity, as well as intermediate states in between. The mean duration of the gamma burst state was 2.5s([1.9,3.4]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.7s([1.9,3.8]s) for the human subjects. The mean duration of the slow oscillation state was 1.6s([1.1,2.5]s) and 0.7s([0.6,0.9]s) for the two NHPs, and 2.8s([1.9,4.3]s) for the human subjects. Our beta-HMM framework provides a useful tool for experimental data analysis. Our characterizations of the gamma-burst process offer detailed, quantitative constraints that can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

scholarly journals A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

2021 ◽  
Vol 17 (8) ◽  
pp. e1009280
Author(s):  
Indie C. Garwood ◽  
Sourish Chakravarty ◽  
Jacob Donoghue ◽  
Meredith Mahnke ◽  
Pegah Kahali ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Local Field ◽  
Hidden Markov ◽  
Human Subjects ◽  
Local Field Potentials ◽  
Field Potentials ◽  
State Process ◽  
The Mean ◽  
Eeg Recordings

Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

scholarly journals Physical Context Detection using Wearable Wireless Sensor Networks

2008 ◽  
Vol 4 (3) ◽  
pp. 191 ◽  
Author(s):  
Muhannad Quwaider ◽  
Subir Biswas
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Subjects ◽  
Real Life ◽  
Sensor Data ◽  
Physical Context ◽  
Significant Extension ◽  
The Individual ◽  
Stochastic Identification

This paper presents the architecture of a wearable sensor network and a Hidden Markov Model (HMM) processingframework for stochastic identification of body postures andphysical contexts. The key idea is to collect multi-modal sensor data from strategically placed wireless sensors over a human subject’s body segments, and to process that using HMM in order to identify the subject’s instantaneous physical context. The key contribution of the proposed multi-modal approach is a significant extension of traditional uni-modal accelerometry in which only the individual body segment movements, without their relative proximities and orientation modalities, is used for physical context identification. Through real-life experiments with body mounted sensors it is demonstrated that while the unimodal accelerometry can be used for differentiating activityintensive postures such as walking and running, they are not effective for identification and differentiation between lowactivity postures such as sitting, standing, lying down, etc. In the proposed system, three sensor modalities namely acceleration, relative proximity and orientation are used for context identification through Hidden Markov Model (HMM) based stochastic processing. Controlled experiments using human subjects are carried out for evaluating the accuracy of the HMMidentified postures compared to a naïve threshold based mechanism over different human subjects.

3D Object Classification Using a Two-Dimensional Hidden Markov Model

2013 ◽  
Vol 411-414 ◽  
pp. 2041-2046 ◽  
Author(s):  
Jing Guo ◽  
Ming Quan Zhou ◽  
Chao Li ◽  
Zhe Shi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Object Classification ◽  
Posteriori Probability ◽  
Two Dimensional ◽  
3D Object ◽  
Feature Vectors ◽  
State Process ◽  
3D Object Classification

In this paper, we develop a novel method of 3D object classification based on a Two-Dimensional Hidden Markov Model (2D HMM). Hidden Markov Models are a widely used methodology for sequential data modeling, of growing importance in the last years. In the proposed approach, each object is decomposed by a spiderweb model and a shape function D2 is computed for each bin. These feature vectors are then arranged in a sequential fashion to compose a sequence vector, which is used to train HMMs. In 2D HMM, we assume that feature vectors are statistically dependent on an underlying state process which has transition probabilities conditioning the states of two neighboring bins. Thus the dependency of two dimensions is reflected simultaneously. To classify an object, the maximized posteriori probability is calculated by a given model and the observed sequence of an unknown object. Comparing with 1D HMM, the 2D HMM gets more information from the neighboring bins. Analysis and experimental results show that the proposed approach performs better than existing ones in database.

scholarly journals A HIDDEN MARKOV MODEL FOR THE DETECTION OF PURE AND MIXED STRATEGY PLAY IN GAMES

2014 ◽  
Vol 31 (4) ◽  
pp. 729-752 ◽  
Author(s):  
Jason Shachat ◽  
J. Todd Swarthout ◽  
Lijia Wei
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Repeated Games ◽  
Mixed Strategy ◽  
Hidden Markov ◽  
Human Subjects ◽  
Mixed Strategies ◽  
Mixed Strategy Nash Equilibrium ◽  
Equilibrium Profile ◽  
Nonstationary Dynamics

We propose a statistical model to assess whether individuals strategically use mixed strategies in repeated games. We formulate a hidden Markov model in which the latent state space contains both pure and mixed strategies. We apply the model to data from an experiment in which human subjects repeatedly play a normal form game against a computer that always follows its part of the unique mixed strategy Nash equilibrium profile. Estimated results show significant mixed strategy play and nonstationary dynamics. We also explore the ability of the model to forecast action choice.

scholarly journals A lineage tree-based hidden Markov model to quantify cellular heterogeneity and plasticity

2021 ◽  
Author(s):  
Farnaz Mohammadi ◽  
Shakthi Visagan ◽  
Sean M Gross ◽  
Luka Karginov ◽  
JC Lagarde ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Single Cell ◽  
Cell Fate ◽  
Drug Response ◽  
Hidden Markov ◽  
Single Cells ◽  
State Transitions ◽  
Cycle Phase ◽  
Cell Heterogeneity

Cell plasticity, or the ability of cells within a population to reversibly alter their phenotype, is an important feature of tissue homeostasis during processes such as wound healing and cancer. Plasticity operates alongside other sources of cell-to-cell heterogeneity such as genetic mutations and variation in signaling. Ultimately these processes prevent most cancer therapies from being curative. The predominant methods of quantifying tumor-drug response operate on snapshot population-level measurements and therefore lack evolutionary dynamics, which are particularly critical for dynamic processes such as plasticity. Here we apply a tree-based adaptation of a hidden Markov model (tHMM) that employs single cell lineages as input to learn the characteristic patterns of single cell heterogeneity and state transitions in an unsupervised fashion. This model enables single cell classification based on the phenotype of individual cells and their relatives for improved specificity in pinpointing the structure and dynamics of variability in drug response. Integrating this model with a modular interface for defining observed phenotypes allows the model to easily be adapted to any phenotype measured in single cells. To benchmark our model, we paired cell fate with either cell lifetimes or individual cell cycle phase lengths (G1 and S/G2) as our observed phenotypes on synthetic data and demonstrated that the model successfully classifies cells within experimentally tractable dataset sizes. As an application, we analyzed experimental measurements of cell fate and phase duration in cancer cell populations treated with chemotherapies to determine the number of distinct subpopulations. In total, this tHMM framework allows for the flexible classification of single cell heterogeneity across lineages.

scholarly journals Approach to Risk Performance Reasoning with Hidden Markov Model for Bauxite Shipping Process Safety by Handy Carriers

2020 ◽  
Vol 10 (4) ◽  
pp. 1269
Author(s):  
Jianjun Wu ◽  
Yongxing Jin ◽  
Shenping Hu ◽  
Jiangang Fei ◽  
Yuanqiang Zhang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Performance Monitoring ◽  
Hidden Markov ◽  
Current Data ◽  
Process Safety ◽  
State Process ◽  
Observation Process ◽  
Proposed Model ◽  
Underlying Risk

An approach based on the hidden Markov model (HMM) is proposed for risk performance reasoning (RPR) for the bauxite shipping process by Handy carriers. The unobservable (hidden) state process in the approach aims to model the underlying risk performance, while the observation process was formed from the time series of risk factors. Within the framework, the log-likelihood probability was used as the measure of similarity between historical and current data of risk reasoning factors. Based on scalar quantization regulation and risk performance quantization regulation, the RPR approach with different step sizes was conducted on the operational case, the performance of which was evaluated in terms of effectiveness and accuracy. The reasoning performance of the HMM was tested during the validation period using three simulated scenarios and one accident scenario. The results showed significant improvement in the reasoning capacity, and satisfactory performance for numerical risk reasoning and categorical performance reasoning. The proposed model is able to provide a reference for risk performance monitoring and threat pre-warning during the bauxite shipping process.

scholarly journals Pairs Trading Strategy for A and H Shares Based on Kalman-HMM Approach

2021 ◽  
Vol 4 (5) ◽  
pp. 8-16
Author(s):  
Ming Zang
Keyword(s):  
Markov Model ◽  
Financial Markets ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Real Data ◽  
State Transitions ◽  
Institutional Changes ◽  
Pairs Trading ◽  
Statistical Arbitrage ◽  
Arbitrage Strategy

Pairs trading is a statistical arbitrage strategy that takes advantage of unbalanced financial markets. A common difficulty for quantitative trading participants is the detection of market institutional changes in financial markets. In order to solve this issue, the hidden Markov model (HMM) is applied for status detection. The research objective is to use Kalman filter to predict and the hidden Markov model (HMM) to identify state transitions on the basis of screening transaction pairs with obvious co-integration relationship. This research would prove the profitability of the strategy and the ability to resist risk through the combination of these two methods with real data. The empirical results showed that compared with the traditional cointegration strategy, the holding yield increased from 1.6% to 16.2% and the maximum pullback reduced to 0.02%. Further research is required to improve trading rules.

REVERSE ENGINEERING A SOCIAL AGENT-BASED HIDDEN MARKOV MODEL — ViSAGE

2008 ◽  
Vol 18 (06) ◽  
pp. 491-526 ◽  
Author(s):  
HUNG-CHING (JUSTIN) CHEN ◽  
MARK GOLDBERG ◽  
MALIK MAGDON-ISMAIL ◽  
WILLIAM A. WALLACE
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Social Evolution ◽  
Driving Forces ◽  
Hidden Markov ◽  
Group Structure ◽  
Identification Problem ◽  
State Transitions ◽  
Agent Based ◽  
Social Agent

We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.

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