Pediatric Seizure Forecasting using Nonlinear Features and Gaussian Mixture Hidden Markov Models on Scalp EEG Signals

2018 ◽  
Author(s):  
Carlos Emiliano Solorzano-Espindola ◽  
Blanca Tovar-Corona ◽  
Alvaro Anzueto-Rios
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Nonlinear Features

Supported Diagnosis of Attention Deficit and Hyperactivity Disorder from EEG Based on Interpretable Kernels for Hidden Markov Models

2022 ◽  
Author(s):  
M. C. Maya-Piedrahita ◽  
P. M. Herrera-Gomez ◽  
L. Berrío-Mesa ◽  
D. A. Cárdenas-Peña ◽  
A. A. Orozco-Gutierrez
Keyword(s):  
Hidden Markov Models ◽  
Attention Deficit ◽  
Markov Models ◽  
Hidden Markov ◽  
Stop Signal Task ◽  
Support Vector ◽  
Stochastic Dynamic ◽  
Eeg Signals ◽  
Accuracy Rate ◽  
Hyperactivity Disorder

As a neurodevelopmental pathology, Attention Deficit Hyperactivity Disorder (ADHD) mainly arises during childhood. Persistent patterns of generalized inattention, impulsivity, or hyperactivity characterize ADHD that may persist into adulthood. The conventional diagnosis relies on clinical observational processes yielding high rates of overdiagnosis due to varying interpretations among specialists or missing information. Although several studies have designed objective behavioral features to overcome such an issue, they lack significance. Despite electroencephalography (EEG) analyses extracting alternative biomarkers using signal processing techniques, the nonlinearity and nonstationarity of EEG signals restrain performance and generalization of hand-crafted features. This work proposes a methodology to support ADHD diagnosis by characterizing EEG signals from hidden Markov models (HMM), classifying subjects based on similarity measures for probability functions, and spatially interpreting the results using graphic embeddings of stochastic dynamic models. The methodology learns a single HMM for EEG signal from each patient, so favoring the inter-subject variability. Then, the Probability Product Kernel, specifically developed for assessing the similarity between HMMs, fed a support vector machine that classifies subjects according to their stochastic dynamics. Lastly, the kernel variant of Principal Component Analysis provided a means to visualize the EEG transitions in a two-dimensional space, evidencing dynamic differences between ADHD and Healthy Control children. From the electrophysiological perspective, we recorded EEG under the Stop Signal Task modified with reward levels, which considers cognitive features of interest as insufficient motivational circuits recruitment. The methodology compares the supported diagnosis in two EEG channel setups (whole channel set and channels of interest in frontocentral area) and four frequency bands (Theta, Alpha, Beta rhythms, and a wideband). Results evidence an accuracy rate of 97.0% in the Beta band and in the channels where previous works found error-related negativity events. Such accuracy rate strongly supports the dual pathway hypothesis and motivational deficit concerning the pathophysiology of ADHD. It also demonstrates the utility of joining inhibitory and motivational paradigms with dynamic EEG analysis into a noninvasive and affordable diagnostic tool for ADHD patients.

Classification of EEG Signals during Five Mental Tasks using Hidden Markov Models

2010 ◽  
Vol 20 (1) ◽  
pp. 45-60
Author(s):  
E. El-madbouly ◽  
T. Al-ani ◽  
S. Helmy ◽  
Y. Hamam ◽  
A.A. Nasser
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Eeg Signals ◽  
Mental Tasks

scholarly journals Hidden Markov Modeling of Human Pivoting

2007 ◽  
Vol 19 (4) ◽  
pp. 444-447 ◽  
Author(s):  
Yusuke Maeda ◽  
◽  
Tatsuya Ushioda ◽  
Keyword(s):  
Multidimensional Scaling ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Minimum Description Length ◽  
Human Movement ◽  
Gaussian Mixture ◽  
Markov Modeling ◽  
Metric Multidimensional Scaling ◽  
Hidden Markov Modeling

In modeling human movement using hidden Markov models (HMM), the “optimal” HMM with an appropriate number of states is determined based on the minimum description length (MDL) criterion. Human pivoting, typifying graspless manipulation, is modeled using Gaussian mixture HMMs. Analyzing the obtained HMMs using metric multidimensional scaling (MDS) showed the features of individual movement. Such dissimilarity analysis can be used to validate models of tacit skills in human manipulation.

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