scholarly journals Dynamic Analysis on Simultaneous iEEG-MEG Data via Hidden Markov Model

2020 ◽  
Author(s):  
Siqi Zhang ◽  
Chunyan Cao ◽  
Andrew Quinn ◽  
Umesh Vivekananda ◽  
Shikun Zhan ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Brain Activity ◽  
Group Analysis ◽  
Network Activity ◽  
Sensor Data ◽  
Normal Brain ◽  
Alpha Power ◽  
Left And Right

Background: Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. While these recordings afford detailed information about local brain activity, putting this activity in context and comparing results across patients is challenging. Non-invasive whole-brain Magnetoencephalography (MEG) could help translate iEEG in the context of overall brain activity, and thereby aid group analysis and interpretation. Methods: Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy. Pre-processed MEG sensor data was projected to source space. The time delay embedded hidden Markov model (HMM) technique was applied to find recurrent sub-second patterns of network activity in a completely data-driven way. To relate MEG and iEEG results, correlations were computed between HMM state time courses and iEEG power envelopes in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Results: Five HMM states were inferred from MEG. Two of them corresponded to the left and right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. The majority of iEEG contacts were also located in left and right temporal areas and the theta/alpha power of the local field potentials (LFP) recorded from these contacts correlated with the time course of the HMM state corresponding to the temporal lobe of the respective hemisphere. Discussion: Our findings are consistent with the fact that most subjects were diagnosed with temporal epilepsy and implanted with temporal electrodes. As the placement of electrodes between patients was inconsistent, their modulation by HMM states could help group the contacts into functional clusters. This is the first time that HMM was applied to simultaneously recorded iEEG-MEG and our pipeline could be used in future similar studies.

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.

Detection of Irregular Behavior in Room Using Environmental Sensors and Power Consumption of Home Appliances Learning in HMMs

2017 ◽  
Vol 4 (2) ◽  
pp. 94-98
Author(s):  
ShiJie Zhao ◽  
Toshihiko Sasama ◽  
Takao Kawamura ◽  
Kazunori Sugahara
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Sensor Network ◽  
Hidden Markov ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Network System ◽  
Daily Lives

We propose a human behavior detect method based on our development system of multifunctional outlet. This is a low-power sensor network system that can recognize human behavior without any wearable devices. In order to detect human regular daily behaviors, we setup various sensors in rooms and use them to record daily lives. In this paper we present a monitoring method of unusual behaviors, and it also can be used for healthcare and so on. We use Hidden Markov Model(HMM), and set two series HMM input to recognize irregular movement from daily lives, One is time sequential sensor data blocks whose sensor values are binarized and splitted by its response. And the other is time sequential labels using Support Vector Machine (SVM). In experiments, our developed sensor network system logged 34days data. HMM learns data of the first 34days that include only usual daily behaviors as training data, and then evaluates the last 8 days that include unusual behaviors. Index Terms—multifunctional outlet system; behavior detection; hidden markov model; sensor network; support vector machine. REFERENCES [1] T.Sasama, S.Iwasaki, and T.Okamoto, “Sensor Data Classification for Indoor Situation Using the Multifunctional Outlet”, The Institute of Electrinical Engineers of Japan, vol.134(7),2014,pp.949-995 [2] M.Anjali Manikannan, R.Jayarajan, “Wireless Sensor Netwrork For Lonely Elderly Perple Wellness”, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, vol. 3, 2015, pp.41-45 [3] Nagender Kumar Suryadevara, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE SENSORS JOURNAL, VOL. 12, NO. 6, JUNE 2012, pp. 1965-1972. [4] iTec Co., safety confirmation system: Mimamorou, http://www.minamoro.biz/. [6] Alexander Schliep's group for bioinformatics, The General Hidden Markov Model library(GHMM), http://ghmm.sourceforge.net/. [7] Jr Joe H.Ward, Joumal of the American Statistical Association, vol58(301), 1963, pp236-244 [5] SOLXYZ Co., status monitoring system:Ima-Irumo, http://www.imairumo.com/.

Hidden Markov Model based driving event detection and driver profiling from mobile inertial sensor data

2015 ◽  
Author(s):  
Saurabh Daptardar ◽  
Vignesh Lakshminarayanan ◽  
Sharath Reddy ◽  
Suraj Nair ◽  
Saswata Sahoo ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Event Detection ◽  
Hidden Markov ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Model Based

scholarly journals High-order areas and auditory cortex both represent the high-level event structure of music

2021 ◽  
Author(s):  
Jamal A. Williams ◽  
Elizabeth H. Margulis ◽  
Samuel A. Nastase ◽  
Janice Chen ◽  
Uri Hasson ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Brain Activity ◽  
Brain Regions ◽  
Event Structure ◽  
Higher Order ◽  
Default Mode ◽  
High Level

AbstractRecent fMRI studies of event segmentation have found that default mode regions represent high-level event structure during movie watching. In these regions, neural patterns are relatively stable during events and shift at event boundaries. Music, like narratives, contains hierarchical event structure (e.g., sections are composed of phrases). Here, we tested the hypothesis that brain activity patterns in default mode regions reflect the high-level event structure of music. We used fMRI to record brain activity from 25 participants (male and female) as they listened to a continuous playlist of 16 musical excerpts, and additionally collected annotations for these excerpts by asking a separate group of participants to mark when meaningful changes occurred in each one. We then identified temporal boundaries between stable patterns of brain activity using a hidden Markov model and compared the location of the model boundaries to the location of the human annotations. We identified multiple brain regions with significant matches to the observer-identified boundaries, including auditory cortex, mPFC, parietal cortex, and angular gyrus. From these results, we conclude that both higher-order and sensory areas contain information relating to the high-level event structure of music. Moreover, the higher-order areas in this study overlap with areas found in previous studies of event perception in movies and audio narratives, including regions in the default mode network.Significance StatementListening to music requires the brain to track dynamics at multiple hierarchical timescales. In our study, we had fMRI participants listen to real-world music (classical and jazz pieces) and then used an unsupervised learning algorithm (a hidden Markov model) to model the high-level event structure of music within participants’ brain data. This approach revealed that default mode brain regions involved in representing the high-level event structure of narratives are also involved in representing the high-level event structure of music. These findings provide converging support for the hypothesis that these regions play a domain-general role in processing stimuli with long-timescale dependencies.

scholarly journals Hand Gesture Recognition Using Hidden Markov Model Algorithm

MATICS ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Roro Inda Melani
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gesture Recognition ◽  
Hidden Markov ◽  
Dominant Frequency ◽  
Sensor Data ◽  
Hand Gesture ◽  
Human Hand ◽  
Hand Position ◽  
The Difference

<p><em>A</em><em>bstract</em>— Recognizing human hand gesture through the use of INS (Inertial navigation System) sensor, Hidden Markov Model (HMM) was used as a tool to recognize pattern statistically. Employing INS sensor to admit data input , it is assumed that hand gesture could be detected by analizing the acceleration and fluctuation from data sensor and the difference of hand-position in 3-axis. The INS sensor that was being used was came with 6 channels to generate signals of a 3-axis gyroscope and a 3-axis accelerometer. The acceleration fluctuated in three perpendicular directions due to different hand gestures was detected by the accelerometer, while the change of hand-position in 3-axis was detected by gyroscope. Data from sensor was exported to computer via USB (Universal Serial Bus) port.</p><p class="Abstract">During the stage of data collection, a cut algorithm was developed to pick the most significant part of the sensor data. After finishing data comparison stage, DCT (Discrete Cosine Transform) was selected to transform the signal from time domain to frequency domain. Sequences of calculation were performed to analyze the best sampling frequency to select dominant frequency of every gesture to be picked as parameter value. The parameter value used in HMM as the approach to recognize and differs gestures.</p><p> </p><em>Index Terms</em>—<em>3-axis gyroscope,</em><em> accelerometer, gesture, gesture recognition, hand gesture, human gesture</em>.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing
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