Phase space in EEG signals of women refferred to meditation clinic

Goshvarpour, Ateke; Goshvarpour, Atefeh; Rahati, Saeed; Saadatian, Vahid; Morvarid, Minoo

doi:10.4236/jbise.2011.46060

Cited by 13 publications

(18 citation statements)

References 8 publications

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“…The subjects were asked not to eat salty or fat foods before meditation practices or data recording. Informed written consent was obtained from each subject after the experimental procedures had been explained [21].…”

Section: A Data Collectionmentioning

confidence: 99%

Radial Basis Function and K-Nearest Neighbor Classifiers for Studying Heart Rate Signals during Meditation

Goshvarpour¹,

Goshvarpour²

2012

IJMECS

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Abstract-Meditation refers to a family of selfregulation practices that focus on training attention and awareness in order to bring mental processes under greater voluntary control. The aim of this study is to evaluate the efficiency of two different classifiers, kNearest Neighbor (k-NN) and Radial Basis Function (RBF), on the heart rate signals in a specific psychological state. For this purpose, two types of heart rate time series (before, and during meditation) of 25 healthy women are collected in the meditation clinic in Mashhad. The spectral parameters are used to gain insight into the autonomic nervous system (ANS) response induced by meditation. Therefore, very low frequency, low frequency, high frequency, the LF/HF ratio and frequency of the highest spectral peak of heart rate signals are extracted and used as inputs of the classifiers. To evaluate performance of the classifiers, the classification accuracies and mean square error (MSE) of the classifiers were examined. The classification results of this study denote that the RBF classifier trained on spectral features obtains higher accuracy than that of k-NN classifier. The total classification accuracy of the RBF classifier is 92.3% with 0.026 classification error. However, k-Nearest Neighbor classifier gives encouraging results (86.5%). Experimental results verify that radial basis function is an efficient classifier for classifying heart rate signals in a specific psychological state.

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Section: A Data Collectionmentioning

confidence: 99%

Radial Basis Function and K-Nearest Neighbor Classifiers for Studying Heart Rate Signals during Meditation

Goshvarpour¹,

Goshvarpour²

2012

IJMECS

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“…Poincare plot or return map is a phase space where a time series is represented in a Cartesian plane [12]. A phase space allows studying variations in a signal only with respect to itself.…”

Section: Poincare Plotmentioning

confidence: 99%

Assessment of sedation-analgesia by means of poincaré analysis of the electroencephalogram

Bolaños

Vallverdú

Caminal

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Monitoring the levels of sedation-analgesia may be helpful for managing patient stress on minimally invasive medical procedures. Monitors based on EEG analysis and designed to assess general anesthesia cannot distinguish reliably between a light and deep sedation. In this work, the Poincaré plot is used as a nonlinear technique applied to EEG signals in order to characterize the levels of sedation-analgesia, according to observed categorical responses that were evaluated by means of Ramsay Sedation Scale (RSS). To study the effect of high frequencies due to EMG activity, three different frequency ranges (FR1=0.5-110 Hz, FR2=0.5-30 Hz and FR3=30-110 Hz) were considered. Indexes from power spectral analysis and plasma concentration of propofol and remifentanil were also compared with the bispectral index BIS. An adaptive Neurofuzzy Inference System was applied to model the interaction of the best indexes with respect to RSS score for each analysis, and leave-one-out cross validation method was used. The ability of the indexes to describe the level of sedation-analgesia, according with the RSS score, was evaluated using the prediction probability (Pk). The results showed that the ratio SD1/SD2FR3 contains useful information about the sedation level, and SD1FR2 and SD2FR2 had the best performance classifying response to noxious stimuli. Models including parameters from Poincaré plot emerge as a good estimator of sedation-analgesia levels.

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“…Therefore, several nonlinear indexes of EEG t ime series have been studied in healthy subjects, in various mental and physical states and in different pathologies [6][7][8][9].…”

Section: Imentioning

confidence: 99%

Analysis of Electroencephalogram Signals in Different Sleep Stages using Detrended Fluctuation Analysis

Goshvarpour

Abbasi²

2013

IJIGSP

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-Scaling behavior is an indicator of the lack of characteristic time scale, and the existence of longrange correlations related to physiological constancy preservation. To investigate the fluctuations of the sleep electroencephalogram (EEG) over various time scales during different sleep stages detrended fluctuation analysis (DFA) is studied. The sleep EEG signals for analysis were obtained fro m the Sleep-EDF Database available online at the PhysioBank. The DFA computations were performed in different sleep stages. The scaling behavior of these time series was investigated with detrended fluctuation analysis (window size: 50 to 500). The results show that the mean values of scaling exponents were lo wer in subjects during stage 4 and standard deviation of scaling exponents of stage 4 was larger than that of the other stages. In contrast, the mean value of scaling exponents of stage 2 was larger, wh ile a small variat ion of scaling exponent is observed at this stage. Therefore, DFA has a more stable behavior in stage 2, whereas the random variability and unpredictable behavior of DFA can be observed in the stage 4. In conclusion, scaling exponent indices are efficacious in quantifying EEG signals in different sleep stages.

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Phase space in EEG signals of women refferred to meditation clinic

Abstract: ABSTRACT

Cited by 13 publications

References 8 publications

Radial Basis Function and K-Nearest Neighbor Classifiers for Studying Heart Rate Signals during Meditation

Radial Basis Function and K-Nearest Neighbor Classifiers for Studying Heart Rate Signals during Meditation

Assessment of sedation-analgesia by means of poincaré analysis of the electroencephalogram

Analysis of Electroencephalogram Signals in Different Sleep Stages using Detrended Fluctuation Analysis

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