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Seyed Mohammad Arash Taghavi

Bio: Seyed Mohammad Arash Taghavi is an academic researcher from Isfahan University of Medical Sciences. The author has contributed to research in topics: Approximate entropy. The author has an hindex of 2, co-authored 2 publications receiving 36 citations.

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Journal Article
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27 citations

Journal Article
TL;DR: Several discriminative features including AR coefficients, band power, fractal dimension, and approximation entropy (ApEn) were chosen to extract quantitative values from the EEG signals to distinguish the control subjects from the schizophrenic patients.
Abstract: Objectives: Diagnosis of the psychiatric diseases is a bit challenging at the first interview due to this fact that qualitative criteria are not as accurate as quantitative ones. Here, the objective is to classify schizophrenic patients from the healthy subject using a quantitative index elicited from their electroencephalogram (EEG) signals. Methods: Ten right handed male patients with schizophrenia who had just auditory hallucination and did not have any other psychotic features and ten age-matched right handed normal male control participants participated in this study. The patients used haloperidol to minimize the drug-related affection on their EEG signals. Electrophysiological data were recorded using a Neuroscan 24 Channel Synamps system, with a signal gain equal to 75K (150 xs at the headbox). According to the observable anatomical differences in the brain of schizophrenic patients from controls, several discriminative features including AR coefficients, band power, fractal dimension, and approximation entropy (ApEn) were chosen to extract quantitative values from the EEG signals. Results: The extracted features were applied to support vector machine (SVM) classifier that produced 88.40% accuracy for distinguishing the two groups. Incidentally, ApEn produces more discriminative information compare to the other features. Conclusion: This research presents a reliable quantitative approach to distinguish the control subjects from the schizophrenic patients. Moreover, other representative features are implemented but ApEn produces higher performance due to complex and irregular nature of EEG signals. Declaration of interest: None. Citation: Taghavi M, Boostani R, Sabeti M, TaghaviSMA. Usefulness of approximate entropy in the diagnosis of schizophrenia. Iran J Psychiatry Behav Sci 2011; 5(2): 62-70.

11 citations


Cited by
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TL;DR: The findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes and expects that nonlinear analysis will give us deeper understanding of schizophrenics' brain.
Abstract: Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.

72 citations

Journal ArticleDOI
TL;DR: This work presents a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, performs its security analysis, and demonstrates its security characteristics.
Abstract: Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

54 citations

Journal ArticleDOI
TL;DR: The self-compassion, social support and Sense of belonging are effective on the resilience among Iranian women with breast cancer and it is recommended to design some interventional programs to increase the aspect of resilience in these patients.
Abstract: Background: The purpose of this study was to investigate the associations among Resilience, self- compassion, social support and Sense of belonging in Iranian women with breast cancer. Materials and methods: This study was a descriptive-analytical cross-sectional study .The data of 150 patients with breast cancer were collected by convenience sampling using Demographic characteristics questionnaire, Connor-Davidson resilience scale, self-compassion scale and the multidimensional scale of perceived social support in Urmia, Iran in 2016. Results: The most age of the patients were in the range of 41-49 years, and most of them were married. The self- compassion, social support and Sense of belonging (r = all correlated significantly with resilience). Significant positive correlation was identified among self-compassion, social support, sense of belonging and resilience (P < 0.01). Conclusions: The results of this study clarified the self-compassion, social support and Sense of belonging are effective on the resilience among Iranian women with breast cancer. It is recommended to design some interventional programs to increase the aspect of resilience in these patients.

44 citations

Journal ArticleDOI
TL;DR: A review of machine learning-based methods for schizophrenia classification using EEG data is presented in this paper, where the authors discuss their potentialities and limitations, as well as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia.

34 citations

Journal ArticleDOI
27 Jul 2015-Entropy
TL;DR: The findings suggest that the activation response is weakly phase-locked to stimulus onset in SCH and related to the default mode and salience networks.
Abstract: The aim of the present study was to characterize the neural network reorganization during a cognitive task in schizophrenia (SCH) by means of wavelet entropy (WE). Previous studies suggest that the cognitive impairment in patients with SCH could be related to the disrupted integrative functions of neural circuits. Nevertheless, further characterization of this effect is needed, especially in the time-frequency domain. This characterization is sensitive to fast neuronal dynamics and their synchronization that may be an important component of distributed neuronal interactions; especially in light of the disconnection hypothesis for SCH and its electrophysiological correlates. In this work, the irregularity dynamics elicited by an auditory oddball paradigm were analyzed through synchronized-averaging (SA) and single-trial (ST) analyses. They provide complementary information on the spatial patterns involved in the neural network reorganization. Our results from 20 healthy controls and 20 SCH patients showed a WE decrease from baseline to response both in controls and SCH subjects. These changes were significantly more pronounced for healthy controls after ST analysis, mainly in central and frontopolar areas. On the other hand, SA analysis showed more widespread spatial differences than ST results. These findings suggest that the activation response is weakly phase-locked to stimulus onset in SCH and related to the default mode and salience networks. Furthermore, the less pronounced changes in WE from baseline to response for SCH patients suggest an impaired ability to reorganize neural dynamics during an oddball task.

28 citations