M. Bedeeuzzaman

Aligarh Muslim University, Koil, Uttar Pradesh, India

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Publications (9)8.23 Total impact

  • M. Bedeeuzzaman · Thasneem Fathima · Yusuf U. Khan · Omar Farooq
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    ABSTRACT: Researches indicate that electrophysiological changes develop minutes to hours before the actual onset of epileptic seizures due to abnormal neuronal discharges. These precursors perceived through symptoms like sleep problems or headaches are observable from the analysis of the intracranial electroencephalogram (iEEG). It can be utilized as a major tool for seizure prediction well in advance. In this work an algorithm with a statistical feature set consisting of mean absolute deviation (MAD) and inter quartile range (IQR) is proposed to predict epileptic seizures. A linear classifier has been used to find the seizure prediction time in preictal iEEGs. A sensitivity of 100% with zero false positive rate (FPR) in 12 patients and very low values of FPR for the rest were achieved using widely used Freiburg iEEG dataset. Average prediction time varies between 51 and 96 min.
    Biomedical Signal Processing and Control 03/2014; 10:338–341. DOI:10.1016/j.bspc.2012.12.001 · 1.53 Impact Factor
  • Thasneem Fathima · M. Bedeeuzzaman · Paul K. Joseph
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    ABSTRACT: Electroencephalogram (EEG) is the major diagnostic tool used for analyzing the human epileptic seizure activity and there is a strong need of an efficient automatic seizure detection using it to ease the diagnosis. In this paper a method of classification of EEG signals using wavelet based features is presented. The wavelet decomposition was done up to fourth level, followed by the calculation of inter quartile range (IQR), an important statistical feature, over third and fourth level wavelet coefficients. The methodology was applied to five types of EEG signals: healthy subjects (eyes open and eyes closed), epileptic subjects during seizure free interval (interictal EEG from epileptogenic zone and opposite hemisphere of epileptogenic zone) and epileptic subjects during a seizure (ictal EEG). A linear classifier trained on these features could classify normal and ictal EEG signals with 100% sensitivity and specificity. The overall accuracy obtained for five classes was 95.6%.
    Journal of Medical Imaging and Health Informatics 06/2013; 3(2):301-305. DOI:10.1166/jmihi.2013.1161 · 0.62 Impact Factor
  • M. Bedeeuzzaman · Thasneem Fathima · Yusuf U. Khan · Omar Farooq
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    ABSTRACT: One of the most troubling features of epileptic seizure is its apparently erratic nature. Researches point to some changes in dynamical properties of electroencephalogram (EEG) indicative of epileptic seizures. If these changes could be properly detected, they could be used to predict seizures. Prior knowledge regarding an oncoming seizure can be used to actuate some intervention mechanism to prevent or control seizure. A method of automatic seizure prediction using wavelet entropy (WE) and mean absolute deviation (MAD) is described in this paper. A linear classifier has been used to classify the preictal and interictal EEGs. In order to assess the effectiveness of the proposed method, tests have been performed using widely used Freiburg EEG dataset. Results showed that this method could predict all the seizures, providing 100% sensitivity. Average prediction time (APT) found to vary between 52 minutes to 99 minutes.
    Journal of Medical Imaging and Health Informatics 09/2012; 2(3):238-243. DOI:10.1166/jmihi.2012.1090 · 0.62 Impact Factor
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    M. Bedeeuzzaman · Omar Farooq · Yusuf U Khan
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    ABSTRACT: The statistical properties of seizure EEG are found to be different from that of the normal EEG. This paper ascertains the efficacy of inter quartile range (IQR), a median based measure of statistical dispersion, as a discriminating feature that can be used for the classification of EEG signals into normal, interictal and ictal classes. IQR along with variance and entropy are calculated for each frame of EEG. To reduce the feature vector size, standard statistical features such as mean, minimum, maximum and standard deviation were evaluated and were given as input to a linear classifier. Without resorting to any kind of transformation, the proposed method reduces the computational complexity and achieves a classification accuracy of 100%.
    International Journal of Computer Applications 04/2012; 44(11):1-5. DOI:10.5120/6304-8614 · 0.82 Impact Factor
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    M. Bedeeuzzaman · O. Farooq · Y.U. Khan
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    ABSTRACT: Electroencephalogram (EEG) is an important technique for detecting epileptic seizures. In this paper a method of classification of EEG signal into normal, interictal and ictal classes is presented. Statistical measures such as median absolute deviation (MAD), variance and entropy showing the dispersion and rhythmicity, were calculated for each frame of EEG signals. The classification was done using a linear classifier. The direct time domain approach adopted without resorting into any kind of transformations yields an accuracy of 100%.
    Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on; 06/2011
  • T. Fathima · Y.U. Khan · M. Bedeeuzzaman · O. Farooq
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    ABSTRACT: Epilepsy is characterized by the sudden and recurrent neuronal firing in the brain. It can be detected by analyzing Electroencephalogram (EEG) of the subject. In this paper, a method of classification of EEG signals into normal and seizure classes is presented. Features based on the statistical distributions were calculated for each frame of EEG signals. After ranking the features using Fisher's discriminant analysis variance, skewness and coefficient of variation (CoV) were found to form the best set of features. Classification was done using linear classifier which showed an accuracy of 96.9%.
    Devices and Communications (ICDeCom), 2011 International Conference on; 03/2011
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    Thasneem Fathima · M Bedeeuzzaman · Omar Farooq · U Khan
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    ABSTRACT: Electroencephalogram (EEG) is a medical imaging technique that records the electrical activity in the brain. Epilepsy, the most common neurological disorder characterized by sudden and recurrent neuronal firing in the brain can be detected by analyzing EEG of the subject. This paper illustrates the use of wavelet based features for the classification between normal and seizure EEG signals. The EEG signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. Normal and seizure signals were classified using a linear classifier with an accuracy of 99.5%.
  • M. Bedeeuzzaman · Omar Farooq · Yusuf U Khan
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    ABSTRACT: Electro encephalogram (EEG) is a widely used tool for the clinical investigation of epileptic seizures. A new scheme of epileptic seizure detection using statistical features and Discrete Cosine Transform (DCT) is presented in this paper. Median absolute deviation (MAD) and variance is taken as the discriminating features between three different classes of EEG under study. The DCT was used for feature reduction, whose ability to pack input data into as few coefficients as possible makes it a good choice for the purpose. The representative DCT coefficients were given as the input to a linear classifier to yield 100% accuracy.
  • M. Bedeeuzzaman · O. Farooq · YU Khan