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RISS 인기검색어
- 검색키워드
- 저자 : Varun Bajaj (검색결과 2 건)
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Varun Bajaj,Ram Bilas Pachori 대한의용생체공학회 2013 Biomedical Engineering Letters (BMEL) Vol.3 No.1
Purpose Epileptic seizure is generated by abnormal synchronization of neurons of the cerebral cortex of the patients,which is commonly detected by electroencephalograph (EEG)signals. In this paper, the intracranial EEG signals have been used to detect focal temporal lobe epilepsy. Methods This paper presents a new method based on empirical mode decomposition (EMD) of EEG signals for detection of epileptic seizures. The proposed method uses the Hilbert transformation of intrinsic mode functions (IMFs),obtained by EMD process that provides analytic signal representation of IMFs. The instantaneous area measured from the trace of the windowed analytic IMFs of EEG signals provides rules-based detection of focal temporal lobe epilepsy. Results The experiment results on intracranial EEG signals are included to show the effectiveness of the proposed method for detection of focal temporal lobe epilepsy. The performance evaluation of the proposed method for epileptic seizure detection has performed by computing the sensitivity (SEN),specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and error rate detection (ERD). Conclusions The proposed method has been compared to the existing methods for detecting focal temporal lobe epilepsy from intracranial EEG signals. The proposed method has provided detection of focal temporal lobe epilepsy with increased accuracy.
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A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm
Santhos Kumar Avuti,Varun Bajaj,Anil Kumar,Girish Kumar Singh 대한의용생체공학회 2019 Biomedical Engineering Letters (BMEL) Vol.9 No.4
Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected afterexcluding the pectoral muscle from mammogram images. Hence, it is very signifi cant to identify and segment the pectoralmuscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetismoptimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among thecharges to develop the members of a population. Here, both Kapur’s and Otsu based cost functions are employed with EMOseparately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimalthreshold levels can be identifi ed for the considered mammographic image. The proposed methodology is applied on all thethree twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentationof the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found tobe robust for variations in the pectoral muscle.
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