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RISS 인기검색어
- 검색키워드
- 저자 : Ram Bilas Pachori (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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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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Omkishor Sahu,Vijay Anand,Vivek Kanhangad,Ram Bilas Pachori 대한의용생체공학회 2015 Biomedical Engineering Letters (BMEL) Vol.5 No.4
Purpose Automated classification of brain magnetic resonance(MR) images has been an extensively researched topic inbiomedical image processing. In this work, we propose anew approach for classifying normal and abnormal brain MRimages using bi-dimensional empirical mode decomposition(BEMD) and autoregressive (AR) model. Methods In our approach, brain MR image is decomposedinto four intrinsic mode functions (IMFs) using BEMD andAR coefficients from multiple IMFs are concatenated toform a feature vector. Finally a binary classifier, least-squaressupport vector machine (LS-SVM), is employed to discriminatebetween normal and abnormal brain MR images. Results The proposed technique achieves 100% classificationaccuracy using second-order AR model with linear andradial basis function (RBF) as kernels in LS-SVM. Conclusions Experimental results confirm that the performanceof the proposed method is quite comparable with the existingresults. Specifically, the presented approach outperformsone-dimensional empirical mode decomposition (1D-EMD)based classification of brain MR images.
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Dada Saheb Ramteke,Anand Parey,Ram Bilas Pachori 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.12
Gearboxes have an important role in power transmission systems. For such systems, vibration-based fault diagnosis techniques are frequently used to prevent premature failure and to ensure smooth transmission. We automated the fault diagnosis of gears having level of wear fault at micron using variational mode decomposition (VMD). VMD has been applied iteratively with specific input parameters. VMD decomposes the gear vibration signal into different narrowband components (NBCs) or obtained components (OCs). Various statistical features, namely kurtosis, skewness, standard deviation, root mean square, and crest factor, were extracted from the different OCs. Kruskal-Wallis test based on probability values was used to identify the significant features. For the automation of fault detection system, a comparative study was done using the random forest, multilayer perceptron, and J48 classifiers. The proposed method exhibits 96.5 % accuracy using random forest classifier with combined kurtosis, skewness, and standard deviation features.
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