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RISS 인기검색어
- 검색키워드
- 저자 : Yatindra Kumar (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Relative Wavelet Energy and Wavelet Entropy Based Epileptic Brain Signals Classification
Yatindra Kumar,Mohan Lal Dewal,Radhey Shyam Anand 대한의용생체공학회 2012 Biomedical Engineering Letters (BMEL) Vol.2 No.3
Purpose Manual analysis of EEG signals by an expert is very much time consuming due to the long length of EEG recordings. The suitable computerized analysis is essentially required to differentiate among the normal, interictal and ictal (epileptic) EEGs. Methods In the present work the EEG signals are decomposed into different sub-bands using discrete wavelet transform (DWT) to obtain the detail and the approximation wavelet coefficients. The coefficients are used to calculate the quantitative values of relative wavelet energy and wavelet entropy from different data sets to select the features of EEG signals. The support vector machine (SVM), feed forward back- propagation neural network (FFBPNN), k-Nearest Neighbor Classifier (k-NN) and Decision tree classifier (DT) are used to classify the EEG signals. Results It is revealed that the accuracy between normal subjects with eyes open condition (data set A) epileptic data set E using SVM is obtained as 96.25%. Classification accuracy between the normal subjects with eye closed condition and epileptic data set E is obtained as 83.75%using k-NN classifier. Similar accuracies while discriminating the interictal data set C versus ictal data set E, and interictal data set D versus ictal data set E are obtained as 97.5% and 97.5% respectively, using a FFBPNN. These accuracies are quite higher than the earlier results published. The results are discussed quite in detail towards the last sections of the present paper. Conclusions Our experimental results demonstrate that the proposed method gives quite high statistical parameters for EEG classifications especially to classify the interictal data(C, D) and ictal data (E). These experiments indicate that the present method can be useful in analyzing and detecting the EEG signal associated with epilepsy.
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Preliminary studies on the microplastic pollution in Dal lake, Kashmir (first report)
Firdous, Juhi,Mathur, Yatindra Kumar,Jeelani, Mubashir,Aziz, Adnan,Azmat, Seema,Mudasir, Syeed Techno-Press 2020 Advances in environmental research Vol.9 No.4
We provide the first study on the occurrence of microplastics in Dal lake, Kashmir, India. Microplastics act as catastrophe that trigger many environmental problems. The key origins of microplastics are larger plastics, which split into smaller plastics after UV light disintegration. There is relatively little work carried out on the existence of microplastics. The present work has been undertaken on the occurrence of microplastics at four pre-selected sites (surface water) in Dal lake, Kashmir. The samples were taken to the laboratory to dissolve organic matter by using H2O2 (6%). To speed up the organic digestion; the treated mixture was heated on a hot plate at 70℃. The mixture was then subjected to density separation. The supernatant obtained was observed under the microscope (10X) and measurements were taken. At site-I, the microplastics ranged from 2-3 mm, site-II 5-6 mm, site-III 3-4 mm, site-IV 4-5 mm in thickness, indicating the presence of microplastics in the lake. The presence of microplastics indicated that the lake has undergone an anthropogenic change over a period of time. Our research highlights the value of enhancing the quality of the drainage system and sewage disposal. This work can be helpful to recognize successful microplastic control management techniques and possible threats associated with the Dal lake. So far, no such data on the presence of microplastics in Kashmir lakes is available.
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