Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network
Manisha Chandani,
Arun Kumar
Issue:
Volume 3, Issue 5, October 2017
Pages:
38-43
Received:
28 June 2017
Accepted:
17 July 2017
Published:
24 October 2017
Abstract: EEG (Electroencephalogram) is a technique for identifying neurological disorders. There are various neurological disorders like Epilepsy, brain cancer, etc. Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non stationary signals like EEG. In this work, neural network analysis (NNA) based classifier was employed to detect epileptic seizure activity from background electro encephalographs (EEGs). Two types of EEG signals (healthy subject with eye open condition, epileptic) were selected for the analysis. Signals were reprocessed, decomposed by using discrete wavelet transform DWT till 5th level of decomposition tree. Various features like mean. Standard deviation, median, entropy, kurtosis and skewness were computed and consequently used for classification of signals. The range of these features in non-epileptic and epileptic group of 80 subjects each from data set is analyzed for data available at the Department of Epileptology, University of Bonn, and the parameters with distinct non-overlapping zone are identified. The results show the promising classification accuracy of nearly 100% in detection of abnormal from normal EEG signals. The main purpose of this new approach is that the computation time of NNA classifier is less to provide better accuracy. This proposed classifier can be used to design expert system for epilepsy diagnosis purpose in various hospitals.
Abstract: EEG (Electroencephalogram) is a technique for identifying neurological disorders. There are various neurological disorders like Epilepsy, brain cancer, etc. Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have al...
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Trapezius Flap Reconstruction of Scalp Defect After Removal of Occipital Fibrosarcoma in Neurofibromatosis Type I Patient
Kantenga Dieu Merci Kabulo,
Patrice Ntenga,
Kelvin Nemayire,
Nathanael Harunangoni Zimani,
Aaron Musara,
Sitshengiso Matshalaga,
Kusezweni Kevin Nduku,
Rudo Makunike-Mutasa,
Kazadi Kaluile Ntenga Kalangu
Issue:
Volume 3, Issue 5, October 2017
Pages:
44-48
Received:
27 August 2017
Accepted:
11 September 2017
Published:
28 October 2017
Abstract: Neurofibromatosis type 1 (NF1) is an autosomal dominant condition affecting approximately 1 in 3000 live births. The manifestations of this condition are extremely variable, even within families, and genetic counseling is consequently difficult with regard to prognosis. Individuals with NF1 are acknowledged to be at increased risk of malignancy. Several studies have previously attempted to quantify this risk, but have involved relatively small study populations. Soft tissue tumors represent a heterogeneous group of mesenchymal and neural lesions. We report a case of giant scalp Fibrosarcoma of the scalp in patient with neurofibromatosis type I without intracranial extension, in a 35 year old female which was excised completely along with the involved overlying skin, and reconstruction was done to cover the defect using trapezius flap and split thickness skin graft from the right thigh. She is doing well after treatment and is in regular follow up while awaiting further management by the oncologists.
Abstract: Neurofibromatosis type 1 (NF1) is an autosomal dominant condition affecting approximately 1 in 3000 live births. The manifestations of this condition are extremely variable, even within families, and genetic counseling is consequently difficult with regard to prognosis. Individuals with NF1 are acknowledged to be at increased risk of malignancy. Se...
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