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dc.contributor.author | Khan, Faizan Reg # 31202 | |
dc.contributor.author | Shahzad, Zohaib Reg # 31266 | |
dc.contributor.author | Waseem, Hammad Reg # 25507 | |
dc.date.accessioned | 2017-06-19T07:53:45Z | |
dc.date.available | 2017-06-19T07:53:45Z | |
dc.date.issued | 2016-05 | |
dc.identifier.uri | http://hdl.handle.net/123456789/1812 | |
dc.description | Supervised by Engr Hina Shakir | en_US |
dc.description.abstract | In this research based project (EEG based hearing loss detector) newly bom children are our target audience .The purpose of this project is to detect hearing disability in newly bom through brain signals. There are three main tasks that describe complete project clearly are as follows. • Data Acquisition • Data classification • Training We acquire data of newly bom children through brain signals at different frequencies of 500 HZ 1000 HZ 5000 HZ AND 15000 HZ. In data classification we split brain signals and work on signals generated by brain for hearing purpose. Further we classify brain signals in to different waves Alpha, Beta, Gamma, Delta and theta. After classification, the system will use back propagation feed forward network to predict whether the subject is normal or deaf. The first chapter of this report covers the introductory portion which describe what this application is all about, motivation and implementation of this project. Chapter one is an overview of complete project. Second part of this report covers customer validation, surveys and market competitors of our project. This portion describe how we are better than our competitors and what unique features we are providing in this application. It also covers the complete system functionalities including functional and non-functional requirements. Prototype of the system is also mention in this section which gives an idea how the final product should look like. Feasibility and scope of the project is also discussed in this portion. We used different techniques to develop this project like Fourier analysis for signal processing wavelet transform for the classification of the signals into real and imaginary parts. This technique is used to classify signals in different type of waves. Finally for deafness prediction we used neural network. In this portion all algorithms we used are discuss in detail. The architecture and design of this application is explained in third and fourth chapter. This section include strategies of architecture and explain working of each component for each layer. Also describe integrated system how these components are making a complete system. The final chapter of this report contain implementation of the application, test we performed to validate and verify our system to explain the correctness of this application. Flow of algorithms and test cases we performed are the part of this portion. The actual code references and survey forms are given in appendices A, B and C simultaneously. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Bahria University Karachi Campus | en_US |
dc.subject | Electroencephalogram (EEG), Fourier analysis, Wavelet transform, Back propagation. Bahria | en_US |
dc.title | EEG Based Hearing Loss Detection | en_US |
dc.type | Thesis | en_US |