Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/255880
Title: Medical Signal Analysis using Artificial Intelligence
Researcher: Shah,Chintan Pankajbhai
Guide(s): Shah Vipul A
Keywords: Brain Computer Interface, EEG, Long-Short Term Memory (LSTM), Artificial Intelligence, Inverse Projection, Boundry Element Method, Volume Conduction
Engineering and Technology,Engineering,Instruments and Instrumentation
University: Gujarat Technological University
Completed Date: 15/07/2019
Abstract: quotMedical Signals are considered as vital signs for internal operations of the human body. By closely observing and analyzing them we could infer the state of any important organs like Heart, Lung, Brain etc. From all these medical signals Electroencephalogram (EEG) is most complicated to understand. These signals are measured at the different location of head surface and contain different frequency components in them with lower amplitude than any other medical signal. Another important characteristic of EEG is that when we measure them at any newlineone location it contains signal with superimposition of signals far from measuring site. These make the most challenging signal to analyze and interpret. But if we do handle them carefully we can create an interface that can help persons with several amputees to their limbs. This interface is called Brain Computer Interface (BCI). Essentially this system is rehabilitative in nature so we can reinstate their partial limb movement. So in this research work, we have focused on EEG signals and its end application is to determine movement of the upper limb. Analysis of edical signal is done on signal attributes, which may be either frequency, amplitude, time-frequency combine or any specific event. This analysis can be done by various means but all signals are embedded with nonlinearity so, if we want to categorize the signal than artificial intelligence is best suited for the purpose. We are focusing on EEG signals whose main attributes are frequency and event related to some activity of the brain. So to analyze such signal we can use their time-frequency combined characteristic as features for artificial newlineintelligence. The advantage of artificial intelligence over other classification method is that it can adapt to any nonlinearity present in the signal. For BCI application we need to localize the source of different activity in the brain so that we can improve the prediction of movement. Since different individual s signal attribute are different which makes a
Pagination: 
URI: http://hdl.handle.net/10603/255880
Appears in Departments:Instrumentation & Control Engineering

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01_title.pdfAttached File132.41 kBAdobe PDFView/Open
02_certificate.pdf87.65 kBAdobe PDFView/Open
03_abstract.pdf89.94 kBAdobe PDFView/Open
04_declaration.pdf87.71 kBAdobe PDFView/Open
05_acknowledgement.pdf89.3 kBAdobe PDFView/Open
06_contents.pdf149.2 kBAdobe PDFView/Open
07_list_of_tables.pdf98.28 kBAdobe PDFView/Open
08_list_of_figures.pdf101.11 kBAdobe PDFView/Open
09_abbreviations.pdf88.69 kBAdobe PDFView/Open
10_chapter1.pdf143.94 kBAdobe PDFView/Open
11_chapter2.pdf146.64 kBAdobe PDFView/Open
12_chapter3.pdf803.79 kBAdobe PDFView/Open
13_chapter4.pdf2.66 MBAdobe PDFView/Open
14_chapter5.pdf4.22 MBAdobe PDFView/Open
15_chapter6.pdf132.18 kBAdobe PDFView/Open
16_bibliography.pdf128.76 kBAdobe PDFView/Open


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